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.../series/api/polars.Series.list.last.html | 2 +- .../api/polars.Series.list.lengths.html | 2 +- .../series/api/polars.Series.list.max.html | 2 +- .../series/api/polars.Series.list.mean.html | 2 +- .../series/api/polars.Series.list.min.html | 2 +- .../api/polars.Series.list.reverse.html | 2 +- .../polars.Series.list.set_difference.html | 2 +- .../polars.Series.list.set_intersection.html | 2 +- ....Series.list.set_symmetric_difference.html | 2 +- .../api/polars.Series.list.set_union.html | 2 +- .../series/api/polars.Series.list.shift.html | 2 +- .../series/api/polars.Series.list.slice.html | 2 +- .../series/api/polars.Series.list.sort.html | 2 +- .../series/api/polars.Series.list.sum.html | 2 +- .../series/api/polars.Series.list.tail.html | 2 +- .../series/api/polars.Series.list.take.html | 2 +- .../api/polars.Series.list.to_struct.html | 2 +- .../series/api/polars.Series.list.unique.html | 2 +- .../series/api/polars.Series.log.html | 2 +- .../series/api/polars.Series.log10.html | 2 +- .../series/api/polars.Series.log1p.html | 2 +- .../series/api/polars.Series.lower_bound.html | 2 +- .../series/api/polars.Series.map_dict.html | 2 +- .../series/api/polars.Series.max.html | 2 +- .../series/api/polars.Series.mean.html | 2 +- .../series/api/polars.Series.median.html | 2 +- .../series/api/polars.Series.min.html | 2 +- .../series/api/polars.Series.mode.html | 2 +- .../series/api/polars.Series.n_chunks.html | 2 +- .../series/api/polars.Series.n_unique.html | 2 +- .../series/api/polars.Series.nan_max.html | 2 +- .../series/api/polars.Series.nan_min.html | 2 +- .../api/polars.Series.new_from_index.html | 2 +- .../series/api/polars.Series.null_count.html | 2 +- .../series/api/polars.Series.pct_change.html | 2 +- .../series/api/polars.Series.peak_max.html | 2 +- .../series/api/polars.Series.peak_min.html | 2 +- .../series/api/polars.Series.product.html | 2 +- .../series/api/polars.Series.qcut.html | 2 +- .../series/api/polars.Series.quantile.html | 2 +- .../series/api/polars.Series.rank.html | 2 +- .../series/api/polars.Series.rechunk.html | 2 +- .../series/api/polars.Series.reinterpret.html | 2 +- .../series/api/polars.Series.rename.html | 2 +- .../series/api/polars.Series.reshape.html | 2 +- .../series/api/polars.Series.reverse.html | 2 +- .../series/api/polars.Series.rle.html | 2 +- .../series/api/polars.Series.rle_id.html | 2 +- .../api/polars.Series.rolling_apply.html | 2 +- .../series/api/polars.Series.rolling_max.html | 2 +- .../api/polars.Series.rolling_mean.html | 2 +- .../api/polars.Series.rolling_median.html | 2 +- .../series/api/polars.Series.rolling_min.html | 2 +- .../api/polars.Series.rolling_quantile.html | 2 +- .../api/polars.Series.rolling_skew.html | 2 +- .../series/api/polars.Series.rolling_std.html | 2 +- .../series/api/polars.Series.rolling_sum.html | 2 +- .../series/api/polars.Series.rolling_var.html | 2 +- .../series/api/polars.Series.round.html | 2 +- .../series/api/polars.Series.sample.html | 2 +- .../api/polars.Series.search_sorted.html | 2 +- .../api/polars.Series.series_equal.html | 2 +- .../series/api/polars.Series.set.html | 2 +- .../series/api/polars.Series.set_at_idx.html | 2 +- .../series/api/polars.Series.set_sorted.html | 2 +- .../series/api/polars.Series.shift.html | 2 +- .../api/polars.Series.shift_and_fill.html | 2 +- .../api/polars.Series.shrink_dtype.html | 2 +- .../api/polars.Series.shrink_to_fit.html | 2 +- .../series/api/polars.Series.shuffle.html | 2 +- .../series/api/polars.Series.sign.html | 2 +- .../series/api/polars.Series.sin.html | 2 +- .../series/api/polars.Series.sinh.html | 2 +- .../series/api/polars.Series.skew.html | 2 +- .../series/api/polars.Series.slice.html | 2 +- .../series/api/polars.Series.sort.html | 2 +- .../series/api/polars.Series.sqrt.html | 2 +- .../series/api/polars.Series.std.html | 2 +- .../series/api/polars.Series.str.concat.html | 2 +- .../api/polars.Series.str.contains.html | 2 +- .../api/polars.Series.str.count_match.html | 2 +- .../series/api/polars.Series.str.decode.html | 2 +- .../series/api/polars.Series.str.encode.html | 2 +- .../api/polars.Series.str.ends_with.html | 2 +- .../series/api/polars.Series.str.explode.html | 2 +- .../series/api/polars.Series.str.extract.html | 2 +- .../api/polars.Series.str.extract_all.html | 2 +- .../api/polars.Series.str.json_extract.html | 2 +- .../polars.Series.str.json_path_match.html | 2 +- .../series/api/polars.Series.str.lengths.html | 2 +- .../series/api/polars.Series.str.ljust.html | 2 +- .../series/api/polars.Series.str.lstrip.html | 2 +- .../series/api/polars.Series.str.n_chars.html | 2 +- .../api/polars.Series.str.parse_int.html | 2 +- .../series/api/polars.Series.str.replace.html | 2 +- .../api/polars.Series.str.replace_all.html | 2 +- .../series/api/polars.Series.str.rjust.html | 2 +- .../series/api/polars.Series.str.rstrip.html | 2 +- .../series/api/polars.Series.str.slice.html | 2 +- .../series/api/polars.Series.str.split.html | 2 +- .../api/polars.Series.str.split_exact.html | 2 +- .../series/api/polars.Series.str.splitn.html | 2 +- .../api/polars.Series.str.starts_with.html | 2 +- .../series/api/polars.Series.str.strip.html | 2 +- .../api/polars.Series.str.strptime.html | 2 +- .../series/api/polars.Series.str.to_date.html | 2 +- .../api/polars.Series.str.to_datetime.html | 2 +- .../api/polars.Series.str.to_decimal.html | 2 +- .../api/polars.Series.str.to_lowercase.html | 2 +- .../series/api/polars.Series.str.to_time.html | 2 +- .../api/polars.Series.str.to_titlecase.html | 2 +- .../api/polars.Series.str.to_uppercase.html | 2 +- .../series/api/polars.Series.str.zfill.html | 2 +- .../series/api/polars.Series.sum.html | 2 +- .../series/api/polars.Series.tail.html | 2 +- .../series/api/polars.Series.take.html | 2 +- .../series/api/polars.Series.take_every.html | 2 +- .../series/api/polars.Series.tan.html | 2 +- .../series/api/polars.Series.tanh.html | 2 +- .../series/api/polars.Series.to_arrow.html | 2 +- .../series/api/polars.Series.to_dummies.html | 2 +- .../series/api/polars.Series.to_frame.html | 2 +- .../api/polars.Series.to_init_repr.html | 2 +- .../series/api/polars.Series.to_list.html | 2 +- .../series/api/polars.Series.to_numpy.html | 2 +- .../series/api/polars.Series.to_pandas.html | 2 +- .../series/api/polars.Series.to_physical.html | 2 +- .../series/api/polars.Series.top_k.html | 2 +- .../series/api/polars.Series.unique.html | 2 +- .../api/polars.Series.unique_counts.html | 2 +- .../series/api/polars.Series.upper_bound.html | 2 +- .../api/polars.Series.value_counts.html | 2 +- .../series/api/polars.Series.var.html | 2 +- .../series/api/polars.Series.view.html | 2 +- .../series/api/polars.Series.zip_with.html | 2 +- py-polars/html/reference/series/index.html | 350 +++++++++--------- py-polars/html/reference/sql.html | 8 +- py-polars/html/searchindex.js | 2 +- 537 files changed, 890 insertions(+), 890 deletions(-) diff --git a/py-polars/html/reference/api/polars.DataFrame.write_avro.html b/py-polars/html/reference/api/polars.DataFrame.write_avro.html index 8c4dea2e35e2..f596b4fa9274 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_avro.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_avro.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_avro#

-DataFrame.write_avro(file: BinaryIO | BytesIO | str | Path, compression: AvroCompression = 'uncompressed') → None[source]#
+DataFrame.write_avro(file: BinaryIO | BytesIO | str | Path, compression: AvroCompression = 'uncompressed') → None[source]#

Write to Apache Avro file.

Parameters:
diff --git a/py-polars/html/reference/api/polars.DataFrame.write_csv.html b/py-polars/html/reference/api/polars.DataFrame.write_csv.html index 20cc7607d8c4..9aa0e477b5c5 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_csv.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_csv.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_csv#

-DataFrame.write_csv(file: None = None, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → str[source]#
+DataFrame.write_csv(file: None = None, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → str[source]#
DataFrame.write_csv(file: BytesIO | TextIOWrapper | str | Path, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → None

Write to comma-separated values (CSV) file.

diff --git a/py-polars/html/reference/api/polars.DataFrame.write_database.html b/py-polars/html/reference/api/polars.DataFrame.write_database.html index a6196ae66bc3..d2aef14b0a3f 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_database.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_database.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_database#

-DataFrame.write_database(table_name: str, connection: str, *, if_exists: DbWriteMode = 'fail', engine: DbWriteEngine = 'sqlalchemy') → None[source]#
+DataFrame.write_database(table_name: str, connection: str, *, if_exists: DbWriteMode = 'fail', engine: DbWriteEngine = 'sqlalchemy') → None[source]#

Write a polars frame to a database.

Parameters:
diff --git a/py-polars/html/reference/api/polars.DataFrame.write_delta.html b/py-polars/html/reference/api/polars.DataFrame.write_delta.html index 1cdfd7ae578e..055c4560a7e1 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_delta.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_delta.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_delta#

-DataFrame.write_delta(target: str | Path | deltalake.DeltaTable, *, mode: Literal['error', 'append', 'overwrite', 'ignore'] = 'error', overwrite_schema: bool = False, storage_options: dict[str, str] | None = None, delta_write_options: dict[str, Any] | None = None) → None[source]#
+DataFrame.write_delta(target: str | Path | deltalake.DeltaTable, *, mode: Literal['error', 'append', 'overwrite', 'ignore'] = 'error', overwrite_schema: bool = False, storage_options: dict[str, str] | None = None, delta_write_options: dict[str, Any] | None = None) → None[source]#

Write DataFrame as delta table.

Note: Some polars data types like Null, Categorical and Time are not supported by the delta protocol specification.

diff --git a/py-polars/html/reference/api/polars.DataFrame.write_excel.html b/py-polars/html/reference/api/polars.DataFrame.write_excel.html index 9642b6be9c02..7b337c96ffba 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_excel.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_excel.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_excel#

-DataFrame.write_excel(workbook: Workbook | BytesIO | Path | str | None = None, worksheet: str | None = None, *, position: tuple[int, int] | str = 'A1', table_style: str | dict[str, Any] | None = None, table_name: str | None = None, column_formats: dict[str | tuple[str, ...], str | dict[str, str]] | None = None, dtype_formats: dict[OneOrMoreDataTypes, str] | None = None, conditional_formats: ConditionalFormatDict | None = None, column_totals: ColumnTotalsDefinition | None = None, column_widths: dict[str | tuple[str, ...], int] | int | None = None, row_totals: RowTotalsDefinition | None = None, row_heights: dict[int | tuple[int, ...], int] | int | None = None, sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None, formulas: dict[str, str | dict[str, str]] | None = None, float_precision: int = 3, has_header: bool = True, autofilter: bool = True, autofit: bool = False, hidden_columns: Sequence[str] | None = None, hide_gridlines: bool = False, sheet_zoom: int | None = None, freeze_panes: str | tuple[int, int] | tuple[str, int, int] | tuple[int, int, int, int] | None = None) → Workbook[source]#
+DataFrame.write_excel(workbook: Workbook | BytesIO | Path | str | None = None, worksheet: str | None = None, *, position: tuple[int, int] | str = 'A1', table_style: str | dict[str, Any] | None = None, table_name: str | None = None, column_formats: dict[str | tuple[str, ...], str | dict[str, str]] | None = None, dtype_formats: dict[OneOrMoreDataTypes, str] | None = None, conditional_formats: ConditionalFormatDict | None = None, column_totals: ColumnTotalsDefinition | None = None, column_widths: dict[str | tuple[str, ...], int] | int | None = None, row_totals: RowTotalsDefinition | None = None, row_heights: dict[int | tuple[int, ...], int] | int | None = None, sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None, formulas: dict[str, str | dict[str, str]] | None = None, float_precision: int = 3, has_header: bool = True, autofilter: bool = True, autofit: bool = False, hidden_columns: Sequence[str] | None = None, hide_gridlines: bool = False, sheet_zoom: int | None = None, freeze_panes: str | tuple[int, int] | tuple[str, int, int] | tuple[int, int, int, int] | None = None) → Workbook[source]#

Write frame data to a table in an Excel workbook/worksheet.

Parameters:
diff --git a/py-polars/html/reference/api/polars.DataFrame.write_ipc.html b/py-polars/html/reference/api/polars.DataFrame.write_ipc.html index a5e58dcda817..f1865b8606fa 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_ipc.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_ipc.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_ipc#

-DataFrame.write_ipc(file: None, compression: IpcCompression = 'uncompressed') → BytesIO[source]#
+DataFrame.write_ipc(file: None, compression: IpcCompression = 'uncompressed') → BytesIO[source]#
DataFrame.write_ipc(file: BinaryIO | BytesIO | str | Path, compression: IpcCompression = 'uncompressed') → None

Write to Arrow IPC binary stream or Feather file.

diff --git a/py-polars/html/reference/api/polars.DataFrame.write_json.html b/py-polars/html/reference/api/polars.DataFrame.write_json.html index 27b1045ba726..9c1eb364397f 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_json.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_json.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_json#

-DataFrame.write_json(file: None = None, *, pretty: bool = False, row_oriented: bool = False) → str[source]#
+DataFrame.write_json(file: None = None, *, pretty: bool = False, row_oriented: bool = False) → str[source]#
DataFrame.write_json(file: IOBase | str | Path, *, pretty: bool = False, row_oriented: bool = False) → None

Serialize to JSON representation.

diff --git a/py-polars/html/reference/api/polars.DataFrame.write_ndjson.html b/py-polars/html/reference/api/polars.DataFrame.write_ndjson.html index 2e8822104580..1eddd24bd388 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_ndjson.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_ndjson.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_ndjson#

-DataFrame.write_ndjson(file: None = None) → str[source]#
+DataFrame.write_ndjson(file: None = None) → str[source]#
DataFrame.write_ndjson(file: IOBase | str | Path) → None

Serialize to newline delimited JSON representation.

diff --git a/py-polars/html/reference/api/polars.DataFrame.write_parquet.html b/py-polars/html/reference/api/polars.DataFrame.write_parquet.html index bbb5cc8b0e57..9c07a103b260 100644 --- a/py-polars/html/reference/api/polars.DataFrame.write_parquet.html +++ b/py-polars/html/reference/api/polars.DataFrame.write_parquet.html @@ -1623,7 +1623,7 @@

polars.DataFrame.write_parquet#

-DataFrame.write_parquet(file: str | Path | BytesIO, *, compression: ParquetCompression = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, use_pyarrow: bool = False, pyarrow_options: dict[str, object] | None = None) → None[source]#
+DataFrame.write_parquet(file: str | Path | BytesIO, *, compression: ParquetCompression = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, use_pyarrow: bool = False, pyarrow_options: dict[str, object] | None = None) → None[source]#

Write to Apache Parquet file.

Parameters:
diff --git a/py-polars/html/reference/api/polars.LazyFrame.sink_ipc.html b/py-polars/html/reference/api/polars.LazyFrame.sink_ipc.html index 2f44feb32bc4..6a006a546a8e 100644 --- a/py-polars/html/reference/api/polars.LazyFrame.sink_ipc.html +++ b/py-polars/html/reference/api/polars.LazyFrame.sink_ipc.html @@ -1623,7 +1623,7 @@

polars.LazyFrame.sink_ipc#

-LazyFrame.sink_ipc(path: str | Path, *, compression: str | None = 'zstd', maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]#
+LazyFrame.sink_ipc(path: str | Path, *, compression: str | None = 'zstd', maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]#

Persists a LazyFrame at the provided path.

This allows streaming results that are larger than RAM to be written to disk.

diff --git a/py-polars/html/reference/api/polars.LazyFrame.sink_parquet.html b/py-polars/html/reference/api/polars.LazyFrame.sink_parquet.html index deee11b9e8f5..149b42c266dd 100644 --- a/py-polars/html/reference/api/polars.LazyFrame.sink_parquet.html +++ b/py-polars/html/reference/api/polars.LazyFrame.sink_parquet.html @@ -1623,7 +1623,7 @@

polars.LazyFrame.sink_parquet#

-LazyFrame.sink_parquet(path: str | Path, *, compression: str = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, data_pagesize_limit: int | None = None, maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]#
+LazyFrame.sink_parquet(path: str | Path, *, compression: str = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, data_pagesize_limit: int | None = None, maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]#

Persists a LazyFrame at the provided path.

This allows streaming results that are larger than RAM to be written to disk.

diff --git a/py-polars/html/reference/api/polars.SQLContext.execute.html b/py-polars/html/reference/api/polars.SQLContext.execute.html index de35067559d2..dccf425b967f 100644 --- a/py-polars/html/reference/api/polars.SQLContext.execute.html +++ b/py-polars/html/reference/api/polars.SQLContext.execute.html @@ -1623,7 +1623,7 @@

polars.SQLContext.execute#

-SQLContext.execute(query: str, eager: None = None) → DataFrame[source]#
+SQLContext.execute(query: str, eager: None = None) → DataFrame[source]#
SQLContext.execute(query: str, eager: Literal[False]) → LazyFrame
diff --git a/py-polars/html/reference/api/polars.SQLContext.register.html b/py-polars/html/reference/api/polars.SQLContext.register.html index 96a5eae857d5..9413b7f037b7 100644 --- a/py-polars/html/reference/api/polars.SQLContext.register.html +++ b/py-polars/html/reference/api/polars.SQLContext.register.html @@ -1623,7 +1623,7 @@

polars.SQLContext.register#

-SQLContext.register(name: str, frame: DataFrame | LazyFrame) → Self[source]#
+SQLContext.register(name: str, frame: DataFrame | LazyFrame) → Self[source]#

Register a single frame as a table, using the given name.

Parameters:
diff --git a/py-polars/html/reference/api/polars.SQLContext.register_globals.html b/py-polars/html/reference/api/polars.SQLContext.register_globals.html index d5543dd24588..16b6c7ae59fe 100644 --- a/py-polars/html/reference/api/polars.SQLContext.register_globals.html +++ b/py-polars/html/reference/api/polars.SQLContext.register_globals.html @@ -1623,7 +1623,7 @@

polars.SQLContext.register_globals#

-SQLContext.register_globals(n: int | None = None) → Self[source]#
+SQLContext.register_globals(n: int | None = None) → Self[source]#

Register all frames (lazy or eager) found in the current globals scope.

Automatically maps variable names to table names.

diff --git a/py-polars/html/reference/api/polars.SQLContext.register_many.html b/py-polars/html/reference/api/polars.SQLContext.register_many.html index a6cb285e9ee2..0785bad6351f 100644 --- a/py-polars/html/reference/api/polars.SQLContext.register_many.html +++ b/py-polars/html/reference/api/polars.SQLContext.register_many.html @@ -1623,7 +1623,7 @@

polars.SQLContext.register_many#

-SQLContext.register_many(frames: Mapping[str, DataFrame | LazyFrame] | None = None, **named_frames: DataFrame | LazyFrame) → Self[source]#
+SQLContext.register_many(frames: Mapping[str, DataFrame | LazyFrame] | None = None, **named_frames: DataFrame | LazyFrame) → Self[source]#

Register multiple eager/lazy frames as tables, using the associated names.

Parameters:
diff --git a/py-polars/html/reference/api/polars.SQLContext.tables.html b/py-polars/html/reference/api/polars.SQLContext.tables.html index f60556aaa245..5102a8ccd396 100644 --- a/py-polars/html/reference/api/polars.SQLContext.tables.html +++ b/py-polars/html/reference/api/polars.SQLContext.tables.html @@ -1622,7 +1622,7 @@

polars.SQLContext.tables#

-SQLContext.tables() → list[str][source]#
+SQLContext.tables() → list[str][source]#

Return a list of the registered table names.

Notes

The tables() method will return the same values as the diff --git a/py-polars/html/reference/api/polars.SQLContext.unregister.html b/py-polars/html/reference/api/polars.SQLContext.unregister.html index bb761a2e920d..fa27314b9f42 100644 --- a/py-polars/html/reference/api/polars.SQLContext.unregister.html +++ b/py-polars/html/reference/api/polars.SQLContext.unregister.html @@ -1623,7 +1623,7 @@

polars.SQLContext.unregister#

-SQLContext.unregister(names: str | Collection[str]) → Self[source]#
+SQLContext.unregister(names: str | Collection[str]) → Self[source]#

Unregister one or more eager/lazy frames by name.

Parameters:
diff --git a/py-polars/html/reference/api/polars.collect_all.html b/py-polars/html/reference/api/polars.collect_all.html index 5b95e936abc5..5a0741afe5e0 100644 --- a/py-polars/html/reference/api/polars.collect_all.html +++ b/py-polars/html/reference/api/polars.collect_all.html @@ -1623,7 +1623,7 @@

polars.collect_all#

-polars.collect_all(lazy_frames: Sequence[LazyFrame], *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → list[DataFrame][source]#
+polars.collect_all(lazy_frames: Sequence[LazyFrame], *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → list[DataFrame][source]#

Collect multiple LazyFrames at the same time.

This runs all the computation graphs in parallel on the Polars threadpool.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.__dataframe__.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.__dataframe__.html index 8d3bec843d32..aa0865cfd830 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.__dataframe__.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.__dataframe__.html @@ -1628,7 +1628,7 @@

polars.DataFrame.__dataframe__#

-DataFrame.__dataframe__(nan_as_null: bool = False, allow_copy: bool = True) → _PyArrowDataFrame[source]#
+DataFrame.__dataframe__(nan_as_null: bool = False, allow_copy: bool = True) → _PyArrowDataFrame[source]#

Convert to a dataframe object implementing the dataframe interchange protocol.

Parameters:
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.apply.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.apply.html index c23213536c93..45576aa001f9 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.apply.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.apply.html @@ -1628,7 +1628,7 @@

polars.DataFrame.apply#

-DataFrame.apply(function: Callable[[tuple[Any, ...]], Any], return_dtype: PolarsDataType | None = None, *, inference_size: int = 256) → DataFrame[source]#
+DataFrame.apply(function: Callable[[tuple[Any, ...]], Any], return_dtype: PolarsDataType | None = None, *, inference_size: int = 256) → DataFrame[source]#

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

Warning

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.bottom_k.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.bottom_k.html index f63ee404f9dc..88dd71e83c06 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.bottom_k.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.bottom_k.html @@ -1628,7 +1628,7 @@

polars.DataFrame.bottom_k#

-DataFrame.bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]#
+DataFrame.bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]#

Return the k smallest elements.

If ‘descending=True` the largest elements will be given.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.clear.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.clear.html index a030bb4d5043..bf3e4a6b3161 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.clear.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.clear.html @@ -1628,7 +1628,7 @@

polars.DataFrame.clear#

-DataFrame.clear(n: int = 0) → Self[source]#
+DataFrame.clear(n: int = 0) → Self[source]#

Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.

Returns a n-row null-filled DataFrame with an identical schema. n can be greater than the current number of rows in the DataFrame.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.clone.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.clone.html index 87278e76b15c..b016c928e7e3 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.clone.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.clone.html @@ -1628,7 +1628,7 @@

polars.DataFrame.clone#

-DataFrame.clone() → Self[source]#
+DataFrame.clone() → Self[source]#

Cheap deepcopy/clone.

See also

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.columns.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.columns.html index ee47e10c8e1a..9f214bcfd001 100644 --- a/py-polars/html/reference/dataframe/api/polars.DataFrame.columns.html +++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.columns.html @@ -1628,7 +1628,7 @@

polars.DataFrame.columns#

-property DataFrame.columns: list[str][source]#
+property DataFrame.columns: list[str][source]#

Get or set column names.

Examples

>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.corr.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.corr.html
index 4d1a7ea4e918..bab1efacd6ac 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.corr.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.corr.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.corr#

 

 

-DataFrame.corr(**kwargs: Any) → DataFrame[source]#

+DataFrame.corr(**kwargs: Any) → DataFrame[source]#
 
Return pairwise Pearson product-moment correlation coefficients between columns.

 
See numpy corrcoef for more information:
 https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.describe.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.describe.html
index efa4aeb65fab..79cd97b4d38e 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.describe.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.describe.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.describe#

 

 

-DataFrame.describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → Self[source]#

+DataFrame.describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → Self[source]#
 
Summary statistics for a DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop.html
index e6349c99c7c1..996e1068ec23 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.drop#

 

 

-DataFrame.drop(columns: str | Collection[str], *more_columns: str) → DataFrame[source]#

+DataFrame.drop(columns: str | Collection[str], *more_columns: str) → DataFrame[source]#
 
Remove columns from the dataframe.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_in_place.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_in_place.html
index 4f347e2f1db3..00d6bbb7f4ad 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_in_place.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_in_place.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.drop_in_place#

 

 

-DataFrame.drop_in_place(name: str) → Series[source]#

+DataFrame.drop_in_place(name: str) → Series[source]#
 
Drop a single column in-place and return the dropped column.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_nulls.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_nulls.html
index 6effeb6c7871..25edb9902b60 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_nulls.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.drop_nulls.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.drop_nulls#

 

 

-DataFrame.drop_nulls(subset: str | Collection[str] | None = None) → DataFrame[source]#

+DataFrame.drop_nulls(subset: str | Collection[str] | None = None) → DataFrame[source]#
 
Drop all rows that contain null values.

 
Returns a new DataFrame.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.dtypes.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.dtypes.html
index c2f0e3dc6130..be104d027ffd 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.dtypes.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.dtypes.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.dtypes#

 

 

-property DataFrame.dtypes: list[PolarsDataType][source]#

+property DataFrame.dtypes: list[PolarsDataType][source]#
 
Get the datatypes of the columns of this DataFrame.

 
The datatypes can also be found in column headers when printing the DataFrame.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.estimated_size.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.estimated_size.html
index 041855e29dc1..5596db4f64d5 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.estimated_size.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.estimated_size.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.estimated_size#

 

 

-DataFrame.estimated_size(unit: SizeUnit = 'b') → int | float[source]#

+DataFrame.estimated_size(unit: SizeUnit = 'b') → int | float[source]#
 
Return an estimation of the total (heap) allocated size of the DataFrame.

 
Estimated size is given in the specified unit (bytes by default).

 
This estimation is the sum of the size of its buffers, validity, including
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.explode.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.explode.html
index 837cd3af2758..ca6b613e7ff4 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.explode.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.explode.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.explode#

 

 

-DataFrame.explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → DataFrame[source]#

+DataFrame.explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → DataFrame[source]#
 
Explode the dataframe to long format by exploding the given columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.extend.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.extend.html
index 9d2a657c3b10..818b96a60af9 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.extend.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.extend.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.extend#

 

 

-DataFrame.extend(other: DataFrame) → Self[source]#

+DataFrame.extend(other: DataFrame) → Self[source]#
 
Extend the memory backed by this DataFrame with the values from other.

 
Different from vstack which adds the chunks from other to the chunks of
 this DataFrame, extend appends the data from other to the underlying
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_nan.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_nan.html
index 2c40fa60109d..c803ef4c6f93 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_nan.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_nan.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.fill_nan#

 

 

-DataFrame.fill_nan(value: Expr | int | float | None) → DataFrame[source]#

+DataFrame.fill_nan(value: Expr | int | float | None) → DataFrame[source]#
 
Fill floating point NaN values by an Expression evaluation.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_null.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_null.html
index 4e50c051e64a..997997cee41b 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_null.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.fill_null.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.fill_null#

 

 

-DataFrame.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → DataFrame[source]#

+DataFrame.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → DataFrame[source]#
 
Fill null values using the specified value or strategy.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.filter.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.filter.html
index b7c6a835e13e..3f8d063fbd52 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.filter.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.filter.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.filter#

 

 

-DataFrame.filter(predicate: Expr | str | Series | list[bool] | np.ndarray[Any, Any] | bool) → DataFrame[source]#

+DataFrame.filter(predicate: Expr | str | Series | list[bool] | np.ndarray[Any, Any] | bool) → DataFrame[source]#
 
Filter the rows in the DataFrame based on a predicate expression.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
index 8551d21f6aa6..279edc632f01 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.find_idx_by_name#

 

 

-DataFrame.find_idx_by_name(name: str) → int[source]#

+DataFrame.find_idx_by_name(name: str) → int[source]#
 
Find the index of a column by name.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.flags.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.flags.html
index 5c7409a589f3..9dc9ad2b64fe 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.flags.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.flags.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.flags#

 

 

-property DataFrame.flags: dict[str, dict[str, bool]][source]#

+property DataFrame.flags: dict[str, dict[str, bool]][source]#
 
Get flags that are set on the columns of this DataFrame.

 

 
Returns:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.fold.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.fold.html
index 84012d8ba096..82944919ab6d 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.fold.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.fold.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.fold#

 

 

-DataFrame.fold(operation: Callable[[Series, Series], Series]) → Series[source]#

+DataFrame.fold(operation: Callable[[Series, Series], Series]) → Series[source]#
 
Apply a horizontal reduction on a DataFrame.

 
This can be used to effectively determine aggregations on a row level, and can
 be applied to any DataType that can be supercasted (casted to a similar parent
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.frame_equal.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.frame_equal.html
index 1d5a07e1a1f2..1f875e8d5c29 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.frame_equal.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.frame_equal.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.frame_equal#

 

 

-DataFrame.frame_equal(other: DataFrame, *, null_equal: bool = True) → bool[source]#

+DataFrame.frame_equal(other: DataFrame, *, null_equal: bool = True) → bool[source]#
 
Check if DataFrame is equal to other.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.get_column.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.get_column.html
index 1452b6a9ec86..b68ea580b8c3 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.get_column.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.get_column.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.get_column#

 

 

-DataFrame.get_column(name: str) → Series[source]#

+DataFrame.get_column(name: str) → Series[source]#
 
Get a single column as Series by name.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.get_columns.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.get_columns.html
index 0eb15ee34d57..88b0f3def344 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.get_columns.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.get_columns.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.get_columns#

 

 

-DataFrame.get_columns() → list[Series][source]#

+DataFrame.get_columns() → list[Series][source]#
 
Get the DataFrame as a List of Series.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.glimpse.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.glimpse.html
index 8d1719c9bd93..9756066c28ec 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.glimpse.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.glimpse.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.glimpse#

 

 

-DataFrame.glimpse(*, return_as_string: Literal[False]) → None[source]#

+DataFrame.glimpse(*, return_as_string: Literal[False]) → None[source]#
 

 DataFrame.glimpse(*, return_as_string: Literal[True]) → str

 
Return a dense preview of the dataframe.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html
index e01225b27938..840bf16fc769 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.groupby#

 

 

-DataFrame.groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → GroupBy[source]#

+DataFrame.groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → GroupBy[source]#
 
Start a groupby operation.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
index 7880d61fbd3f..3436b5afe833 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.groupby_dynamic#

 

 

-DataFrame.groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → DynamicGroupBy[source]#

+DataFrame.groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → DynamicGroupBy[source]#
 
Group based on a time value (or index value of type Int32, Int64).

 
Time windows are calculated and rows are assigned to windows. Different from a
 normal groupby is that a row can be member of multiple groups. The time/index
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_rolling.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
index ab32a4d5a6fa..2c264e75fd26 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.groupby_rolling#

 

 

-DataFrame.groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → RollingGroupBy[source]#

+DataFrame.groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → RollingGroupBy[source]#
 
Create rolling groups based on a time, Int32, or Int64 column.

 
Different from a dynamic_groupby the windows are now determined by the
 individual values and are not of constant intervals. For constant intervals use
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.hash_rows.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.hash_rows.html
index 400804262969..035907d95d31 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.hash_rows.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.hash_rows.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.hash_rows#

 

 

-DataFrame.hash_rows(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]#

+DataFrame.hash_rows(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]#
 
Hash and combine the rows in this DataFrame.

 
The hash value is of type UInt64.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.head.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.head.html
index e48e87bd594f..6a6f0254bb66 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.head.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.head.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.head#

 

 

-DataFrame.head(n: int = 5) → Self[source]#

+DataFrame.head(n: int = 5) → Self[source]#
 
Get the first n rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.height.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.height.html
index 8184d003b7c8..eb560091b100 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.height.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.height.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.height#

 

 

-property DataFrame.height: int[source]#

+property DataFrame.height: int[source]#
 
Get the height of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.hstack.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.hstack.html
index afe2e2b67ed8..222566b70bd6 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.hstack.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.hstack.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.hstack#

 

 

-DataFrame.hstack(columns: list[Series] | DataFrame, *, in_place: bool = False) → Self[source]#

+DataFrame.hstack(columns: list[Series] | DataFrame, *, in_place: bool = False) → Self[source]#
 
Return a new DataFrame grown horizontally by stacking multiple Series to it.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.insert_at_idx.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
index d9a8b8696785..8469fa4efbf4 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.insert_at_idx#

 

 

-DataFrame.insert_at_idx(index: int, series: Series) → Self[source]#

+DataFrame.insert_at_idx(index: int, series: Series) → Self[source]#
 
Insert a Series at a certain column index. This operation is in place.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.interpolate.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.interpolate.html
index f1feaed559aa..ddc083eea6f5 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.interpolate.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.interpolate.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.interpolate#

 

 

-DataFrame.interpolate() → DataFrame[source]#

+DataFrame.interpolate() → DataFrame[source]#
 
Interpolate intermediate values. The interpolation method is linear.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_duplicated.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_duplicated.html
index 752da2562656..a6d794afcc9f 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_duplicated.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_duplicated.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.is_duplicated#

 

 

-DataFrame.is_duplicated() → Series[source]#

+DataFrame.is_duplicated() → Series[source]#
 
Get a mask of all duplicated rows in this DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_empty.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_empty.html
index ea267af82053..885b434a224a 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_empty.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_empty.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.is_empty#

 

 

-DataFrame.is_empty() → bool[source]#

+DataFrame.is_empty() → bool[source]#
 
Check if the dataframe is empty.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_unique.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_unique.html
index c23139574165..804a2283af16 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.is_unique.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.is_unique.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.is_unique#

 

 

-DataFrame.is_unique() → Series[source]#

+DataFrame.is_unique() → Series[source]#
 
Get a mask of all unique rows in this DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.item.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.item.html
index 78d13366851e..c00ca94661ee 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.item.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.item.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.item#

 

 

-DataFrame.item(row: int | None = None, column: int | str | None = None) → Any[source]#

+DataFrame.item(row: int | None = None, column: int | str | None = None) → Any[source]#
 
Return the dataframe as a scalar, or return the element at the given row/column.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_rows.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_rows.html
index 993fe23ad364..2448e64068d4 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_rows.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_rows.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.iter_rows#

 

 

-DataFrame.iter_rows(*, named: Literal[False] = False, buffer_size: int = 500) → Iterator[tuple[Any, ...]][source]#

+DataFrame.iter_rows(*, named: Literal[False] = False, buffer_size: int = 500) → Iterator[tuple[Any, ...]][source]#
 

 DataFrame.iter_rows(*, named: Literal[True], buffer_size: int = 500) → Iterator[dict[str, Any]]

 
Returns an iterator over the DataFrame of rows of python-native values.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_slices.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_slices.html
index 8a7d81b92c1b..edff0b2b22d3 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_slices.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.iter_slices.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.iter_slices#

 

 

-DataFrame.iter_slices(n_rows: int = 10000) → Iterator[DataFrame][source]#

+DataFrame.iter_slices(n_rows: int = 10000) → Iterator[DataFrame][source]#
 
Returns a non-copying iterator of slices over the underlying DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.join.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.join.html
index 0e7c2f603fb8..66d3b2b1603d 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.join.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.join.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.join#

 

 

-DataFrame.join(other: DataFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m') → DataFrame[source]#

+DataFrame.join(other: DataFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m') → DataFrame[source]#
 
Join in SQL-like fashion.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.join_asof.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.join_asof.html
index e50153b1e74e..81fedb125f17 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.join_asof.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.join_asof.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.join_asof#

 

 

-DataFrame.join_asof(other: DataFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → DataFrame[source]#

+DataFrame.join_asof(other: DataFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → DataFrame[source]#
 
Perform an asof join.

 
This is similar to a left-join except that we match on nearest key rather than
 equal keys.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.lazy.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.lazy.html
index 9c2d9a64a5af..5306406a26b3 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.lazy.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.lazy.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.lazy#

 

 

-DataFrame.lazy() → LazyFrame[source]#

+DataFrame.lazy() → LazyFrame[source]#
 
Start a lazy query from this point. This returns a LazyFrame object.

 
Operations on a LazyFrame are not executed until this is requested by either
 calling:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.limit.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.limit.html
index 7a0ac508ee36..c1be342c24a8 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.limit.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.limit.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.limit#

 

 

-DataFrame.limit(n: int = 5) → Self[source]#

+DataFrame.limit(n: int = 5) → Self[source]#
 
Get the first n rows.

 
Alias for DataFrame.head().

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.max.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.max.html
index 65cba1e89975..ff0f5a77caa0 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.max.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.max.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.max#

 

 

-DataFrame.max(axis: Literal[0] = 0) → Self[source]#

+DataFrame.max(axis: Literal[0] = 0) → Self[source]#
 

 DataFrame.max(axis: Literal[1]) → Series

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.mean.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.mean.html
index f4408c0f97d0..96b75a357ade 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.mean.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.mean.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.mean#

 

 

-DataFrame.mean(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]#

+DataFrame.mean(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]#

 

 DataFrame.mean(*, axis: Literal[1], null_strategy: NullStrategy = 'ignore') → Series

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.median.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.median.html
index 0d81e3402476..a0c8a91ec8a6 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.median.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.median.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.median#

 

 

-DataFrame.median() → Self[source]#

+DataFrame.median() → Self[source]#

 
Aggregate the columns of this DataFrame to their median value.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.melt.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.melt.html
index 4fa4d375f0ae..34e51fd6bf21 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.melt.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.melt.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.melt#

 

 

-DataFrame.melt(id_vars: Sequence[str] | str | None = None, value_vars: Sequence[str] | str | None = None, variable_name: str | None = None, value_name: str | None = None) → Self[source]#

+DataFrame.melt(id_vars: Sequence[str] | str | None = None, value_vars: Sequence[str] | str | None = None, variable_name: str | None = None, value_name: str | None = None) → Self[source]#
 
Unpivot a DataFrame from wide to long format.

 
Optionally leaves identifiers set.

 
This function is useful to massage a DataFrame into a format where one or more
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.merge_sorted.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.merge_sorted.html
index 8ed739d8cb88..223d74ce6c5e 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.merge_sorted.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.merge_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.merge_sorted#

 

 

-DataFrame.merge_sorted(other: DataFrame, key: str) → DataFrame[source]#

+DataFrame.merge_sorted(other: DataFrame, key: str) → DataFrame[source]#
 
Take two sorted DataFrames and merge them by the sorted key.

 
The output of this operation will also be sorted.
 It is the callers responsibility that the frames are sorted
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.min.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.min.html
index d35fcfd2ec7a..012a12481181 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.min.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.min.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.min#

 

 

-DataFrame.min(axis: Literal[0] = 0) → Self[source]#

+DataFrame.min(axis: Literal[0] = 0) → Self[source]#
 

 DataFrame.min(axis: Literal[1]) → Series

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.n_chunks.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.n_chunks.html
index 4ccdb5d1e333..a25f67fe267e 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.n_chunks.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.n_chunks.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.n_chunks#

 

 

-DataFrame.n_chunks(strategy: Literal['first'] = 'first') → int[source]#

+DataFrame.n_chunks(strategy: Literal['first'] = 'first') → int[source]#

 

 DataFrame.n_chunks(strategy: Literal['all']) → list[int]

 
Get number of chunks used by the ChunkedArrays of this DataFrame.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.n_unique.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.n_unique.html
index 7f3b5ec1bafc..bebcf77ff417 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.n_unique.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.n_unique.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.n_unique#

 

 

-DataFrame.n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) → int[source]#

+DataFrame.n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) → int[source]#
 
Return the number of unique rows, or the number of unique row-subsets.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.null_count.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.null_count.html
index 3db44c6ff3cc..17745cfb4b66 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.null_count.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.null_count.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.null_count#

 

 

-DataFrame.null_count() → Self[source]#

+DataFrame.null_count() → Self[source]#
 
Create a new DataFrame that shows the null counts per column.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.partition_by.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.partition_by.html
index 60bf47238dae..97a6d4ffbbc4 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.partition_by.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.partition_by.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.partition_by#

 

 

-DataFrame.partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[False] = False) → list[Self][source]#

+DataFrame.partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[False] = False) → list[Self][source]#
 

 DataFrame.partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[True]) → dict[Any, Self]

 
Group by the given columns and return the groups as separate dataframes.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.pipe.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.pipe.html
index c05ed35685ef..5a4cc4966f13 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.pipe.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.pipe.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.pipe#

 

 

-DataFrame.pipe(function: Callable[Concatenate[DataFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]#

+DataFrame.pipe(function: Callable[Concatenate[DataFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]#
 
Offers a structured way to apply a sequence of user-defined functions (UDFs).

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.pivot.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.pivot.html
index f3c786ab26f9..4ce51c3d4627 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.pivot.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.pivot.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.pivot#

 

 

-DataFrame.pivot(values: Sequence[str] | str, index: Sequence[str] | str, columns: Sequence[str] | str, aggregate_function: PivotAgg | Expr | None | NoDefault = _NoDefault.no_default, *, maintain_order: bool = True, sort_columns: bool = False, separator: str = '_') → Self[source]#

+DataFrame.pivot(values: Sequence[str] | str, index: Sequence[str] | str, columns: Sequence[str] | str, aggregate_function: PivotAgg | Expr | None | NoDefault = _NoDefault.no_default, *, maintain_order: bool = True, sort_columns: bool = False, separator: str = '_') → Self[source]#
 
Create a spreadsheet-style pivot table as a DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.product.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.product.html
index cbf93b35c139..f4008c5cf191 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.product.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.product.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.product#

 

 

-DataFrame.product() → DataFrame[source]#

+DataFrame.product() → DataFrame[source]#
 
Aggregate the columns of this DataFrame to their product values.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.quantile.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.quantile.html
index 3f17f93110c8..08887a50a108 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.quantile.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.quantile.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.quantile#

 

 

-DataFrame.quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]#

+DataFrame.quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]#
 
Aggregate the columns of this DataFrame to their quantile value.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.rechunk.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.rechunk.html
index d7f406460c4a..f2a69ead42af 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.rechunk.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.rechunk.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.rechunk#

 

 

-DataFrame.rechunk() → Self[source]#

+DataFrame.rechunk() → Self[source]#
 
Rechunk the data in this DataFrame to a contiguous allocation.

 
This will make sure all subsequent operations have optimal and predictable
 performance.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.rename.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.rename.html
index 74c8ff1e8995..07b286b6d0ac 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.rename.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.rename.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.rename#

 

 

-DataFrame.rename(mapping: dict[str, str]) → DataFrame[source]#

+DataFrame.rename(mapping: dict[str, str]) → DataFrame[source]#
 
Rename column names.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.replace.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.replace.html
index 84e92784323c..d39a963e2682 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.replace.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.replace.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.replace#

 

 

-DataFrame.replace(column: str, new_column: Series) → Self[source]#

+DataFrame.replace(column: str, new_column: Series) → Self[source]#
 
Replace a column by a new Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.replace_at_idx.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
index d23da107ef9b..4e366a5b9835 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.replace_at_idx#

 

 

-DataFrame.replace_at_idx(index: int, series: Series) → Self[source]#

+DataFrame.replace_at_idx(index: int, series: Series) → Self[source]#
 
Replace a column at an index location.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.reverse.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.reverse.html
index d809b76c8d7b..7408ca17c79f 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.reverse.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.reverse.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.reverse#

 

 

-DataFrame.reverse() → DataFrame[source]#

+DataFrame.reverse() → DataFrame[source]#
 
Reverse the DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.row.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.row.html
index d7660e9510e6..31452fbc3361 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.row.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.row.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.row#

 

 

-DataFrame.row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[False] = False) → tuple[Any, ...][source]#

+DataFrame.row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[False] = False) → tuple[Any, ...][source]#
 

 DataFrame.row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[True]) → dict[str, Any]

 
Get the values of a single row, either by index or by predicate.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.rows.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.rows.html
index 416893180345..7a129cbdfcd2 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.rows.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.rows.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.rows#

 

 

-DataFrame.rows(*, named: Literal[False] = False) → list[tuple[Any, ...]][source]#

+DataFrame.rows(*, named: Literal[False] = False) → list[tuple[Any, ...]][source]#
 

 DataFrame.rows(*, named: Literal[True]) → list[dict[str, Any]]

 
Returns all data in the DataFrame as a list of rows of python-native values.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.rows_by_key.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.rows_by_key.html
index 9ac028d10f4e..dfd27d40bb7c 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.rows_by_key.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.rows_by_key.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.rows_by_key#

 

 

-DataFrame.rows_by_key(key: str | Sequence[str] | SelectorType, *, named: bool = False, include_key: bool = False, unique: bool = False) → dict[Any, Iterable[Any]][source]#

+DataFrame.rows_by_key(key: str | Sequence[str] | SelectorType, *, named: bool = False, include_key: bool = False, unique: bool = False) → dict[Any, Iterable[Any]][source]#
 
Returns DataFrame data as a keyed dictionary of python-native values.

 
Note that this method should not be used in place of native operations, due to
 the high cost of materialising all frame data out into a dictionary; it should
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.sample.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.sample.html
index bfa6ef1794f3..f6ade5ecb472 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.sample.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.sample.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.sample#

 

 

-DataFrame.sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Self[source]#

+DataFrame.sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Self[source]#
 
Sample from this DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.schema.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.schema.html
index ee40238f43d1..0444964d843c 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.schema.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.schema.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.schema#

 

 

-property DataFrame.schema: SchemaDict[source]#

+property DataFrame.schema: SchemaDict[source]#
 
Get a dict[column name, DataType].

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.select.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.select.html
index 8a3fde568de0..5e7aee935161 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.select.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.select.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.select#

 

 

-DataFrame.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#

+DataFrame.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#
 
Select columns from this DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.set_sorted.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.set_sorted.html
index 59df238f0a1b..86fca91b289b 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.set_sorted.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.set_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.set_sorted#

 

 

-DataFrame.set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → DataFrame[source]#

+DataFrame.set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → DataFrame[source]#
 
Indicate that one or multiple columns are sorted.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.shape.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.shape.html
index a04931d59139..7592d1cc2132 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.shape.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.shape.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.shape#

 

 

-property DataFrame.shape: tuple[int, int][source]#

+property DataFrame.shape: tuple[int, int][source]#
 
Get the shape of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.shift.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.shift.html
index fe00bb13ada9..544a4f8e44a8 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.shift.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.shift.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.shift#

 

 

-DataFrame.shift(periods: int) → Self[source]#

+DataFrame.shift(periods: int) → Self[source]#
 
Shift values by the given period.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.shift_and_fill.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
index 85e5a4bf226c..2dea3a4d376a 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.shift_and_fill#

 

 

-DataFrame.shift_and_fill(fill_value: int | str | float, *, periods: int = 1) → DataFrame[source]#

+DataFrame.shift_and_fill(fill_value: int | str | float, *, periods: int = 1) → DataFrame[source]#
 
Shift the values by a given period and fill the resulting null values.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
index ea99f6aa175c..38d82e945dd6 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.shrink_to_fit#

 

 

-DataFrame.shrink_to_fit(*, in_place: bool = False) → Self[source]#

+DataFrame.shrink_to_fit(*, in_place: bool = False) → Self[source]#
 
Shrink DataFrame memory usage.

 
Shrinks to fit the exact capacity needed to hold the data.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.slice.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.slice.html
index 9a427290a1c2..bc61edffc8c6 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.slice.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.slice.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.slice#

 

 

-DataFrame.slice(offset: int, length: int | None = None) → Self[source]#

+DataFrame.slice(offset: int, length: int | None = None) → Self[source]#
 
Get a slice of this DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.sort.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.sort.html
index 6cd17685543e..ee3f8dfe9914 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.sort.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.sort.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.sort#

 

 

-DataFrame.sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False) → DataFrame[source]#

+DataFrame.sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False) → DataFrame[source]#
 
Sort the dataframe by the given columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.std.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.std.html
index 9fbdabc5b184..911647e5acea 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.std.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.std.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.std#

 

 

-DataFrame.std(ddof: int = 1) → Self[source]#

+DataFrame.std(ddof: int = 1) → Self[source]#
 
Aggregate the columns of this DataFrame to their standard deviation value.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.sum.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.sum.html
index cd2d83f573c0..bd2f3360b773 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.sum.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.sum.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.sum#

 

 

-DataFrame.sum(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]#

+DataFrame.sum(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]#
 

 DataFrame.sum(*, axis: Literal[1], null_strategy: NullStrategy = 'ignore') → Series

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.tail.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.tail.html
index 5fe5a8587878..2ef1da30b5d5 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.tail.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.tail.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.tail#

 

 

-DataFrame.tail(n: int = 5) → Self[source]#

+DataFrame.tail(n: int = 5) → Self[source]#

 
Get the last n rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.take_every.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.take_every.html
index 796c51e7eb8d..9d6fc5f1f6f8 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.take_every.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.take_every.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.take_every#

 

 

-DataFrame.take_every(n: int) → DataFrame[source]#

+DataFrame.take_every(n: int) → DataFrame[source]#
 
Take every nth row in the DataFrame and return as a new DataFrame.

 
Examples

 
>>> s = pl.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_arrow.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_arrow.html
index d1987e4b3b39..b57723944415 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_arrow.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_arrow.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_arrow#

 

 

-DataFrame.to_arrow() → Table[source]#

+DataFrame.to_arrow() → Table[source]#
 
Collect the underlying arrow arrays in an Arrow Table.

 
This operation is mostly zero copy.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dict.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dict.html
index 442b43aa5f31..546dea627117 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dict.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dict.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_dict#

 

 

-DataFrame.to_dict(as_series: Literal[True] = True) → dict[str, Series][source]#

+DataFrame.to_dict(as_series: Literal[True] = True) → dict[str, Series][source]#
 

 DataFrame.to_dict(as_series: Literal[False]) → dict[str, list[Any]]

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dicts.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dicts.html
index 8ac7612c8c51..1b497ac46826 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dicts.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dicts.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_dicts#

 

 

-DataFrame.to_dicts() → list[dict[str, Any]][source]#

+DataFrame.to_dicts() → list[dict[str, Any]][source]#

 
Convert every row to a dictionary of Python-native values.

 
Notes

 
If you have ns-precision temporal values you should be aware that Python
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dummies.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dummies.html
index 66591c650ff3..10b86fd1e097 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dummies.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_dummies.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_dummies#

 

 

-DataFrame.to_dummies(columns: str | Sequence[str] | None = None, *, separator: str = '_', drop_first: bool = False) → Self[source]#

+DataFrame.to_dummies(columns: str | Sequence[str] | None = None, *, separator: str = '_', drop_first: bool = False) → Self[source]#
 
Convert categorical variables into dummy/indicator variables.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_init_repr.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_init_repr.html
index 3bd414e56785..607d0b99f3c8 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_init_repr.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_init_repr.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_init_repr#

 

 

-DataFrame.to_init_repr(n: int = 1000) → str[source]#

+DataFrame.to_init_repr(n: int = 1000) → str[source]#
 
Convert DataFrame to instantiatable string representation.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_numpy.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_numpy.html
index 92f0b1e96780..bffe5f772819 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_numpy.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_numpy.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_numpy#

 

 

-DataFrame.to_numpy(structured: bool = False, *, order: IndexOrder = 'fortran') → np.ndarray[Any, Any][source]#

+DataFrame.to_numpy(structured: bool = False, *, order: IndexOrder = 'fortran') → np.ndarray[Any, Any][source]#
 
Convert DataFrame to a 2D NumPy array.

 
This operation clones data.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_pandas.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_pandas.html
index 6b9bbe6b5bba..b836161c40f5 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_pandas.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_pandas.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_pandas#

 

 

-DataFrame.to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → DataFrame[source]#

+DataFrame.to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → DataFrame[source]#
 
Cast to a pandas DataFrame.

 
This requires that pandas and pyarrow are installed.
 This operation clones data, unless use_pyarrow_extension_array=True.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_series.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_series.html
index aa2ec328b617..4991c074554f 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_series.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_series.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_series#

 

 

-DataFrame.to_series(index: int = 0) → Series[source]#

+DataFrame.to_series(index: int = 0) → Series[source]#
 
Select column as Series at index location.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_struct.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_struct.html
index 0ab1b7433b91..afb61972cae3 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.to_struct.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.to_struct.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.to_struct#

 

 

-DataFrame.to_struct(name: str) → Series[source]#

+DataFrame.to_struct(name: str) → Series[source]#
 
Convert a DataFrame to a Series of type Struct.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.top_k.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.top_k.html
index 4c896898d317..f9266a6d3198 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.top_k.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.top_k.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.top_k#

 

 

-DataFrame.top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]#

+DataFrame.top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]#
 
Return the k largest elements.

 
If ‘descending=True` the smallest elements will be given.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.transpose.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.transpose.html
index 1e2feace7fd6..643f895aaa63 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.transpose.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.transpose.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.transpose#

 

 

-DataFrame.transpose(*, include_header: bool = False, header_name: str = 'column', column_names: str | Iterable[str] | None = None) → Self[source]#

+DataFrame.transpose(*, include_header: bool = False, header_name: str = 'column', column_names: str | Iterable[str] | None = None) → Self[source]#
 
Transpose a DataFrame over the diagonal.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html
index 9f480d7fe356..a62609556643 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.unique.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.unique#

 

 

-DataFrame.unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → DataFrame[source]#

+DataFrame.unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → DataFrame[source]#
 
Drop duplicate rows from this dataframe.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.unnest.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.unnest.html
index d4d9afd440eb..07e5b4bd2ed2 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.unnest.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.unnest.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.unnest#

 

 

-DataFrame.unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]#

+DataFrame.unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]#
 
Decompose struct columns into separate columns for each of their fields.

 
The new columns will be inserted into the dataframe at the location of the
 struct column.

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.unstack.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.unstack.html
index f47a80fafbf5..76976544854b 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.unstack.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.unstack.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.unstack#

 

 

-DataFrame.unstack(step: int, how: UnstackDirection = 'vertical', columns: str | Sequence[str] | None = None, fill_values: list[Any] | None = None) → DataFrame[source]#

+DataFrame.unstack(step: int, how: UnstackDirection = 'vertical', columns: str | Sequence[str] | None = None, fill_values: list[Any] | None = None) → DataFrame[source]#
 
Unstack a long table to a wide form without doing an aggregation.

 
This can be much faster than a pivot, because it can skip the grouping phase.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.update.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.update.html
index 75ef68e6d9b8..451494625a34 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.update.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.update.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.update#

 

 

-DataFrame.update(other: DataFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → DataFrame[source]#

+DataFrame.update(other: DataFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → DataFrame[source]#
 
Update the values in this DataFrame with the non-null values in other.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.upsample.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.upsample.html
index 5d35f00219c7..7393114337ff 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.upsample.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.upsample.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.upsample#

 

 

-DataFrame.upsample(time_column: str, *, every: str | timedelta, offset: str | timedelta | None = None, by: str | Sequence[str] | None = None, maintain_order: bool = False) → Self[source]#

+DataFrame.upsample(time_column: str, *, every: str | timedelta, offset: str | timedelta | None = None, by: str | Sequence[str] | None = None, maintain_order: bool = False) → Self[source]#
 
Upsample a DataFrame at a regular frequency.

 
The every and offset arguments are created with
 the following string language:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.var.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.var.html
index c134396c07d6..39d8a062e176 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.var.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.var.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.var#

 

 

-DataFrame.var(ddof: int = 1) → Self[source]#

+DataFrame.var(ddof: int = 1) → Self[source]#
 
Aggregate the columns of this DataFrame to their variance value.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.vstack.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.vstack.html
index 937e53f364db..b582d59bd5e9 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.vstack.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.vstack.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.vstack#

 

 

-DataFrame.vstack(other: DataFrame, *, in_place: bool = False) → Self[source]#

+DataFrame.vstack(other: DataFrame, *, in_place: bool = False) → Self[source]#
 
Grow this DataFrame vertically by stacking a DataFrame to it.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.width.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.width.html
index 667b320c51f2..d4063cfd7a96 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.width.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.width.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.width#

 

 

-property DataFrame.width: int[source]#

+property DataFrame.width: int[source]#
 
Get the width of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.with_columns.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.with_columns.html
index 56b22462be71..bc686847154f 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.with_columns.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.with_columns.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.with_columns#

 

 

-DataFrame.with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#

+DataFrame.with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#
 
Add columns to this DataFrame.

 
Added columns will replace existing columns with the same name.

 

diff --git a/py-polars/html/reference/dataframe/api/polars.DataFrame.with_row_count.html b/py-polars/html/reference/dataframe/api/polars.DataFrame.with_row_count.html
index 114de89021b0..4988a339e740 100644
--- a/py-polars/html/reference/dataframe/api/polars.DataFrame.with_row_count.html
+++ b/py-polars/html/reference/dataframe/api/polars.DataFrame.with_row_count.html
@@ -1628,7 +1628,7 @@
 
polars.DataFrame.with_row_count#

 

 

-DataFrame.with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]#

+DataFrame.with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]#
 
Add a column at index 0 that counts the rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/dataframe/index.html b/py-polars/html/reference/dataframe/index.html
index 6e879f585289..2ee6b4b7ec34 100644
--- a/py-polars/html/reference/dataframe/index.html
+++ b/py-polars/html/reference/dataframe/index.html
@@ -1619,7 +1619,7 @@ 
DataFrame
 

-class polars.DataFrame(data: FrameInitTypes | None = None, schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, orient: Orientation | None = None, infer_schema_length: int | None = 100, nan_to_null: bool = False)[source]

+class polars.DataFrame(data: FrameInitTypes | None = None, schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, orient: Orientation | None = None, infer_schema_length: int | None = 100, nan_to_null: bool = False)[source]
 
Two-dimensional data structure representing data as a table with rows and columns.

 

 
Parameters:

@@ -2108,7 +2108,7 @@ 
DataFrame
 

-apply(function: Callable[[tuple[Any, ...]], Any], return_dtype: PolarsDataType | None = None, *, inference_size: int = 256) → DataFrame[source]

+apply(function: Callable[[tuple[Any, ...]], Any], return_dtype: PolarsDataType | None = None, *, inference_size: int = 256) → DataFrame[source]
 
Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

 

 
Warning

@@ -2197,7 +2197,7 @@ 
DataFrame
 

-bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]

+bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]
 
Return the k smallest elements.

 
If ‘descending=True` the largest elements will be given.

 

@@ -2269,7 +2269,7 @@ 
DataFrame
 

-clear(n: int = 0) → Self[source]

+clear(n: int = 0) → Self[source]
 
Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.

 
Returns a n-row null-filled DataFrame with an identical schema.
 n can be greater than the current number of rows in the DataFrame.

@@ -2322,7 +2322,7 @@ 
DataFrame
 

-clone() → Self[source]

+clone() → Self[source]
 
Cheap deepcopy/clone.

 

 
See also

@@ -2357,7 +2357,7 @@ 
DataFrame
 

-property columns: list[str][source]

+property columns: list[str][source]
 
Get or set column names.

 
Examples

 
>>> df = pl.DataFrame(
@@ -2390,7 +2390,7 @@ 
DataFrame
 

-corr(**kwargs: Any) → DataFrame[source]

+corr(**kwargs: Any) → DataFrame[source]
 
Return pairwise Pearson product-moment correlation coefficients between columns.

 
See numpy corrcoef for more information:
 https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html

@@ -2423,7 +2423,7 @@ 
DataFrame
 

-describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → Self[source]

+describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → Self[source]
 
Summary statistics for a DataFrame.

 

 
Parameters:

@@ -2475,7 +2475,7 @@ 
DataFrame
 

-drop(columns: str | Collection[str], *more_columns: str) → DataFrame[source]

+drop(columns: str | Collection[str], *more_columns: str) → DataFrame[source]
 
Remove columns from the dataframe.

 

 
Parameters:

@@ -2541,7 +2541,7 @@ 
DataFrame
 

-drop_in_place(name: str) → Series[source]

+drop_in_place(name: str) → Series[source]
 
Drop a single column in-place and return the dropped column.

 

 
Parameters:

@@ -2579,7 +2579,7 @@ 
DataFrame
 

-drop_nulls(subset: str | Collection[str] | None = None) → DataFrame[source]

+drop_nulls(subset: str | Collection[str] | None = None) → DataFrame[source]
 
Drop all rows that contain null values.

 
Returns a new DataFrame.

 

@@ -2668,7 +2668,7 @@ 
DataFrame
 

-property dtypes: list[PolarsDataType][source]

+property dtypes: list[PolarsDataType][source]
 
Get the datatypes of the columns of this DataFrame.

 
The datatypes can also be found in column headers when printing the DataFrame.

 

@@ -2705,7 +2705,7 @@ 
DataFrame
 

-estimated_size(unit: SizeUnit = 'b') → int | float[source]

+estimated_size(unit: SizeUnit = 'b') → int | float[source]
 
Return an estimation of the total (heap) allocated size of the DataFrame.

 
Estimated size is given in the specified unit (bytes by default).

 
This estimation is the sum of the size of its buffers, validity, including
@@ -2743,7 +2743,7 @@ 
DataFrame
 

-explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → DataFrame[source]

+explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → DataFrame[source]
 
Explode the dataframe to long format by exploding the given columns.

 

 
Parameters:

@@ -2802,7 +2802,7 @@ 
DataFrame
 

-extend(other: DataFrame) → Self[source]

+extend(other: DataFrame) → Self[source]
 
Extend the memory backed by this DataFrame with the values from other.

 
Different from vstack which adds the chunks from other to the chunks of
 this DataFrame, extend appends the data from other to the underlying
@@ -2858,7 +2858,7 @@ 
DataFrame
 

-fill_nan(value: Expr | int | float | None) → DataFrame[source]

+fill_nan(value: Expr | int | float | None) → DataFrame[source]
 
Fill floating point NaN values by an Expression evaluation.

 

 
Parameters:

@@ -2910,7 +2910,7 @@ 
DataFrame
 

-fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → DataFrame[source]

+fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → DataFrame[source]
 
Fill null values using the specified value or strategy.

 

 
Parameters:

@@ -3004,7 +3004,7 @@ 
DataFrame
 

-filter(predicate: Expr | str | Series | list[bool] | np.ndarray[Any, Any] | bool) → DataFrame[source]

+filter(predicate: Expr | str | Series | list[bool] | np.ndarray[Any, Any] | bool) → DataFrame[source]
 
Filter the rows in the DataFrame based on a predicate expression.

 

 
Parameters:

@@ -3066,7 +3066,7 @@ 
DataFrame
 

-find_idx_by_name(name: str) → int[source]

+find_idx_by_name(name: str) → int[source]
 
Find the index of a column by name.

 

 
Parameters:

@@ -3088,7 +3088,7 @@ 
DataFrame
 

-property flags: dict[str, dict[str, bool]][source]

+property flags: dict[str, dict[str, bool]][source]
 
Get flags that are set on the columns of this DataFrame.

 

 
Returns:

@@ -3102,7 +3102,7 @@ 
DataFrame
 

-fold(operation: Callable[[Series, Series], Series]) → Series[source]

+fold(operation: Callable[[Series, Series], Series]) → Series[source]
 
Apply a horizontal reduction on a DataFrame.

 
This can be used to effectively determine aggregations on a row level, and can
 be applied to any DataType that can be supercasted (casted to a similar parent
@@ -3190,7 +3190,7 @@ 
DataFrame
 

-frame_equal(other: DataFrame, *, null_equal: bool = True) → bool[source]

+frame_equal(other: DataFrame, *, null_equal: bool = True) → bool[source]
 
Check if DataFrame is equal to other.

 

 
Parameters:

@@ -3227,7 +3227,7 @@ 
DataFrame
 

-get_column(name: str) → Series[source]

+get_column(name: str) → Series[source]
 
Get a single column as Series by name.

 

 
Parameters:

@@ -3259,7 +3259,7 @@ 
DataFrame
 

-get_columns() → list[Series][source]

+get_columns() → list[Series][source]
 
Get the DataFrame as a List of Series.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
@@ -3315,7 +3315,7 @@ 
DataFrame
 

-glimpse(*, return_as_string: Literal[False]) → None[source]

+glimpse(*, return_as_string: Literal[False]) → None[source]
 

 glimpse(*, return_as_string: Literal[True]) → str

 
Return a dense preview of the dataframe.

@@ -3362,7 +3362,7 @@ 
DataFrame
 

-groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → GroupBy[source]

+groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → GroupBy[source]
 
Start a groupby operation.

 

 
Parameters:

@@ -3500,7 +3500,7 @@ 
DataFrame
 

-groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → DynamicGroupBy[source]

+groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → DynamicGroupBy[source]
 
Group based on a time value (or index value of type Int32, Int64).

 
Time windows are calculated and rows are assigned to windows. Different from a
 normal groupby is that a row can be member of multiple groups. The time/index
@@ -3809,7 +3809,7 @@ 
DataFrame
 

-groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → RollingGroupBy[source]

+groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → RollingGroupBy[source]
 
Create rolling groups based on a time, Int32, or Int64 column.

 
Different from a dynamic_groupby the windows are now determined by the
 individual values and are not of constant intervals. For constant intervals use
@@ -3937,7 +3937,7 @@ 
DataFrame
 

-hash_rows(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]

+hash_rows(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]
 
Hash and combine the rows in this DataFrame.

 
The hash value is of type UInt64.

 

@@ -3976,7 +3976,7 @@ 
DataFrame
 

-head(n: int = 5) → Self[source]

+head(n: int = 5) → Self[source]
 
Get the first n rows.

 

 
Parameters:

@@ -4031,7 +4031,7 @@ 
DataFrame
 

-property height: int[source]

+property height: int[source]
 
Get the height of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
@@ -4043,7 +4043,7 @@ 
DataFrame
 

-hstack(columns: list[Series] | DataFrame, *, in_place: bool = False) → Self[source]

+hstack(columns: list[Series] | DataFrame, *, in_place: bool = False) → Self[source]
 
Return a new DataFrame grown horizontally by stacking multiple Series to it.

 

 
Parameters:

@@ -4081,7 +4081,7 @@ 
DataFrame
 

-insert_at_idx(index: int, series: Series) → Self[source]

+insert_at_idx(index: int, series: Series) → Self[source]
 
Insert a Series at a certain column index. This operation is in place.

 

 
Parameters:

@@ -4135,7 +4135,7 @@ 
DataFrame
 

-interpolate() → DataFrame[source]

+interpolate() → DataFrame[source]
 
Interpolate intermediate values. The interpolation method is linear.

 
Examples

 
>>> df = pl.DataFrame(
@@ -4163,7 +4163,7 @@ 
DataFrame
 

-is_duplicated() → Series[source]

+is_duplicated() → Series[source]
 
Get a mask of all duplicated rows in this DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
@@ -4200,7 +4200,7 @@ 
DataFrame
 

-is_empty() → bool[source]

+is_empty() → bool[source]
 
Check if the dataframe is empty.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
@@ -4214,7 +4214,7 @@ 
DataFrame
 

-is_unique() → Series[source]

+is_unique() → Series[source]
 
Get a mask of all unique rows in this DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
@@ -4251,7 +4251,7 @@ 
DataFrame
 

-item(row: int | None = None, column: int | str | None = None) → Any[source]

+item(row: int | None = None, column: int | str | None = None) → Any[source]
 
Return the dataframe as a scalar, or return the element at the given row/column.

 

 
Parameters:

@@ -4287,7 +4287,7 @@ 
DataFrame
 

-iter_rows(*, named: Literal[False] = False, buffer_size: int = 500) → Iterator[tuple[Any, ...]][source]

+iter_rows(*, named: Literal[False] = False, buffer_size: int = 500) → Iterator[tuple[Any, ...]][source]
 

 iter_rows(*, named: Literal[True], buffer_size: int = 500) → Iterator[dict[str, Any]]

 
Returns an iterator over the DataFrame of rows of python-native values.

@@ -4349,7 +4349,7 @@ 
DataFrame
 

-iter_slices(n_rows: int = 10000) → Iterator[DataFrame][source]

+iter_slices(n_rows: int = 10000) → Iterator[DataFrame][source]
 
Returns a non-copying iterator of slices over the underlying DataFrame.

 

 
Parameters:

@@ -4407,7 +4407,7 @@ 
DataFrame
 

-join(other: DataFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m') → DataFrame[source]

+join(other: DataFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m') → DataFrame[source]
 
Join in SQL-like fashion.

 

 
Parameters:

@@ -4553,7 +4553,7 @@ 
DataFrame
 

-join_asof(other: DataFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → DataFrame[source]

+join_asof(other: DataFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → DataFrame[source]
 
Perform an asof join.

 
This is similar to a left-join except that we match on nearest key rather than
 equal keys.

@@ -4673,7 +4673,7 @@ 
DataFrame
 

-lazy() → LazyFrame[source]

+lazy() → LazyFrame[source]
 
Start a lazy query from this point. This returns a LazyFrame object.

 
Operations on a LazyFrame are not executed until this is requested by either
 calling:

@@ -4729,7 +4729,7 @@ 
DataFrame
 

-limit(n: int = 5) → Self[source]

+limit(n: int = 5) → Self[source]
 
Get the first n rows.

 
Alias for DataFrame.head().

 

@@ -4751,7 +4751,7 @@ 
DataFrame
 

-max(axis: Literal[0] = 0) → Self[source]

+max(axis: Literal[0] = 0) → Self[source]
 

 max(axis: Literal[1]) → Series

 

@@ -4780,7 +4780,7 @@ 
DataFrame
 

-mean(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]

+mean(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]

 

 mean(*, axis: Literal[1], null_strategy: NullStrategy = 'ignore') → Series

 

@@ -4828,7 +4828,7 @@ 
DataFrame
 

-median() → Self[source]

+median() → Self[source]

 
Aggregate the columns of this DataFrame to their median value.

 
Examples

 
>>> df = pl.DataFrame(
@@ -4853,7 +4853,7 @@ 
DataFrame
 

-melt(id_vars: Sequence[str] | str | None = None, value_vars: Sequence[str] | str | None = None, variable_name: str | None = None, value_name: str | None = None) → Self[source]

+melt(id_vars: Sequence[str] | str | None = None, value_vars: Sequence[str] | str | None = None, variable_name: str | None = None, value_name: str | None = None) → Self[source]
 
Unpivot a DataFrame from wide to long format.

 
Optionally leaves identifiers set.

 
This function is useful to massage a DataFrame into a format where one or more
@@ -4903,7 +4903,7 @@ 
DataFrame
 

-merge_sorted(other: DataFrame, key: str) → DataFrame[source]

+merge_sorted(other: DataFrame, key: str) → DataFrame[source]
 
Take two sorted DataFrames and merge them by the sorted key.

 
The output of this operation will also be sorted.
 It is the callers responsibility that the frames are sorted
@@ -4970,7 +4970,7 @@ 
DataFrame
 

-min(axis: Literal[0] = 0) → Self[source]

+min(axis: Literal[0] = 0) → Self[source]
 

 min(axis: Literal[1]) → Series

 

@@ -4999,7 +4999,7 @@ 
DataFrame
 

-n_chunks(strategy: Literal['first'] = 'first') → int[source]

+n_chunks(strategy: Literal['first'] = 'first') → int[source]

 

 n_chunks(strategy: Literal['all']) → list[int]

 
Get number of chunks used by the ChunkedArrays of this DataFrame.

@@ -5030,7 +5030,7 @@ 
DataFrame
 

-n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) → int[source]

+n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) → int[source]
 
Return the number of unique rows, or the number of unique row-subsets.

 

 
Parameters:

@@ -5089,7 +5089,7 @@ 
DataFrame
 

-null_count() → Self[source]

+null_count() → Self[source]
 
Create a new DataFrame that shows the null counts per column.

 
Examples

 
>>> df = pl.DataFrame(
@@ -5114,7 +5114,7 @@ 
DataFrame
 

-partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[False] = False) → list[Self][source]

+partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[False] = False) → list[Self][source]
 

 partition_by(by: str | Iterable[str], *more_by: str, maintain_order: bool = True, include_key: bool = True, as_dict: Literal[True]) → dict[Any, Self]

 
Group by the given columns and return the groups as separate dataframes.

@@ -5246,7 +5246,7 @@ 
DataFrame
 

-pipe(function: Callable[Concatenate[DataFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]

+pipe(function: Callable[Concatenate[DataFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]
 
Offers a structured way to apply a sequence of user-defined functions (UDFs).

 

 
Parameters:

@@ -5311,7 +5311,7 @@ 
DataFrame
 

-pivot(values: Sequence[str] | str, index: Sequence[str] | str, columns: Sequence[str] | str, aggregate_function: PivotAgg | Expr | None | NoDefault = _NoDefault.no_default, *, maintain_order: bool = True, sort_columns: bool = False, separator: str = '_') → Self[source]

+pivot(values: Sequence[str] | str, index: Sequence[str] | str, columns: Sequence[str] | str, aggregate_function: PivotAgg | Expr | None | NoDefault = _NoDefault.no_default, *, maintain_order: bool = True, sort_columns: bool = False, separator: str = '_') → Self[source]
 
Create a spreadsheet-style pivot table as a DataFrame.

 

 
Parameters:

@@ -5397,7 +5397,7 @@ 
DataFrame
 

-product() → DataFrame[source]

+product() → DataFrame[source]
 
Aggregate the columns of this DataFrame to their product values.

 
Examples

 
>>> df = pl.DataFrame(
@@ -5424,7 +5424,7 @@ 
DataFrame
 

-quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]

+quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]
 
Aggregate the columns of this DataFrame to their quantile value.

 

 
Parameters:

@@ -5459,7 +5459,7 @@ 
DataFrame
 

-rechunk() → Self[source]

+rechunk() → Self[source]
 
Rechunk the data in this DataFrame to a contiguous allocation.

 
This will make sure all subsequent operations have optimal and predictable
 performance.

@@ -5467,7 +5467,7 @@ 
DataFrame
 

-rename(mapping: dict[str, str]) → DataFrame[source]

+rename(mapping: dict[str, str]) → DataFrame[source]
 
Rename column names.

 

 
Parameters:

@@ -5498,7 +5498,7 @@ 
DataFrame
 

-replace(column: str, new_column: Series) → Self[source]

+replace(column: str, new_column: Series) → Self[source]
 
Replace a column by a new Series.

 

 
Parameters:

@@ -5530,7 +5530,7 @@ 
DataFrame
 

-replace_at_idx(index: int, series: Series) → Self[source]

+replace_at_idx(index: int, series: Series) → Self[source]
 
Replace a column at an index location.

 

 
Parameters:

@@ -5568,7 +5568,7 @@ 
DataFrame
 

-reverse() → DataFrame[source]

+reverse() → DataFrame[source]
 
Reverse the DataFrame.

 
Examples

 
>>> df = pl.DataFrame(
@@ -5594,7 +5594,7 @@ 
DataFrame
 

-row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[False] = False) → tuple[Any, ...][source]

+row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[False] = False) → tuple[Any, ...][source]
 

 row(index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[True]) → dict[str, Any]

 
Get the values of a single row, either by index or by predicate.

@@ -5667,7 +5667,7 @@ 
DataFrame
 

-rows(*, named: Literal[False] = False) → list[tuple[Any, ...]][source]

+rows(*, named: Literal[False] = False) → list[tuple[Any, ...]][source]
 

 rows(*, named: Literal[True]) → list[dict[str, Any]]

 
Returns all data in the DataFrame as a list of rows of python-native values.

@@ -5728,7 +5728,7 @@ 
DataFrame
 

-rows_by_key(key: str | Sequence[str] | SelectorType, *, named: bool = False, include_key: bool = False, unique: bool = False) → dict[Any, Iterable[Any]][source]

+rows_by_key(key: str | Sequence[str] | SelectorType, *, named: bool = False, include_key: bool = False, unique: bool = False) → dict[Any, Iterable[Any]][source]
 
Returns DataFrame data as a keyed dictionary of python-native values.

 
Note that this method should not be used in place of native operations, due to
 the high cost of materialising all frame data out into a dictionary; it should
@@ -5824,7 +5824,7 @@ 
DataFrame
 

-sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Self[source]

+sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Self[source]
 
Sample from this DataFrame.

 

 
Parameters:

@@ -5870,7 +5870,7 @@ 
DataFrame
 

-property schema: SchemaDict[source]

+property schema: SchemaDict[source]
 
Get a dict[column name, DataType].

 
Examples

 
>>> df = pl.DataFrame(
@@ -5888,7 +5888,7 @@ 
DataFrame
 

-select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]

+select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]
 
Select columns from this DataFrame.

 

 
Parameters:

@@ -5991,7 +5991,7 @@ 
DataFrame
 

-set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → DataFrame[source]

+set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → DataFrame[source]
 
Indicate that one or multiple columns are sorted.

 

 
Parameters:

@@ -6009,7 +6009,7 @@ 
DataFrame
 

-property shape: tuple[int, int][source]

+property shape: tuple[int, int][source]
 
Get the shape of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
@@ -6021,7 +6021,7 @@ 
DataFrame
 

-shift(periods: int) → Self[source]

+shift(periods: int) → Self[source]
 
Shift values by the given period.

 

 
Parameters:

@@ -6073,7 +6073,7 @@ 
DataFrame
 

-shift_and_fill(fill_value: int | str | float, *, periods: int = 1) → DataFrame[source]

+shift_and_fill(fill_value: int | str | float, *, periods: int = 1) → DataFrame[source]
 
Shift the values by a given period and fill the resulting null values.

 

 
Parameters:

@@ -6110,14 +6110,14 @@ 
DataFrame
 

-shrink_to_fit(*, in_place: bool = False) → Self[source]

+shrink_to_fit(*, in_place: bool = False) → Self[source]
 
Shrink DataFrame memory usage.

 
Shrinks to fit the exact capacity needed to hold the data.

 

 
 

 

-slice(offset: int, length: int | None = None) → Self[source]

+slice(offset: int, length: int | None = None) → Self[source]
 
Get a slice of this DataFrame.

 

 
Parameters:

@@ -6154,7 +6154,7 @@ 
DataFrame
 

-sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False) → DataFrame[source]

+sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False) → DataFrame[source]
 
Sort the dataframe by the given columns.

 

 
Parameters:

@@ -6240,7 +6240,7 @@ 
DataFrame
 

-std(ddof: int = 1) → Self[source]

+std(ddof: int = 1) → Self[source]
 
Aggregate the columns of this DataFrame to their standard deviation value.

 

 
Parameters:

@@ -6284,7 +6284,7 @@ 
DataFrame
 

-sum(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]

+sum(*, axis: Literal[0] = 0, null_strategy: NullStrategy = 'ignore') → Self[source]
 

 sum(*, axis: Literal[1], null_strategy: NullStrategy = 'ignore') → Series

 

@@ -6331,7 +6331,7 @@ 
DataFrame
 

-tail(n: int = 5) → Self[source]

+tail(n: int = 5) → Self[source]

 
Get the last n rows.

 

 
Parameters:

@@ -6386,7 +6386,7 @@ 
DataFrame
 

-take_every(n: int) → DataFrame[source]

+take_every(n: int) → DataFrame[source]
 
Take every nth row in the DataFrame and return as a new DataFrame.

 
Examples

 
>>> s = pl.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
@@ -6406,7 +6406,7 @@ 
DataFrame
 

-to_arrow() → Table[source]

+to_arrow() → Table[source]
 
Collect the underlying arrow arrays in an Arrow Table.

 
This operation is mostly zero copy.

 

@@ -6432,7 +6432,7 @@ 
DataFrame
 

-to_dict(as_series: Literal[True] = True) → dict[str, Series][source]

+to_dict(as_series: Literal[True] = True) → dict[str, Series][source]
 

 to_dict(as_series: Literal[False]) → dict[str, list[Any]]

 

@@ -6524,7 +6524,7 @@ 
DataFrame
 

-to_dicts() → list[dict[str, Any]][source]

+to_dicts() → list[dict[str, Any]][source]

 
Convert every row to a dictionary of Python-native values.

 
Notes

 
If you have ns-precision temporal values you should be aware that Python
@@ -6541,7 +6541,7 @@ 
DataFrame
 

-to_dummies(columns: str | Sequence[str] | None = None, *, separator: str = '_', drop_first: bool = False) → Self[source]

+to_dummies(columns: str | Sequence[str] | None = None, *, separator: str = '_', drop_first: bool = False) → Self[source]
 
Convert categorical variables into dummy/indicator variables.

 

 
Parameters:

@@ -6580,7 +6580,7 @@ 
DataFrame
 

-to_init_repr(n: int = 1000) → str[source]

+to_init_repr(n: int = 1000) → str[source]
 
Convert DataFrame to instantiatable string representation.

 

 
Parameters:

@@ -6633,7 +6633,7 @@ 
DataFrame
 

-to_numpy(structured: bool = False, *, order: IndexOrder = 'fortran') → np.ndarray[Any, Any][source]

+to_numpy(structured: bool = False, *, order: IndexOrder = 'fortran') → np.ndarray[Any, Any][source]
 
Convert DataFrame to a 2D NumPy array.

 
This operation clones data.

 

@@ -6692,7 +6692,7 @@ 
DataFrame
 

-to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → DataFrame[source]

+to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → DataFrame[source]
 
Cast to a pandas DataFrame.

 
This requires that pandas and pyarrow are installed.
 This operation clones data, unless use_pyarrow_extension_array=True.

@@ -6769,7 +6769,7 @@ 
DataFrame
 

-to_series(index: int = 0) → Series[source]

+to_series(index: int = 0) → Series[source]
 
Select column as Series at index location.

 

 
Parameters:

@@ -6807,7 +6807,7 @@ 
DataFrame
 

-to_struct(name: str) → Series[source]

+to_struct(name: str) → Series[source]
 
Convert a DataFrame to a Series of type Struct.

 

 
Parameters:

@@ -6840,7 +6840,7 @@ 
DataFrame
 

-top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]

+top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → DataFrame[source]
 
Return the k largest elements.

 
If ‘descending=True` the smallest elements will be given.

 

@@ -6912,7 +6912,7 @@ 
DataFrame
 

-transpose(*, include_header: bool = False, header_name: str = 'column', column_names: str | Iterable[str] | None = None) → Self[source]

+transpose(*, include_header: bool = False, header_name: str = 'column', column_names: str | Iterable[str] | None = None) → Self[source]
 
Transpose a DataFrame over the diagonal.

 

 
Parameters:

@@ -7025,7 +7025,7 @@ 
DataFrame
 

-unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → DataFrame[source]

+unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → DataFrame[source]
 
Drop duplicate rows from this dataframe.

 

 
Parameters:

@@ -7109,7 +7109,7 @@ 
DataFrame
 

-unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]

+unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]
 
Decompose struct columns into separate columns for each of their fields.

 
The new columns will be inserted into the dataframe at the location of the
 struct column.

@@ -7160,7 +7160,7 @@ 
DataFrame
 

-unstack(step: int, how: UnstackDirection = 'vertical', columns: str | Sequence[str] | None = None, fill_values: list[Any] | None = None) → DataFrame[source]

+unstack(step: int, how: UnstackDirection = 'vertical', columns: str | Sequence[str] | None = None, fill_values: list[Any] | None = None) → DataFrame[source]
 
Unstack a long table to a wide form without doing an aggregation.

 
This can be much faster than a pivot, because it can skip the grouping phase.

 

@@ -7236,7 +7236,7 @@ 
DataFrame
 

-update(other: DataFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → DataFrame[source]

+update(other: DataFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → DataFrame[source]
 
Update the values in this DataFrame with the non-null values in other.

 

 
Parameters:

@@ -7313,7 +7313,7 @@ 
DataFrame
 

-upsample(time_column: str, *, every: str | timedelta, offset: str | timedelta | None = None, by: str | Sequence[str] | None = None, maintain_order: bool = False) → Self[source]

+upsample(time_column: str, *, every: str | timedelta, offset: str | timedelta | None = None, by: str | Sequence[str] | None = None, maintain_order: bool = False) → Self[source]
 
Upsample a DataFrame at a regular frequency.

 
The every and offset arguments are created with
 the following string language:

@@ -7403,7 +7403,7 @@ 
DataFrame
 

-var(ddof: int = 1) → Self[source]

+var(ddof: int = 1) → Self[source]
 
Aggregate the columns of this DataFrame to their variance value.

 

 
Parameters:

@@ -7447,7 +7447,7 @@ 
DataFrame
 

-vstack(other: DataFrame, *, in_place: bool = False) → Self[source]

+vstack(other: DataFrame, *, in_place: bool = False) → Self[source]
 
Grow this DataFrame vertically by stacking a DataFrame to it.

 

 
Parameters:

@@ -7498,7 +7498,7 @@ 
DataFrame
 

-property width: int[source]

+property width: int[source]
 
Get the width of the DataFrame.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
@@ -7510,7 +7510,7 @@ 
DataFrame
 

-with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]

+with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]
 
Add columns to this DataFrame.

 
Added columns will replace existing columns with the same name.

 

@@ -7655,7 +7655,7 @@ 
DataFrame
 

-with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]

+with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]
 
Add a column at index 0 that counts the rows.

 

 
Parameters:

@@ -7691,7 +7691,7 @@ 
DataFrame
 

-write_avro(file: BinaryIO | BytesIO | str | Path, compression: AvroCompression = 'uncompressed') → None[source]

+write_avro(file: BinaryIO | BytesIO | str | Path, compression: AvroCompression = 'uncompressed') → None[source]
 
Write to Apache Avro file.

 

 
Parameters:

@@ -7721,7 +7721,7 @@ 
DataFrame
 

-write_csv(file: None = None, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → str[source]

+write_csv(file: None = None, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → str[source]
 

 write_csv(file: BytesIO | TextIOWrapper | str | Path, *, has_header: bool = True, separator: str = ',', quote: str = '"', batch_size: int = 1024, datetime_format: str | None = None, date_format: str | None = None, time_format: str | None = None, float_precision: int | None = None, null_value: str | None = None) → None

 
Write to comma-separated values (CSV) file.

@@ -7779,7 +7779,7 @@ 
DataFrame
 

-write_database(table_name: str, connection: str, *, if_exists: DbWriteMode = 'fail', engine: DbWriteEngine = 'sqlalchemy') → None[source]

+write_database(table_name: str, connection: str, *, if_exists: DbWriteMode = 'fail', engine: DbWriteEngine = 'sqlalchemy') → None[source]
 
Write a polars frame to a database.

 

 
Parameters:

@@ -7807,7 +7807,7 @@ 
DataFrame
 

-write_delta(target: str | Path | deltalake.DeltaTable, *, mode: Literal['error', 'append', 'overwrite', 'ignore'] = 'error', overwrite_schema: bool = False, storage_options: dict[str, str] | None = None, delta_write_options: dict[str, Any] | None = None) → None[source]

+write_delta(target: str | Path | deltalake.DeltaTable, *, mode: Literal['error', 'append', 'overwrite', 'ignore'] = 'error', overwrite_schema: bool = False, storage_options: dict[str, str] | None = None, delta_write_options: dict[str, Any] | None = None) → None[source]
 
Write DataFrame as delta table.

 
Note: Some polars data types like Null, Categorical and Time are
 not supported by the delta protocol specification.

@@ -7887,7 +7887,7 @@ 
DataFrame
 

-write_excel(workbook: Workbook | BytesIO | Path | str | None = None, worksheet: str | None = None, *, position: tuple[int, int] | str = 'A1', table_style: str | dict[str, Any] | None = None, table_name: str | None = None, column_formats: dict[str | tuple[str, ...], str | dict[str, str]] | None = None, dtype_formats: dict[OneOrMoreDataTypes, str] | None = None, conditional_formats: ConditionalFormatDict | None = None, column_totals: ColumnTotalsDefinition | None = None, column_widths: dict[str | tuple[str, ...], int] | int | None = None, row_totals: RowTotalsDefinition | None = None, row_heights: dict[int | tuple[int, ...], int] | int | None = None, sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None, formulas: dict[str, str | dict[str, str]] | None = None, float_precision: int = 3, has_header: bool = True, autofilter: bool = True, autofit: bool = False, hidden_columns: Sequence[str] | None = None, hide_gridlines: bool = False, sheet_zoom: int | None = None, freeze_panes: str | tuple[int, int] | tuple[str, int, int] | tuple[int, int, int, int] | None = None) → Workbook[source]

+write_excel(workbook: Workbook | BytesIO | Path | str | None = None, worksheet: str | None = None, *, position: tuple[int, int] | str = 'A1', table_style: str | dict[str, Any] | None = None, table_name: str | None = None, column_formats: dict[str | tuple[str, ...], str | dict[str, str]] | None = None, dtype_formats: dict[OneOrMoreDataTypes, str] | None = None, conditional_formats: ConditionalFormatDict | None = None, column_totals: ColumnTotalsDefinition | None = None, column_widths: dict[str | tuple[str, ...], int] | int | None = None, row_totals: RowTotalsDefinition | None = None, row_heights: dict[int | tuple[int, ...], int] | int | None = None, sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None, formulas: dict[str, str | dict[str, str]] | None = None, float_precision: int = 3, has_header: bool = True, autofilter: bool = True, autofit: bool = False, hidden_columns: Sequence[str] | None = None, hide_gridlines: bool = False, sheet_zoom: int | None = None, freeze_panes: str | tuple[int, int] | tuple[str, int, int] | tuple[int, int, int, int] | None = None) → Workbook[source]
 
Write frame data to a table in an Excel workbook/worksheet.

 

 
Parameters:

@@ -8213,7 +8213,7 @@ 
DataFrame
 

-write_ipc(file: None, compression: IpcCompression = 'uncompressed') → BytesIO[source]

+write_ipc(file: None, compression: IpcCompression = 'uncompressed') → BytesIO[source]
 

 write_ipc(file: BinaryIO | BytesIO | str | Path, compression: IpcCompression = 'uncompressed') → None

 
Write to Arrow IPC binary stream or Feather file.

@@ -8246,7 +8246,7 @@ 
DataFrame
 

-write_json(file: None = None, *, pretty: bool = False, row_oriented: bool = False) → str[source]

+write_json(file: None = None, *, pretty: bool = False, row_oriented: bool = False) → str[source]
 

 write_json(file: IOBase | str | Path, *, pretty: bool = False, row_oriented: bool = False) → None

 
Serialize to JSON representation.

@@ -8286,7 +8286,7 @@ 
DataFrame
 

-write_ndjson(file: None = None) → str[source]

+write_ndjson(file: None = None) → str[source]
 

 write_ndjson(file: IOBase | str | Path) → None

 
Serialize to newline delimited JSON representation.

@@ -8314,7 +8314,7 @@ 
DataFrame
 

-write_parquet(file: str | Path | BytesIO, *, compression: ParquetCompression = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, use_pyarrow: bool = False, pyarrow_options: dict[str, object] | None = None) → None[source]

+write_parquet(file: str | Path | BytesIO, *, compression: ParquetCompression = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, use_pyarrow: bool = False, pyarrow_options: dict[str, object] | None = None) → None[source]
 
Write to Apache Parquet file.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.all.html b/py-polars/html/reference/expressions/api/polars.Expr.list.all.html
index a5c3a4c70387..e748a66e6ce4 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.all.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.all.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.all#

 

 

-Expr.list.all() → Expr[source]#

+Expr.list.all() → Expr[source]#
 
Evaluate whether all boolean values in a list are true.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.any.html b/py-polars/html/reference/expressions/api/polars.Expr.list.any.html
index cb2b974aa836..e62365278ed6 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.any.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.any.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.any#

 

 

-Expr.list.any() → Expr[source]#

+Expr.list.any() → Expr[source]#
 
Evaluate whether any boolean value in a list is true.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.arg_max.html b/py-polars/html/reference/expressions/api/polars.Expr.list.arg_max.html
index 6b9afce44044..fae4f322f7a8 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.arg_max.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.arg_max.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.arg_max#

 

 

-Expr.list.arg_max() → Expr[source]#

+Expr.list.arg_max() → Expr[source]#
 
Retrieve the index of the maximum value in every sublist.

 

 
Returns:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.arg_min.html b/py-polars/html/reference/expressions/api/polars.Expr.list.arg_min.html
index cb90f71192bf..75198542b474 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.arg_min.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.arg_min.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.arg_min#

 

 

-Expr.list.arg_min() → Expr[source]#

+Expr.list.arg_min() → Expr[source]#
 
Retrieve the index of the minimal value in every sublist.

 

 
Returns:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.concat.html b/py-polars/html/reference/expressions/api/polars.Expr.list.concat.html
index 642e775c1fd7..7ff248ce0f1c 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.concat.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.concat.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.concat#

 

 

-Expr.list.concat(other: list[Expr | str] | Expr | str | Series | list[Any]) → Expr[source]#

+Expr.list.concat(other: list[Expr | str] | Expr | str | Series | list[Any]) → Expr[source]#
 
Concat the arrays in a Series dtype List in linear time.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.contains.html b/py-polars/html/reference/expressions/api/polars.Expr.list.contains.html
index 40b97f96b470..51fcd410cb30 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.contains.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.contains.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.contains#

 

 

-Expr.list.contains(item: float | str | bool | int | date | datetime | time | Expr) → Expr[source]#

+Expr.list.contains(item: float | str | bool | int | date | datetime | time | Expr) → Expr[source]#
 
Check if sublists contain the given item.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.count_match.html b/py-polars/html/reference/expressions/api/polars.Expr.list.count_match.html
index fbb82e0ba077..a48040dc59b6 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.count_match.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.count_match.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.count_match#

 

 

-Expr.list.count_match(element: IntoExpr) → Expr[source]#

+Expr.list.count_match(element: IntoExpr) → Expr[source]#
 
Count how often the value produced by element occurs.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.diff.html b/py-polars/html/reference/expressions/api/polars.Expr.list.diff.html
index 8e1b03806dc5..94fc02a6ee4f 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.diff.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.diff.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.diff#

 

 

-Expr.list.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Expr[source]#

+Expr.list.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Expr[source]#
 
Calculate the n-th discrete difference of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.eval.html b/py-polars/html/reference/expressions/api/polars.Expr.list.eval.html
index e28889cef48b..f0f47819a86e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.eval.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.eval.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.eval#

 

 

-Expr.list.eval(expr: Expr, *, parallel: bool = False) → Expr[source]#

+Expr.list.eval(expr: Expr, *, parallel: bool = False) → Expr[source]#
 
Run any polars expression against the lists’ elements.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.explode.html b/py-polars/html/reference/expressions/api/polars.Expr.list.explode.html
index 4a39746d2fe8..9f7d2b63292e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.explode.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.explode.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.explode#

 

 

-Expr.list.explode() → Expr[source]#

+Expr.list.explode() → Expr[source]#
 
Returns a column with a separate row for every list element.

 

 
Returns:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.first.html b/py-polars/html/reference/expressions/api/polars.Expr.list.first.html
index 6fd0ed5e0647..e018a666e746 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.first.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.first.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.first#

 

 

-Expr.list.first() → Expr[source]#

+Expr.list.first() → Expr[source]#
 
Get the first value of the sublists.

 
Examples

 
>>> df = pl.DataFrame({"foo": [[3, 2, 1], [], [1, 2]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.get.html b/py-polars/html/reference/expressions/api/polars.Expr.list.get.html
index be5305739673..f54f294f6b6b 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.get.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.get.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.get#

 

 

-Expr.list.get(index: int | Expr | str) → Expr[source]#

+Expr.list.get(index: int | Expr | str) → Expr[source]#
 
Get the value by index in the sublists.

 
So index 0 would return the first item of every sublist
 and index -1 would return the last item of every sublist
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.head.html b/py-polars/html/reference/expressions/api/polars.Expr.list.head.html
index 3322ca5dc93e..868d330bea81 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.head.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.head.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.head#

 

 

-Expr.list.head(n: int | str | Expr = 5) → Expr[source]#

+Expr.list.head(n: int | str | Expr = 5) → Expr[source]#
 
Slice the first n values of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.join.html b/py-polars/html/reference/expressions/api/polars.Expr.list.join.html
index b7e07177a579..042d00ee0e40 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.join.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.join.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.join#

 

 

-Expr.list.join(separator: str) → Expr[source]#

+Expr.list.join(separator: str) → Expr[source]#
 
Join all string items in a sublist and place a separator between them.

 
This errors if inner type of list != Utf8.

 

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.last.html b/py-polars/html/reference/expressions/api/polars.Expr.list.last.html
index 09d0360b1c0f..11d7883c9c40 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.last.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.last.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.last#

 

 

-Expr.list.last() → Expr[source]#

+Expr.list.last() → Expr[source]#
 
Get the last value of the sublists.

 
Examples

 
>>> df = pl.DataFrame({"foo": [[3, 2, 1], [], [1, 2]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.lengths.html b/py-polars/html/reference/expressions/api/polars.Expr.list.lengths.html
index 1d57735c8d57..4962e064e8fe 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.lengths.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.lengths.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.lengths#

 

 

-Expr.list.lengths() → Expr[source]#

+Expr.list.lengths() → Expr[source]#
 
Get the length of the arrays as UInt32.

 
Examples

 
>>> df = pl.DataFrame({"foo": [1, 2], "bar": [["a", "b"], ["c"]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.max.html b/py-polars/html/reference/expressions/api/polars.Expr.list.max.html
index e7c349a45785..8db6df8e57b4 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.max.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.max.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.max#

 

 

-Expr.list.max() → Expr[source]#

+Expr.list.max() → Expr[source]#
 
Compute the max value of the lists in the array.

 
Examples

 
>>> df = pl.DataFrame({"values": [[1], [2, 3]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.mean.html b/py-polars/html/reference/expressions/api/polars.Expr.list.mean.html
index f033b97bb554..edbc1d98e1ef 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.mean.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.mean.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.mean#

 

 

-Expr.list.mean() → Expr[source]#

+Expr.list.mean() → Expr[source]#
 
Compute the mean value of the lists in the array.

 
Examples

 
>>> df = pl.DataFrame({"values": [[1], [2, 3]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.min.html b/py-polars/html/reference/expressions/api/polars.Expr.list.min.html
index ef286f3c0d5d..1fa6ff83df0e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.min.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.min.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.min#

 

 

-Expr.list.min() → Expr[source]#

+Expr.list.min() → Expr[source]#
 
Compute the min value of the lists in the array.

 
Examples

 
>>> df = pl.DataFrame({"values": [[1], [2, 3]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.reverse.html b/py-polars/html/reference/expressions/api/polars.Expr.list.reverse.html
index 9ea6c284713c..8e6758d20d2d 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.reverse.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.reverse.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.reverse#

 

 

-Expr.list.reverse() → Expr[source]#

+Expr.list.reverse() → Expr[source]#
 
Reverse the arrays in the list.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.set_difference.html b/py-polars/html/reference/expressions/api/polars.Expr.list.set_difference.html
index b29ee7f9abf6..9e0e09413fc8 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.set_difference.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.set_difference.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.set_difference#

 

 

-Expr.list.set_difference(other: Expr | IntoExpr) → Expr[source]#

+Expr.list.set_difference(other: Expr | IntoExpr) → Expr[source]#
 
Compute the SET DIFFERENCE between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.set_intersection.html b/py-polars/html/reference/expressions/api/polars.Expr.list.set_intersection.html
index e8c4f2adda7c..d6a2709fd3d1 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.set_intersection.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.set_intersection.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.set_intersection#

 

 

-Expr.list.set_intersection(other: Expr | IntoExpr) → Expr[source]#

+Expr.list.set_intersection(other: Expr | IntoExpr) → Expr[source]#
 
Compute the SET INTERSECTION between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.set_symmetric_difference.html b/py-polars/html/reference/expressions/api/polars.Expr.list.set_symmetric_difference.html
index efb91039efe5..207449a6c694 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.set_symmetric_difference.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.set_symmetric_difference.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.set_symmetric_difference#

 

 

-Expr.list.set_symmetric_difference(other: Expr | IntoExpr) → Expr[source]#

+Expr.list.set_symmetric_difference(other: Expr | IntoExpr) → Expr[source]#
 
Compute the SET SYMMETRIC DIFFERENCE between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.set_union.html b/py-polars/html/reference/expressions/api/polars.Expr.list.set_union.html
index cf6b7b49309f..7cd4f469f8b7 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.set_union.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.set_union.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.set_union#

 

 

-Expr.list.set_union(other: Expr | IntoExpr) → Expr[source]#

+Expr.list.set_union(other: Expr | IntoExpr) → Expr[source]#
 
Compute the SET UNION between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.shift.html b/py-polars/html/reference/expressions/api/polars.Expr.list.shift.html
index 4f98d6cb8a96..7eaae9f14b16 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.shift.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.shift.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.shift#

 

 

-Expr.list.shift(periods: int = 1) → Expr[source]#

+Expr.list.shift(periods: int = 1) → Expr[source]#
 
Shift values by the given period.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.slice.html b/py-polars/html/reference/expressions/api/polars.Expr.list.slice.html
index 55ff6c373625..9dafb22596e7 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.slice.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.slice.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.slice#

 

 

-Expr.list.slice(offset: int | str | Expr, length: int | str | Expr | None = None) → Expr[source]#

+Expr.list.slice(offset: int | str | Expr, length: int | str | Expr | None = None) → Expr[source]#
 
Slice every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.sort.html b/py-polars/html/reference/expressions/api/polars.Expr.list.sort.html
index e1c473d5015b..fb8973a76547 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.sort.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.sort.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.sort#

 

 

-Expr.list.sort(*, descending: bool = False) → Expr[source]#

+Expr.list.sort(*, descending: bool = False) → Expr[source]#
 
Sort the arrays in this column.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.sum.html b/py-polars/html/reference/expressions/api/polars.Expr.list.sum.html
index 941933241953..f78ded33ba79 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.sum.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.sum.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.sum#

 

 

-Expr.list.sum() → Expr[source]#

+Expr.list.sum() → Expr[source]#
 
Sum all the lists in the array.

 
Examples

 
>>> df = pl.DataFrame({"values": [[1], [2, 3]]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.tail.html b/py-polars/html/reference/expressions/api/polars.Expr.list.tail.html
index caa447f5c8c8..4981d100b261 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.tail.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.tail.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.tail#

 

 

-Expr.list.tail(n: int | str | Expr = 5) → Expr[source]#

+Expr.list.tail(n: int | str | Expr = 5) → Expr[source]#
 
Slice the last n values of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.take.html b/py-polars/html/reference/expressions/api/polars.Expr.list.take.html
index d46c0885a37c..293f7afb1daa 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.take.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.take.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.take#

 

 

-Expr.list.take(index: Expr | Series | list[int] | list[list[int]], *, null_on_oob: bool = False) → Expr[source]#

+Expr.list.take(index: Expr | Series | list[int] | list[list[int]], *, null_on_oob: bool = False) → Expr[source]#
 
Take sublists by multiple indices.

 
The indices may be defined in a single column, or by sublists in another
 column of dtype List.

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.to_struct.html b/py-polars/html/reference/expressions/api/polars.Expr.list.to_struct.html
index 9f43643e307d..583fe2aee199 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.to_struct.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.to_struct.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.to_struct#

 

 

-Expr.list.to_struct(n_field_strategy: ToStructStrategy = 'first_non_null', fields: Sequence[str] | Callable[[int], str] | None = None, upper_bound: int = 0) → Expr[source]#

+Expr.list.to_struct(n_field_strategy: ToStructStrategy = 'first_non_null', fields: Sequence[str] | Callable[[int], str] | None = None, upper_bound: int = 0) → Expr[source]#
 
Convert the series of type List to a series of type Struct.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.list.unique.html b/py-polars/html/reference/expressions/api/polars.Expr.list.unique.html
index 52852639cfdc..f95cd332542e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.list.unique.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.list.unique.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.list.unique#

 

 

-Expr.list.unique(*, maintain_order: bool = False) → Expr[source]#

+Expr.list.unique(*, maintain_order: bool = False) → Expr[source]#
 
Get the unique/distinct values in the list.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.concat.html b/py-polars/html/reference/expressions/api/polars.Expr.str.concat.html
index 5f73cccc102e..e824306510da 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.concat.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.concat.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.concat#

 

 

-Expr.str.concat(delimiter: str = '-') → Expr[source]#

+Expr.str.concat(delimiter: str = '-') → Expr[source]#
 
Vertically concat the values in the Series to a single string value.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.contains.html b/py-polars/html/reference/expressions/api/polars.Expr.str.contains.html
index 8870cad0da01..9352f12272f0 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.contains.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.contains.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.contains#

 

 

-Expr.str.contains(pattern: str | Expr, *, literal: bool = False, strict: bool = True) → Expr[source]#

+Expr.str.contains(pattern: str | Expr, *, literal: bool = False, strict: bool = True) → Expr[source]#
 
Check if string contains a substring that matches a regex.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.count_match.html b/py-polars/html/reference/expressions/api/polars.Expr.str.count_match.html
index 0cce6fc035e2..222ad393d3c5 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.count_match.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.count_match.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.count_match#

 

 

-Expr.str.count_match(pattern: str) → Expr[source]#

+Expr.str.count_match(pattern: str) → Expr[source]#
 
Count all successive non-overlapping regex matches.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.decode.html b/py-polars/html/reference/expressions/api/polars.Expr.str.decode.html
index 05673efb9675..38a07ba75c85 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.decode.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.decode.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.decode#

 

 

-Expr.str.decode(encoding: TransferEncoding, *, strict: bool = True) → Expr[source]#

+Expr.str.decode(encoding: TransferEncoding, *, strict: bool = True) → Expr[source]#
 
Decode a value using the provided encoding.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.encode.html b/py-polars/html/reference/expressions/api/polars.Expr.str.encode.html
index 14ab7ff22fdb..31ac627a6596 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.encode.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.encode.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.encode#

 

 

-Expr.str.encode(encoding: TransferEncoding) → Expr[source]#

+Expr.str.encode(encoding: TransferEncoding) → Expr[source]#
 
Encode a value using the provided encoding.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.ends_with.html b/py-polars/html/reference/expressions/api/polars.Expr.str.ends_with.html
index 675072407507..8e0026e33c93 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.ends_with.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.ends_with.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.ends_with#

 

 

-Expr.str.ends_with(suffix: str | Expr) → Expr[source]#

+Expr.str.ends_with(suffix: str | Expr) → Expr[source]#
 
Check if string values end with a substring.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.explode.html b/py-polars/html/reference/expressions/api/polars.Expr.str.explode.html
index 5cc1160a09a5..30d8d4a4f5ae 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.explode.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.explode.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.explode#

 

 

-Expr.str.explode() → Expr[source]#

+Expr.str.explode() → Expr[source]#
 
Returns a column with a separate row for every string character.

 

 
Returns:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.extract.html b/py-polars/html/reference/expressions/api/polars.Expr.str.extract.html
index f401f4777be0..20585d94a3f1 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.extract.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.extract.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.extract#

 

 

-Expr.str.extract(pattern: str, group_index: int = 1) → Expr[source]#

+Expr.str.extract(pattern: str, group_index: int = 1) → Expr[source]#
 
Extract the target capture group from provided patterns.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.extract_all.html b/py-polars/html/reference/expressions/api/polars.Expr.str.extract_all.html
index c5bba441408b..38b3edfc005a 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.extract_all.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.extract_all.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.extract_all#

 

 

-Expr.str.extract_all(pattern: str | Expr) → Expr[source]#

+Expr.str.extract_all(pattern: str | Expr) → Expr[source]#
 
Extract all matches for the given regex pattern.

 
Extract each successive non-overlapping regex match in an individual string
 as a list. Extracted matches contain null if the original value is null
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.json_extract.html b/py-polars/html/reference/expressions/api/polars.Expr.str.json_extract.html
index 3b48d3151928..ae90afe967d0 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.json_extract.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.json_extract.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.json_extract#

 

 

-Expr.str.json_extract(dtype: PolarsDataType | None = None, infer_schema_length: int | None = 100) → Expr[source]#

+Expr.str.json_extract(dtype: PolarsDataType | None = None, infer_schema_length: int | None = 100) → Expr[source]#
 
Parse string values as JSON.

 
Throw errors if encounter invalid JSON strings.

 

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.json_path_match.html b/py-polars/html/reference/expressions/api/polars.Expr.str.json_path_match.html
index 51c8117ce5a5..498ab1bb04b4 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.json_path_match.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.json_path_match.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.json_path_match#

 

 

-Expr.str.json_path_match(json_path: str) → Expr[source]#

+Expr.str.json_path_match(json_path: str) → Expr[source]#
 
Extract the first match of JSON string with the provided JSONPath expression.

 
Throws errors if invalid JSON strings are encountered.
 All return values will be cast to Utf8 regardless of the original
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.lengths.html b/py-polars/html/reference/expressions/api/polars.Expr.str.lengths.html
index 3155cf8dce0e..8380fa6342fd 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.lengths.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.lengths.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.lengths#

 

 

-Expr.str.lengths() → Expr[source]#

+Expr.str.lengths() → Expr[source]#
 
Get length of the strings as UInt32 (as number of bytes).

 
Notes

 
The returned lengths are equal to the number of bytes in the UTF8 string. If you
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.ljust.html b/py-polars/html/reference/expressions/api/polars.Expr.str.ljust.html
index 1a6113009c68..f455d068975e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.ljust.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.ljust.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.ljust#

 

 

-Expr.str.ljust(width: int, fill_char: str = ' ') → Expr[source]#

+Expr.str.ljust(width: int, fill_char: str = ' ') → Expr[source]#
 
Return the string left justified in a string of length width.

 
Padding is done using the specified fill_char.
 The original string is returned if width is less than or equal to
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.lstrip.html b/py-polars/html/reference/expressions/api/polars.Expr.str.lstrip.html
index 39feeffc4107..bb182d4eba8c 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.lstrip.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.lstrip.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.lstrip#

 

 

-Expr.str.lstrip(characters: str | None = None) → Expr[source]#

+Expr.str.lstrip(characters: str | None = None) → Expr[source]#
 
Remove leading characters.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.n_chars.html b/py-polars/html/reference/expressions/api/polars.Expr.str.n_chars.html
index fbbfb73ac0aa..cb6d22320432 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.n_chars.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.n_chars.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.n_chars#

 

 

-Expr.str.n_chars() → Expr[source]#

+Expr.str.n_chars() → Expr[source]#
 
Get length of the strings as UInt32 (as number of chars).

 
Notes

 
If you know that you are working with ASCII text, lengths will be
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.parse_int.html b/py-polars/html/reference/expressions/api/polars.Expr.str.parse_int.html
index ba9ee57e450f..c9e7bd7e3b2c 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.parse_int.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.parse_int.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.parse_int#

 

 

-Expr.str.parse_int(radix: int = 2, *, strict: bool = True) → Expr[source]#

+Expr.str.parse_int(radix: int = 2, *, strict: bool = True) → Expr[source]#
 
Parse integers with base radix from strings.

 
By default base 2. ParseError/Overflows become Nulls.

 

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.replace.html b/py-polars/html/reference/expressions/api/polars.Expr.str.replace.html
index b37d091c2b1e..aa95a7e5876c 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.replace.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.replace.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.replace#

 

 

-Expr.str.replace(pattern: str | Expr, value: str | Expr, *, literal: bool = False, n: int = 1) → Expr[source]#

+Expr.str.replace(pattern: str | Expr, value: str | Expr, *, literal: bool = False, n: int = 1) → Expr[source]#
 
Replace first matching regex/literal substring with a new string value.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.replace_all.html b/py-polars/html/reference/expressions/api/polars.Expr.str.replace_all.html
index 49ee183769b2..c3b3d2193ef3 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.replace_all.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.replace_all.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.replace_all#

 

 

-Expr.str.replace_all(pattern: str | Expr, value: str | Expr, *, literal: bool = False) → Expr[source]#

+Expr.str.replace_all(pattern: str | Expr, value: str | Expr, *, literal: bool = False) → Expr[source]#
 
Replace all matching regex/literal substrings with a new string value.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.rjust.html b/py-polars/html/reference/expressions/api/polars.Expr.str.rjust.html
index 06ce3d381485..c3d8d4aecf7f 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.rjust.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.rjust.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.rjust#

 

 

-Expr.str.rjust(width: int, fill_char: str = ' ') → Expr[source]#

+Expr.str.rjust(width: int, fill_char: str = ' ') → Expr[source]#
 
Return the string right justified in a string of length width.

 
Padding is done using the specified fill_char.
 The original string is returned if width is less than or equal to
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.rstrip.html b/py-polars/html/reference/expressions/api/polars.Expr.str.rstrip.html
index a832cc29ee55..cf0e7b7aef9e 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.rstrip.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.rstrip.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.rstrip#

 

 

-Expr.str.rstrip(characters: str | None = None) → Expr[source]#

+Expr.str.rstrip(characters: str | None = None) → Expr[source]#
 
Remove trailing characters.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.slice.html b/py-polars/html/reference/expressions/api/polars.Expr.str.slice.html
index 9e23f074b24c..259b62875d3f 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.slice.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.slice.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.slice#

 

 

-Expr.str.slice(offset: int, length: int | None = None) → Expr[source]#

+Expr.str.slice(offset: int, length: int | None = None) → Expr[source]#
 
Create subslices of the string values of a Utf8 Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.split.html b/py-polars/html/reference/expressions/api/polars.Expr.str.split.html
index 62d7f44da8ec..0cde95ad2b18 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.split.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.split.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.split#

 

 

-Expr.str.split(by: str, *, inclusive: bool = False) → Expr[source]#

+Expr.str.split(by: str, *, inclusive: bool = False) → Expr[source]#
 
Split the string by a substring.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.split_exact.html b/py-polars/html/reference/expressions/api/polars.Expr.str.split_exact.html
index c1a093232b3f..ccaa4dfac91a 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.split_exact.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.split_exact.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.split_exact#

 

 

-Expr.str.split_exact(by: str, n: int, *, inclusive: bool = False) → Expr[source]#

+Expr.str.split_exact(by: str, n: int, *, inclusive: bool = False) → Expr[source]#
 
Split the string by a substring using n splits.

 
Results in a struct of n+1 fields.

 
If it cannot make n splits, the remaining field elements will be null.

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.splitn.html b/py-polars/html/reference/expressions/api/polars.Expr.str.splitn.html
index c8673e44b3a1..352fe3b8d975 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.splitn.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.splitn.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.splitn#

 

 

-Expr.str.splitn(by: str, n: int) → Expr[source]#

+Expr.str.splitn(by: str, n: int) → Expr[source]#
 
Split the string by a substring, restricted to returning at most n items.

 
If the number of possible splits is less than n-1, the remaining field
 elements will be null. If the number of possible splits is n-1 or greater,
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.starts_with.html b/py-polars/html/reference/expressions/api/polars.Expr.str.starts_with.html
index 6ff7731b4514..083bb03ebdfb 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.starts_with.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.starts_with.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.starts_with#

 

 

-Expr.str.starts_with(prefix: str | Expr) → Expr[source]#

+Expr.str.starts_with(prefix: str | Expr) → Expr[source]#
 
Check if string values start with a substring.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.strip.html b/py-polars/html/reference/expressions/api/polars.Expr.str.strip.html
index edacc1ed8ca7..5509dcf1eed8 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.strip.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.strip.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.strip#

 

 

-Expr.str.strip(characters: str | None = None) → Expr[source]#

+Expr.str.strip(characters: str | None = None) → Expr[source]#
 
Remove leading and trailing characters.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.strptime.html b/py-polars/html/reference/expressions/api/polars.Expr.str.strptime.html
index 284c9a822e11..82a204014829 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.strptime.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.strptime.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.strptime#

 

 

-Expr.str.strptime(dtype: PolarsTemporalType, format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Expr[source]#

+Expr.str.strptime(dtype: PolarsTemporalType, format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Expr[source]#
 
Convert a Utf8 column into a Date/Datetime/Time column.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_date.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_date.html
index e01255716fff..a3cded5ac909 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_date.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_date.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_date#

 

 

-Expr.str.to_date(format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True) → Expr[source]#

+Expr.str.to_date(format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True) → Expr[source]#
 
Convert a Utf8 column into a Date column.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_datetime.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_datetime.html
index 90167bf15b83..4be2c8e12cb0 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_datetime.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_datetime.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_datetime#

 

 

-Expr.str.to_datetime(format: str | None = None, *, time_unit: TimeUnit | None = None, time_zone: str | None = None, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Expr[source]#

+Expr.str.to_datetime(format: str | None = None, *, time_unit: TimeUnit | None = None, time_zone: str | None = None, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Expr[source]#
 
Convert a Utf8 column into a Datetime column.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_decimal.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_decimal.html
index d378b6725dcb..4559e140f2a5 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_decimal.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_decimal.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_decimal#

 

 

-Expr.str.to_decimal(inference_length: int = 100) → Expr[source]#

+Expr.str.to_decimal(inference_length: int = 100) → Expr[source]#
 
Convert a Utf8 column into a Decimal column.

 
This method infers the needed parameters precision and scale.

 

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_lowercase.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_lowercase.html
index a8314ba193a5..66f3b8701db4 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_lowercase.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_lowercase.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_lowercase#

 

 

-Expr.str.to_lowercase() → Expr[source]#

+Expr.str.to_lowercase() → Expr[source]#
 
Transform to lowercase variant.

 
Examples

 
>>> df = pl.DataFrame({"foo": ["CAT", "DOG"]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_time.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_time.html
index 182f61531dc7..6b81af6d7047 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_time.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_time.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_time#

 

 

-Expr.str.to_time(format: str | None = None, *, strict: bool = True, cache: bool = True) → Expr[source]#

+Expr.str.to_time(format: str | None = None, *, strict: bool = True, cache: bool = True) → Expr[source]#
 
Convert a Utf8 column into a Time column.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_titlecase.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_titlecase.html
index 46527f8fa8d0..96852ceb6c87 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_titlecase.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_titlecase.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_titlecase#

 

 

-Expr.str.to_titlecase() → Expr[source]#

+Expr.str.to_titlecase() → Expr[source]#
 
Transform to titlecase variant.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.to_uppercase.html b/py-polars/html/reference/expressions/api/polars.Expr.str.to_uppercase.html
index 740e04ece6ae..bf84c0e78147 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.to_uppercase.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.to_uppercase.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.to_uppercase#

 

 

-Expr.str.to_uppercase() → Expr[source]#

+Expr.str.to_uppercase() → Expr[source]#
 
Transform to uppercase variant.

 
Examples

 
>>> df = pl.DataFrame({"foo": ["cat", "dog"]})
diff --git a/py-polars/html/reference/expressions/api/polars.Expr.str.zfill.html b/py-polars/html/reference/expressions/api/polars.Expr.str.zfill.html
index 13ee3650d076..6fcdfd5fc97b 100644
--- a/py-polars/html/reference/expressions/api/polars.Expr.str.zfill.html
+++ b/py-polars/html/reference/expressions/api/polars.Expr.str.zfill.html
@@ -1628,7 +1628,7 @@
 
polars.Expr.str.zfill#

 

 

-Expr.str.zfill(alignment: int) → Expr[source]#

+Expr.str.zfill(alignment: int) → Expr[source]#
 
Fills the string with zeroes.

 
Return a copy of the string left filled with ASCII ‘0’ digits to make a string
 of length width.

diff --git a/py-polars/html/reference/expressions/api/polars.all.html b/py-polars/html/reference/expressions/api/polars.all.html
index 44eb4bd7505e..843a95724276 100644
--- a/py-polars/html/reference/expressions/api/polars.all.html
+++ b/py-polars/html/reference/expressions/api/polars.all.html
@@ -1628,7 +1628,7 @@
 
polars.all#

 

 

-polars.all(exprs: Series) → bool[source]#

+polars.all(exprs: Series) → bool[source]#
 

 polars.all(exprs: IntoExpr | Iterable[IntoExpr] | None = None, *more_exprs: IntoExpr) → Expr

 
Either return an expression representing all columns, or evaluate a bitwise AND operation.

diff --git a/py-polars/html/reference/expressions/api/polars.any.html b/py-polars/html/reference/expressions/api/polars.any.html
index fda7dd286ab8..cb51baa5c418 100644
--- a/py-polars/html/reference/expressions/api/polars.any.html
+++ b/py-polars/html/reference/expressions/api/polars.any.html
@@ -1628,7 +1628,7 @@
 
polars.any#

 

 

-polars.any(exprs: Series) → bool[source]#

+polars.any(exprs: Series) → bool[source]#
 

 polars.any(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr

 
Evaluate a bitwise OR operation.

diff --git a/py-polars/html/reference/expressions/api/polars.apply.html b/py-polars/html/reference/expressions/api/polars.apply.html
index bd729582cc0a..d863ea0ba0d3 100644
--- a/py-polars/html/reference/expressions/api/polars.apply.html
+++ b/py-polars/html/reference/expressions/api/polars.apply.html
@@ -1628,7 +1628,7 @@
 
polars.apply#

 

 

-polars.apply(exprs: Sequence[str | Expr], function: Callable[[Sequence[Series]], Series | Any], return_dtype: PolarsDataType | None = None, *, returns_scalar: bool = True) → Expr[source]#

+polars.apply(exprs: Sequence[str | Expr], function: Callable[[Sequence[Series]], Series | Any], return_dtype: PolarsDataType | None = None, *, returns_scalar: bool = True) → Expr[source]#
 
Apply a custom/user-defined function (UDF) in a GroupBy context.

 

 
Warning

diff --git a/py-polars/html/reference/expressions/api/polars.approx_unique.html b/py-polars/html/reference/expressions/api/polars.approx_unique.html
index a205138b07ed..8d1e71b898ba 100644
--- a/py-polars/html/reference/expressions/api/polars.approx_unique.html
+++ b/py-polars/html/reference/expressions/api/polars.approx_unique.html
@@ -1628,7 +1628,7 @@
 
polars.approx_unique#

 

 

-polars.approx_unique(column: str | Expr) → Expr[source]#

+polars.approx_unique(column: str | Expr) → Expr[source]#
 
Approx count unique values.

 
This is done using the HyperLogLog++ algorithm for cardinality estimation.

 

diff --git a/py-polars/html/reference/expressions/api/polars.arange.html b/py-polars/html/reference/expressions/api/polars.arange.html
index 9611b1f951ee..98782febe2a5 100644
--- a/py-polars/html/reference/expressions/api/polars.arange.html
+++ b/py-polars/html/reference/expressions/api/polars.arange.html
@@ -1628,7 +1628,7 @@
 
polars.arange#

 

 

-polars.arange(start: int | Expr | Series, end: int | Expr | Series, step: int = 1, *, dtype: PolarsDataType | None = None, eager: Literal[False] = False) → Expr[source]#

+polars.arange(start: int | Expr | Series, end: int | Expr | Series, step: int = 1, *, dtype: PolarsDataType | None = None, eager: Literal[False] = False) → Expr[source]#
 

 polars.arange(start: int | IntoExpr, end: int | IntoExpr, step: int = 1, *, dtype: PolarsDataType | None = None, eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.arctan2.html b/py-polars/html/reference/expressions/api/polars.arctan2.html
index 172c9578f4b9..33ca9c9dd7f1 100644
--- a/py-polars/html/reference/expressions/api/polars.arctan2.html
+++ b/py-polars/html/reference/expressions/api/polars.arctan2.html
@@ -1628,7 +1628,7 @@
 
polars.arctan2#

 

 

-polars.arctan2(y: str | Expr, x: str | Expr) → Expr[source]#

+polars.arctan2(y: str | Expr, x: str | Expr) → Expr[source]#

 
Compute two argument arctan in radians.

 
Returns the angle (in radians) in the plane between the
 positive x-axis and the ray from the origin to (x,y).

diff --git a/py-polars/html/reference/expressions/api/polars.arctan2d.html b/py-polars/html/reference/expressions/api/polars.arctan2d.html
index cee72bdd64bf..cae8517ad448 100644
--- a/py-polars/html/reference/expressions/api/polars.arctan2d.html
+++ b/py-polars/html/reference/expressions/api/polars.arctan2d.html
@@ -1628,7 +1628,7 @@
 
polars.arctan2d#

 

 

-polars.arctan2d(y: str | Expr, x: str | Expr) → Expr[source]#

+polars.arctan2d(y: str | Expr, x: str | Expr) → Expr[source]#
 
Compute two argument arctan in degrees.

 
Returns the angle (in degrees) in the plane between the positive x-axis
 and the ray from the origin to (x,y).

diff --git a/py-polars/html/reference/expressions/api/polars.arg_sort_by.html b/py-polars/html/reference/expressions/api/polars.arg_sort_by.html
index 35ff7c843773..f50dc1dd74e5 100644
--- a/py-polars/html/reference/expressions/api/polars.arg_sort_by.html
+++ b/py-polars/html/reference/expressions/api/polars.arg_sort_by.html
@@ -1628,7 +1628,7 @@
 
polars.arg_sort_by#

 

 

-polars.arg_sort_by(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, descending: bool | Sequence[bool] = False) → Expr[source]#

+polars.arg_sort_by(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, descending: bool | Sequence[bool] = False) → Expr[source]#
 
Return the row indices that would sort the columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.arg_where.html b/py-polars/html/reference/expressions/api/polars.arg_where.html
index d27a977a6716..1659cdd61b8f 100644
--- a/py-polars/html/reference/expressions/api/polars.arg_where.html
+++ b/py-polars/html/reference/expressions/api/polars.arg_where.html
@@ -1628,7 +1628,7 @@
 
polars.arg_where#

 

 

-polars.arg_where(condition: Expr | Series, *, eager: Literal[False] = False) → Expr[source]#

+polars.arg_where(condition: Expr | Series, *, eager: Literal[False] = False) → Expr[source]#
 

 polars.arg_where(condition: Expr | Series, *, eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.avg.html b/py-polars/html/reference/expressions/api/polars.avg.html
index 5dc07d8dbf8f..679420bcf35f 100644
--- a/py-polars/html/reference/expressions/api/polars.avg.html
+++ b/py-polars/html/reference/expressions/api/polars.avg.html
@@ -1628,7 +1628,7 @@
 
polars.avg#

 

 

-polars.avg(column: str) → Expr[source]#

+polars.avg(column: str) → Expr[source]#

 

 polars.avg(column: Series) → float

 
Alias for mean.

diff --git a/py-polars/html/reference/expressions/api/polars.coalesce.html b/py-polars/html/reference/expressions/api/polars.coalesce.html
index fda3e3dd5da4..4657422b929a 100644
--- a/py-polars/html/reference/expressions/api/polars.coalesce.html
+++ b/py-polars/html/reference/expressions/api/polars.coalesce.html
@@ -1628,7 +1628,7 @@
 
polars.coalesce#

 

 

-polars.coalesce(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr[source]#

+polars.coalesce(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr[source]#
 
Folds the columns from left to right, keeping the first non-null value.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.col.html b/py-polars/html/reference/expressions/api/polars.col.html
index 031972f7d3f9..5841da7c108d 100644
--- a/py-polars/html/reference/expressions/api/polars.col.html
+++ b/py-polars/html/reference/expressions/api/polars.col.html
@@ -1628,7 +1628,7 @@
 
polars.col#

 

 

-polars.col(name: str | PolarsDataType | Iterable[str] | Iterable[PolarsDataType], *more_names: str | PolarsDataType) → Expr[source]#

+polars.col(name: str | PolarsDataType | Iterable[str] | Iterable[PolarsDataType], *more_names: str | PolarsDataType) → Expr[source]#
 
Return an expression representing column(s) in a dataframe.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.corr.html b/py-polars/html/reference/expressions/api/polars.corr.html
index 17424897a0de..4d345461bb67 100644
--- a/py-polars/html/reference/expressions/api/polars.corr.html
+++ b/py-polars/html/reference/expressions/api/polars.corr.html
@@ -1628,7 +1628,7 @@
 
polars.corr#

 

 

-polars.corr(a: str | Expr, b: str | Expr, *, method: CorrelationMethod = 'pearson', ddof: int = 1, propagate_nans: bool = False) → Expr[source]#

+polars.corr(a: str | Expr, b: str | Expr, *, method: CorrelationMethod = 'pearson', ddof: int = 1, propagate_nans: bool = False) → Expr[source]#
 
Compute the Pearson’s or Spearman rank correlation correlation between two columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.count.html b/py-polars/html/reference/expressions/api/polars.count.html
index 4486d5b62332..487a461a0b98 100644
--- a/py-polars/html/reference/expressions/api/polars.count.html
+++ b/py-polars/html/reference/expressions/api/polars.count.html
@@ -1628,7 +1628,7 @@
 
polars.count#

 

 

-polars.count(column: str) → Expr[source]#

+polars.count(column: str) → Expr[source]#
 

 polars.count(column: Series) → int

 

diff --git a/py-polars/html/reference/expressions/api/polars.cov.html b/py-polars/html/reference/expressions/api/polars.cov.html
index f7faffe1c694..691fb56d76ca 100644
--- a/py-polars/html/reference/expressions/api/polars.cov.html
+++ b/py-polars/html/reference/expressions/api/polars.cov.html
@@ -1628,7 +1628,7 @@
 
polars.cov#

 

 

-polars.cov(a: str | Expr, b: str | Expr) → Expr[source]#

+polars.cov(a: str | Expr, b: str | Expr) → Expr[source]#

 
Compute the covariance between two columns/ expressions.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.cumfold.html b/py-polars/html/reference/expressions/api/polars.cumfold.html
index 846569b49f69..e6101bd62cc1 100644
--- a/py-polars/html/reference/expressions/api/polars.cumfold.html
+++ b/py-polars/html/reference/expressions/api/polars.cumfold.html
@@ -1628,7 +1628,7 @@
 
polars.cumfold#

 

 

-polars.cumfold(acc: IntoExpr, function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr, *, include_init: bool = False) → Expr[source]#

+polars.cumfold(acc: IntoExpr, function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr, *, include_init: bool = False) → Expr[source]#
 
Cumulatively accumulate over multiple columns horizontally/ row wise with a left fold.

 
Every cumulative result is added as a separate field in a Struct column.

 

diff --git a/py-polars/html/reference/expressions/api/polars.cumreduce.html b/py-polars/html/reference/expressions/api/polars.cumreduce.html
index 8ea47575e07e..a2009d11b4f9 100644
--- a/py-polars/html/reference/expressions/api/polars.cumreduce.html
+++ b/py-polars/html/reference/expressions/api/polars.cumreduce.html
@@ -1628,7 +1628,7 @@
 
polars.cumreduce#

 

 

-polars.cumreduce(function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#

+polars.cumreduce(function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#
 
Cumulatively accumulate over multiple columns horizontally/ row wise with a left fold.

 
Every cumulative result is added as a separate field in a Struct column.

 

diff --git a/py-polars/html/reference/expressions/api/polars.cumsum.html b/py-polars/html/reference/expressions/api/polars.cumsum.html
index 377db0f6b0ae..30737f327eca 100644
--- a/py-polars/html/reference/expressions/api/polars.cumsum.html
+++ b/py-polars/html/reference/expressions/api/polars.cumsum.html
@@ -1628,7 +1628,7 @@
 
polars.cumsum#

 

 

-polars.cumsum(exprs: Series) → Series[source]#

+polars.cumsum(exprs: Series) → Series[source]#
 

 polars.cumsum(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr

 
Cumulatively sum all values.

diff --git a/py-polars/html/reference/expressions/api/polars.date_range.html b/py-polars/html/reference/expressions/api/polars.date_range.html
index 7ea5a9128e89..2cb27af94158 100644
--- a/py-polars/html/reference/expressions/api/polars.date_range.html
+++ b/py-polars/html/reference/expressions/api/polars.date_range.html
@@ -1628,7 +1628,7 @@
 
polars.date_range#

 

 

-polars.date_range(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[False] = False, name: str | None = None) → Expr[source]#

+polars.date_range(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[False] = False, name: str | None = None) → Expr[source]#
 

 polars.date_range(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[True], name: str | None = None) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.date_ranges.html b/py-polars/html/reference/expressions/api/polars.date_ranges.html
index f706ef2657ae..d40dee503e72 100644
--- a/py-polars/html/reference/expressions/api/polars.date_ranges.html
+++ b/py-polars/html/reference/expressions/api/polars.date_ranges.html
@@ -1628,7 +1628,7 @@
 
polars.date_ranges#

 

 

-polars.date_ranges(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[False] = False) → Expr[source]#

+polars.date_ranges(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[False] = False) → Expr[source]#

 

 polars.date_ranges(start: date | datetime | IntoExpr, end: date | datetime | IntoExpr, interval: str | timedelta = '1d', *, closed: ClosedInterval = 'both', time_unit: TimeUnit | None = None, time_zone: str | None = None, eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.element.html b/py-polars/html/reference/expressions/api/polars.element.html
index f79a0cf36262..7296ea80897d 100644
--- a/py-polars/html/reference/expressions/api/polars.element.html
+++ b/py-polars/html/reference/expressions/api/polars.element.html
@@ -1628,7 +1628,7 @@
 
polars.element#

 

 

-polars.element() → Expr[source]#

+polars.element() → Expr[source]#

 
Alias for an element being evaluated in an eval expression.

 
Examples

 
A horizontal rank computation by taking the elements of a list

diff --git a/py-polars/html/reference/expressions/api/polars.exclude.html b/py-polars/html/reference/expressions/api/polars.exclude.html
index 5a5bd816c674..48c4d7e0a7f9 100644
--- a/py-polars/html/reference/expressions/api/polars.exclude.html
+++ b/py-polars/html/reference/expressions/api/polars.exclude.html
@@ -1628,7 +1628,7 @@
 
polars.exclude#

 

 

-polars.exclude(columns: str | PolarsDataType | Iterable[str] | Iterable[PolarsDataType], *more_columns: str | PolarsDataType) → Expr[source]#

+polars.exclude(columns: str | PolarsDataType | Iterable[str] | Iterable[PolarsDataType], *more_columns: str | PolarsDataType) → Expr[source]#
 
Represent all columns except for the given columns.

 
Syntactic sugar for pl.all().exclude(columns).

 

diff --git a/py-polars/html/reference/expressions/api/polars.first.html b/py-polars/html/reference/expressions/api/polars.first.html
index 797df7074020..7c5918874218 100644
--- a/py-polars/html/reference/expressions/api/polars.first.html
+++ b/py-polars/html/reference/expressions/api/polars.first.html
@@ -1628,7 +1628,7 @@
 
polars.first#

 

 

-polars.first(column: str) → Expr[source]#

+polars.first(column: str) → Expr[source]#
 

 polars.first(column: Series) → Any

 

diff --git a/py-polars/html/reference/expressions/api/polars.fold.html b/py-polars/html/reference/expressions/api/polars.fold.html
index 61dfdf292dd9..11f913db2277 100644
--- a/py-polars/html/reference/expressions/api/polars.fold.html
+++ b/py-polars/html/reference/expressions/api/polars.fold.html
@@ -1628,7 +1628,7 @@
 
polars.fold#

 

 

-polars.fold(acc: IntoExpr, function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#

+polars.fold(acc: IntoExpr, function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#

 
Accumulate over multiple columns horizontally/ row wise with a left fold.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.from_epoch.html b/py-polars/html/reference/expressions/api/polars.from_epoch.html
index 65401d20b0cb..df6f552bc925 100644
--- a/py-polars/html/reference/expressions/api/polars.from_epoch.html
+++ b/py-polars/html/reference/expressions/api/polars.from_epoch.html
@@ -1628,7 +1628,7 @@
 
polars.from_epoch#

 

 

-polars.from_epoch(column: str | Expr, time_unit: EpochTimeUnit = 's') → Expr[source]#

+polars.from_epoch(column: str | Expr, time_unit: EpochTimeUnit = 's') → Expr[source]#
 

 polars.from_epoch(column: Series | Sequence[int], time_unit: EpochTimeUnit = 's') → Series

 
Utility function that parses an epoch timestamp (or Unix time) to Polars Date(time).

diff --git a/py-polars/html/reference/expressions/api/polars.groups.html b/py-polars/html/reference/expressions/api/polars.groups.html
index 9d063c07f1f2..02f6e679895c 100644
--- a/py-polars/html/reference/expressions/api/polars.groups.html
+++ b/py-polars/html/reference/expressions/api/polars.groups.html
@@ -1628,7 +1628,7 @@
 
polars.groups#

 

 

-polars.groups(column: str) → Expr[source]#

+polars.groups(column: str) → Expr[source]#
 
Syntactic sugar for pl.col(“foo”).agg_groups().

 

 
diff --git a/py-polars/html/reference/expressions/api/polars.head.html b/py-polars/html/reference/expressions/api/polars.head.html
index 70615b11d2e8..5363b4e3e79d 100644
--- a/py-polars/html/reference/expressions/api/polars.head.html
+++ b/py-polars/html/reference/expressions/api/polars.head.html
@@ -1628,7 +1628,7 @@
 
polars.head#

 

 

-polars.head(column: str, n: int = 10) → Expr[source]#

+polars.head(column: str, n: int = 10) → Expr[source]#
 

 polars.head(column: Series, n: int = 10) → Series

 
Get the first n rows.

diff --git a/py-polars/html/reference/expressions/api/polars.implode.html b/py-polars/html/reference/expressions/api/polars.implode.html
index e60c244d4965..48b7e786f7e4 100644
--- a/py-polars/html/reference/expressions/api/polars.implode.html
+++ b/py-polars/html/reference/expressions/api/polars.implode.html
@@ -1628,7 +1628,7 @@
 
polars.implode#

 

 

-polars.implode(name: str) → Expr[source]#

+polars.implode(name: str) → Expr[source]#
 
Aggregate all column values into a list.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.int_range.html b/py-polars/html/reference/expressions/api/polars.int_range.html
index 3019ee1e6409..2c229a2a80a4 100644
--- a/py-polars/html/reference/expressions/api/polars.int_range.html
+++ b/py-polars/html/reference/expressions/api/polars.int_range.html
@@ -1628,7 +1628,7 @@
 
polars.int_range#

 

 

-polars.int_range(start: int | IntoExpr, end: int | IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[False] = False) → Expr[source]#

+polars.int_range(start: int | IntoExpr, end: int | IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[False] = False) → Expr[source]#
 

 polars.int_range(start: int | IntoExpr, end: int | IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.int_ranges.html b/py-polars/html/reference/expressions/api/polars.int_ranges.html
index 7f320dfbc825..720013c3144d 100644
--- a/py-polars/html/reference/expressions/api/polars.int_ranges.html
+++ b/py-polars/html/reference/expressions/api/polars.int_ranges.html
@@ -1628,7 +1628,7 @@
 
polars.int_ranges#

 

 

-polars.int_ranges(start: IntoExpr, end: IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[False] = False) → Expr[source]#

+polars.int_ranges(start: IntoExpr, end: IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[False] = False) → Expr[source]#

 

 polars.int_ranges(start: IntoExpr, end: IntoExpr, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.last.html b/py-polars/html/reference/expressions/api/polars.last.html
index e27a01cdd2de..44443d238dbe 100644
--- a/py-polars/html/reference/expressions/api/polars.last.html
+++ b/py-polars/html/reference/expressions/api/polars.last.html
@@ -1628,7 +1628,7 @@
 
polars.last#

 

 

-polars.last(column: str) → Expr[source]#

+polars.last(column: str) → Expr[source]#

 

 polars.last(column: Series) → Any

 

diff --git a/py-polars/html/reference/expressions/api/polars.lit.html b/py-polars/html/reference/expressions/api/polars.lit.html
index b0e745b9c25d..e1322876dbbb 100644
--- a/py-polars/html/reference/expressions/api/polars.lit.html
+++ b/py-polars/html/reference/expressions/api/polars.lit.html
@@ -1628,7 +1628,7 @@
 
polars.lit#

 

 

-polars.lit(value: Any, dtype: PolarsDataType | None = None, *, allow_object: bool = False) → Expr[source]#

+polars.lit(value: Any, dtype: PolarsDataType | None = None, *, allow_object: bool = False) → Expr[source]#

 
Return an expression representing a literal value.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.map.html b/py-polars/html/reference/expressions/api/polars.map.html
index e7d4a2935fcd..bd2189ac43e8 100644
--- a/py-polars/html/reference/expressions/api/polars.map.html
+++ b/py-polars/html/reference/expressions/api/polars.map.html
@@ -1628,7 +1628,7 @@
 
polars.map#

 

 

-polars.map(exprs: Sequence[str] | Sequence[Expr], function: Callable[[Sequence[Series]], Series], return_dtype: PolarsDataType | None = None) → Expr[source]#

+polars.map(exprs: Sequence[str] | Sequence[Expr], function: Callable[[Sequence[Series]], Series], return_dtype: PolarsDataType | None = None) → Expr[source]#
 
Map a custom function over multiple columns/expressions.

 
Produces a single Series result.

 

diff --git a/py-polars/html/reference/expressions/api/polars.max.html b/py-polars/html/reference/expressions/api/polars.max.html
index fd1c8d32eb52..6a541c783afd 100644
--- a/py-polars/html/reference/expressions/api/polars.max.html
+++ b/py-polars/html/reference/expressions/api/polars.max.html
@@ -1628,7 +1628,7 @@
 
polars.max#

 

 

-polars.max(exprs: Series) → PythonLiteral | None[source]#

+polars.max(exprs: Series) → PythonLiteral | None[source]#
 

 polars.max(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr

 
Get the maximum value.

diff --git a/py-polars/html/reference/expressions/api/polars.mean.html b/py-polars/html/reference/expressions/api/polars.mean.html
index ac66d11d8cc0..19dd601d06ff 100644
--- a/py-polars/html/reference/expressions/api/polars.mean.html
+++ b/py-polars/html/reference/expressions/api/polars.mean.html
@@ -1628,7 +1628,7 @@
 
polars.mean#

 

 

-polars.mean(column: str) → Expr[source]#

+polars.mean(column: str) → Expr[source]#
 

 polars.mean(column: Series) → float

 
Get the mean value.

diff --git a/py-polars/html/reference/expressions/api/polars.median.html b/py-polars/html/reference/expressions/api/polars.median.html
index 4c9e65336a33..39cf47be2ab3 100644
--- a/py-polars/html/reference/expressions/api/polars.median.html
+++ b/py-polars/html/reference/expressions/api/polars.median.html
@@ -1628,7 +1628,7 @@
 
polars.median#

 

 

-polars.median(column: str) → Expr[source]#

+polars.median(column: str) → Expr[source]#
 

 polars.median(column: Series) → float | int

 
Get the median value.

diff --git a/py-polars/html/reference/expressions/api/polars.min.html b/py-polars/html/reference/expressions/api/polars.min.html
index 2c02434a72f8..67ee17681a63 100644
--- a/py-polars/html/reference/expressions/api/polars.min.html
+++ b/py-polars/html/reference/expressions/api/polars.min.html
@@ -1628,7 +1628,7 @@
 
polars.min#

 

 

-polars.min(exprs: Series) → PythonLiteral | None[source]#

+polars.min(exprs: Series) → PythonLiteral | None[source]#
 

 polars.min(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr

 
Get the minimum value.

diff --git a/py-polars/html/reference/expressions/api/polars.n_unique.html b/py-polars/html/reference/expressions/api/polars.n_unique.html
index 7518d30c9bbf..ab8e658d1685 100644
--- a/py-polars/html/reference/expressions/api/polars.n_unique.html
+++ b/py-polars/html/reference/expressions/api/polars.n_unique.html
@@ -1628,7 +1628,7 @@
 
polars.n_unique#

 

 

-polars.n_unique(column: str) → Expr[source]#

+polars.n_unique(column: str) → Expr[source]#
 

 polars.n_unique(column: Series) → int

 
Count unique values.

diff --git a/py-polars/html/reference/expressions/api/polars.quantile.html b/py-polars/html/reference/expressions/api/polars.quantile.html
index 440ca30948a6..3a5084df6a00 100644
--- a/py-polars/html/reference/expressions/api/polars.quantile.html
+++ b/py-polars/html/reference/expressions/api/polars.quantile.html
@@ -1628,7 +1628,7 @@
 
polars.quantile#

 

 

-polars.quantile(column: str, quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Expr[source]#

+polars.quantile(column: str, quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Expr[source]#
 
Syntactic sugar for pl.col(“foo”).quantile(..).

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.reduce.html b/py-polars/html/reference/expressions/api/polars.reduce.html
index 0e7b9dfe4346..3ffff47bf349 100644
--- a/py-polars/html/reference/expressions/api/polars.reduce.html
+++ b/py-polars/html/reference/expressions/api/polars.reduce.html
@@ -1628,7 +1628,7 @@
 
polars.reduce#

 

 

-polars.reduce(function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#

+polars.reduce(function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr) → Expr[source]#
 
Accumulate over multiple columns horizontally/ row wise with a left fold.

 

 
Parameters:

diff --git a/py-polars/html/reference/expressions/api/polars.rolling_corr.html b/py-polars/html/reference/expressions/api/polars.rolling_corr.html
index 080146655cca..2dac461042ae 100644
--- a/py-polars/html/reference/expressions/api/polars.rolling_corr.html
+++ b/py-polars/html/reference/expressions/api/polars.rolling_corr.html
@@ -1628,7 +1628,7 @@
 
polars.rolling_corr#

 

 

-polars.rolling_corr(a: str | Expr, b: str | Expr, *, window_size: int, min_periods: int | None = None, ddof: int = 1) → Expr[source]#

+polars.rolling_corr(a: str | Expr, b: str | Expr, *, window_size: int, min_periods: int | None = None, ddof: int = 1) → Expr[source]#
 
Compute the rolling correlation between two columns/ expressions.

 
The window at a given row includes the row itself and the
 window_size - 1 elements before it.

diff --git a/py-polars/html/reference/expressions/api/polars.rolling_cov.html b/py-polars/html/reference/expressions/api/polars.rolling_cov.html
index 747c47d89048..aa815a1b2cc9 100644
--- a/py-polars/html/reference/expressions/api/polars.rolling_cov.html
+++ b/py-polars/html/reference/expressions/api/polars.rolling_cov.html
@@ -1628,7 +1628,7 @@
 
polars.rolling_cov#

 

 

-polars.rolling_cov(a: str | Expr, b: str | Expr, *, window_size: int, min_periods: int | None = None, ddof: int = 1) → Expr[source]#

+polars.rolling_cov(a: str | Expr, b: str | Expr, *, window_size: int, min_periods: int | None = None, ddof: int = 1) → Expr[source]#
 
Compute the rolling covariance between two columns/ expressions.

 
The window at a given row includes the row itself and the
 window_size - 1 elements before it.

diff --git a/py-polars/html/reference/expressions/api/polars.select.html b/py-polars/html/reference/expressions/api/polars.select.html
index dfbe7deb3da8..0f6048fbb965 100644
--- a/py-polars/html/reference/expressions/api/polars.select.html
+++ b/py-polars/html/reference/expressions/api/polars.select.html
@@ -1628,7 +1628,7 @@
 
polars.select#

 

 

-polars.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#

+polars.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → DataFrame[source]#
 
Run polars expressions without a context.

 
This is syntactic sugar for running df.select on an empty DataFrame.

 

diff --git a/py-polars/html/reference/expressions/api/polars.sql_expr.html b/py-polars/html/reference/expressions/api/polars.sql_expr.html
index 6ecc182148e1..e4257a0f099d 100644
--- a/py-polars/html/reference/expressions/api/polars.sql_expr.html
+++ b/py-polars/html/reference/expressions/api/polars.sql_expr.html
@@ -1628,7 +1628,7 @@
 
polars.sql_expr#

 

 

-polars.sql_expr(sql: str) → Expr[source]#

+polars.sql_expr(sql: str) → Expr[source]#
 

 polars.sql_expr(sql: Sequence[str]) → list[Expr]

 
Parse one or more SQL expressions to polars expression(s).

diff --git a/py-polars/html/reference/expressions/api/polars.std.html b/py-polars/html/reference/expressions/api/polars.std.html
index a6a06eecec73..d024581c5ab5 100644
--- a/py-polars/html/reference/expressions/api/polars.std.html
+++ b/py-polars/html/reference/expressions/api/polars.std.html
@@ -1628,7 +1628,7 @@
 
polars.std#

 

 

-polars.std(column: str, ddof: int = 1) → Expr[source]#

+polars.std(column: str, ddof: int = 1) → Expr[source]#
 

 polars.std(column: Series, ddof: int = 1) → float | None

 
Get the standard deviation.

diff --git a/py-polars/html/reference/expressions/api/polars.sum.html b/py-polars/html/reference/expressions/api/polars.sum.html
index 1fd91bed4751..af0deea9c173 100644
--- a/py-polars/html/reference/expressions/api/polars.sum.html
+++ b/py-polars/html/reference/expressions/api/polars.sum.html
@@ -1628,7 +1628,7 @@
 
polars.sum#

 

 

-polars.sum(exprs: Series) → int | float[source]#

+polars.sum(exprs: Series) → int | float[source]#
 

 polars.sum(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr

 
Sum all values.

diff --git a/py-polars/html/reference/expressions/api/polars.tail.html b/py-polars/html/reference/expressions/api/polars.tail.html
index b3ffef13a3d2..a12d6894ccaa 100644
--- a/py-polars/html/reference/expressions/api/polars.tail.html
+++ b/py-polars/html/reference/expressions/api/polars.tail.html
@@ -1628,7 +1628,7 @@
 
polars.tail#

 

 

-polars.tail(column: str, n: int = 10) → Expr[source]#

+polars.tail(column: str, n: int = 10) → Expr[source]#
 

 polars.tail(column: Series, n: int = 10) → Series

 
Get the last n rows.

diff --git a/py-polars/html/reference/expressions/api/polars.time_range.html b/py-polars/html/reference/expressions/api/polars.time_range.html
index ffac24f94570..ac0d8770abcb 100644
--- a/py-polars/html/reference/expressions/api/polars.time_range.html
+++ b/py-polars/html/reference/expressions/api/polars.time_range.html
@@ -1628,7 +1628,7 @@
 
polars.time_range#

 

 

-polars.time_range(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[False] = False, name: str | None = None) → Expr[source]#

+polars.time_range(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[False] = False, name: str | None = None) → Expr[source]#
 

 polars.time_range(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[True], name: str | None = None) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.time_ranges.html b/py-polars/html/reference/expressions/api/polars.time_ranges.html
index 1b41ac6e9e97..90fab042582f 100644
--- a/py-polars/html/reference/expressions/api/polars.time_ranges.html
+++ b/py-polars/html/reference/expressions/api/polars.time_ranges.html
@@ -1628,7 +1628,7 @@
 
polars.time_ranges#

 

 

-polars.time_ranges(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[False] = False) → Expr[source]#

+polars.time_ranges(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[False] = False) → Expr[source]#

 

 polars.time_ranges(start: time | IntoExpr | None = None, end: time | IntoExpr | None = None, interval: str | timedelta = '1h', *, closed: ClosedInterval = 'both', eager: Literal[True]) → Series

 

diff --git a/py-polars/html/reference/expressions/api/polars.var.html b/py-polars/html/reference/expressions/api/polars.var.html
index fef04fb6ca34..e742d10d195d 100644
--- a/py-polars/html/reference/expressions/api/polars.var.html
+++ b/py-polars/html/reference/expressions/api/polars.var.html
@@ -1628,7 +1628,7 @@
 
polars.var#

 

 

-polars.var(column: str, ddof: int = 1) → Expr[source]#

+polars.var(column: str, ddof: int = 1) → Expr[source]#

 

 polars.var(column: Series, ddof: int = 1) → float | None

 
Get the variance.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.bottom_k.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
index 4350283027ff..4c89da7444dc 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.bottom_k#

 

 

-LazyFrame.bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#

+LazyFrame.bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#
 
Return the k smallest elements.

 
If ‘descending=True` the largest elements will be given.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.cache.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.cache.html
index 2fb59a49cec6..7bd73eb9749f 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.cache.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.cache.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.cache#

 

 

-LazyFrame.cache() → Self[source]#

+LazyFrame.cache() → Self[source]#
 
Cache the result once the execution of the physical plan hits this node.

 

 
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clear.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clear.html
index 00f7fd44421c..ac7e2585dc8f 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clear.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clear.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.clear#

 

 

-LazyFrame.clear(n: int = 0) → LazyFrame[source]#

+LazyFrame.clear(n: int = 0) → LazyFrame[source]#
 
Create an empty copy of the current LazyFrame, with zero to ‘n’ rows.

 
Returns a copy with an identical schema but no data.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clone.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clone.html
index 40594eae5739..2114e57b7c95 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clone.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.clone.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.clone#

 

 

-LazyFrame.clone() → Self[source]#

+LazyFrame.clone() → Self[source]#
 
Very cheap deepcopy/clone.

 

 
See also

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.collect.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.collect.html
index 50bcebdfe8b1..54f9eed62446 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.collect.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.collect.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.collect#

 

 

-LazyFrame.collect(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]#

+LazyFrame.collect(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]#
 
Collect into a DataFrame.

 
Note: use fetch() if you want to run your query on the first n rows
 only. This can be a huge time saver in debugging queries.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.columns.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.columns.html
index 127c80481cf6..357809bc2378 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.columns.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.columns.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.columns#

 

 

-property LazyFrame.columns: list[str][source]#

+property LazyFrame.columns: list[str][source]#
 
Get column names.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop.html
index 0feff1f995b1..4f64cd24fba0 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.drop#

 

 

-LazyFrame.drop(columns: str | Collection[str], *more_columns: str) → Self[source]#

+LazyFrame.drop(columns: str | Collection[str], *more_columns: str) → Self[source]#
 
Remove columns from the dataframe.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
index 76fd0ffa9f2d..603f45ec8b95 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.drop_nulls#

 

 

-LazyFrame.drop_nulls(subset: str | Collection[str] | None = None) → Self[source]#

+LazyFrame.drop_nulls(subset: str | Collection[str] | None = None) → Self[source]#
 
Drop all rows that contain null values.

 
Returns a new LazyFrame.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.dtypes.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.dtypes.html
index 511bc3017a5c..a445cd2a12cc 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.dtypes.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.dtypes.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.dtypes#

 

 

-property LazyFrame.dtypes: list[PolarsDataType][source]#

+property LazyFrame.dtypes: list[PolarsDataType][source]#
 
Get dtypes of columns in LazyFrame.

 

 
See also

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explain.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explain.html
index 3be0f9a5bd65..169924e0df42 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explain.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explain.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.explain#

 

 

-LazyFrame.explain(*, optimized: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str[source]#

+LazyFrame.explain(*, optimized: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str[source]#
 
Create a string representation of the query plan.

 
Different optimizations can be turned on or off.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explode.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explode.html
index 523a497c0ba7..2c136ed0b2a8 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explode.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.explode.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.explode#

 

 

-LazyFrame.explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → Self[source]#

+LazyFrame.explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → Self[source]#
 
Explode the dataframe to long format by exploding the given columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fetch.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fetch.html
index 0c252d1fe802..06cd6c27847a 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fetch.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fetch.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.fetch#

 

 

-LazyFrame.fetch(n_rows: int = 500, *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]#

+LazyFrame.fetch(n_rows: int = 500, *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]#
 
Collect a small number of rows for debugging purposes.

 
Fetch is like a collect() operation, but it overwrites the number of rows
 read by every scan operation. This is a utility that helps debug a query on a
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_nan.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
index 473d2139531e..34ddf9eb6f00 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.fill_nan#

 

 

-LazyFrame.fill_nan(value: int | float | Expr | None) → Self[source]#

+LazyFrame.fill_nan(value: int | float | Expr | None) → Self[source]#
 
Fill floating point NaN values.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_null.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_null.html
index d224c872c6e0..a592565e49c8 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_null.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.fill_null.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.fill_null#

 

 

-LazyFrame.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → Self[source]#

+LazyFrame.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → Self[source]#
 
Fill null values using the specified value or strategy.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.filter.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.filter.html
index 8191741f4c38..c0e7c3832b79 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.filter.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.filter.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.filter#

 

 

-LazyFrame.filter(predicate: IntoExpr) → Self[source]#

+LazyFrame.filter(predicate: IntoExpr) → Self[source]#
 
Filter the rows in the LazyFrame based on a predicate expression.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.first.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.first.html
index 8132f1951987..0394c64efad2 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.first.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.first.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.first#

 

 

-LazyFrame.first() → Self[source]#

+LazyFrame.first() → Self[source]#
 
Get the first row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.from_json.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.from_json.html
index edf683ec4421..9307c8688599 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.from_json.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.from_json.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.from_json#

 

 

-classmethod LazyFrame.from_json(json: str) → Self[source]#

+classmethod LazyFrame.from_json(json: str) → Self[source]#
 
Read a logical plan from a JSON string to construct a LazyFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby.html
index 28b2fddba1aa..9aedf8e5ec05 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.groupby#

 

 

-LazyFrame.groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → LazyGroupBy[source]#

+LazyFrame.groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → LazyGroupBy[source]#
 
Start a groupby operation.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
index 1bbc97223847..0fa4c3b9c37a 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.groupby_dynamic#

 

 

-LazyFrame.groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → LazyGroupBy[source]#

+LazyFrame.groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → LazyGroupBy[source]#
 
Group based on a time value (or index value of type Int32, Int64).

 
Time windows are calculated and rows are assigned to windows. Different from a
 normal groupby is that a row can be member of multiple groups. The time/index
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
index 9562598df634..500f04af8ab2 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.groupby_rolling#

 

 

-LazyFrame.groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → LazyGroupBy[source]#

+LazyFrame.groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → LazyGroupBy[source]#
 
Create rolling groups based on a time, Int32, or Int64 column.

 
Different from a dynamic_groupby the windows are now determined by the
 individual values and are not of constant intervals. For constant intervals
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.head.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.head.html
index 57340cd3b4f7..087cb1090cbf 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.head.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.head.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.head#

 

 

-LazyFrame.head(n: int = 5) → Self[source]#

+LazyFrame.head(n: int = 5) → Self[source]#
 
Get the first n rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.inspect.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.inspect.html
index d59a788c2472..e006e5e374ef 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.inspect.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.inspect.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.inspect#

 

 

-LazyFrame.inspect(fmt: str = '{}') → Self[source]#

+LazyFrame.inspect(fmt: str = '{}') → Self[source]#
 
Inspect a node in the computation graph.

 
Print the value that this node in the computation graph evaluates to and passes
 on the value.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.interpolate.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.interpolate.html
index 82a42a1361ae..ad2f909e49f0 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.interpolate.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.interpolate.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.interpolate#

 

 

-LazyFrame.interpolate() → Self[source]#

+LazyFrame.interpolate() → Self[source]#
 
Interpolate intermediate values. The interpolation method is linear.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join.html
index 90ba9324bddf..19456eaa70ef 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.join#

 

 

-LazyFrame.join(other: LazyFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m', allow_parallel: bool = True, force_parallel: bool = False) → Self[source]#

+LazyFrame.join(other: LazyFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m', allow_parallel: bool = True, force_parallel: bool = False) → Self[source]#
 
Add a join operation to the Logical Plan.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join_asof.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join_asof.html
index 2261494f6cc0..f34b95a69131 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join_asof.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.join_asof.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.join_asof#

 

 

-LazyFrame.join_asof(other: LazyFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → Self[source]#

+LazyFrame.join_asof(other: LazyFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → Self[source]#
 
Perform an asof join.

 
This is similar to a left-join except that we match on nearest key rather than
 equal keys.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.last.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.last.html
index 9966391442c7..1b638dce74ab 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.last.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.last.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.last#

 

 

-LazyFrame.last() → Self[source]#

+LazyFrame.last() → Self[source]#
 
Get the last row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.lazy.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.lazy.html
index b53997746097..5a377a2e8530 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.lazy.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.lazy.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.lazy#

 

 

-LazyFrame.lazy() → Self[source]#

+LazyFrame.lazy() → Self[source]#
 
Return lazy representation, i.e. itself.

 
Useful for writing code that expects either a DataFrame or
 LazyFrame.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.limit.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.limit.html
index cba6c31a159e..5242483944c3 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.limit.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.limit.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.limit#

 

 

-LazyFrame.limit(n: int = 5) → Self[source]#

+LazyFrame.limit(n: int = 5) → Self[source]#
 
Get the first n rows.

 
Alias for LazyFrame.head().

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.map.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.map.html
index 443efbe4a8e6..b1132754952b 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.map.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.map.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.map#

 

 

-LazyFrame.map(function: Callable[[DataFrame], DataFrame], *, predicate_pushdown: bool = True, projection_pushdown: bool = True, slice_pushdown: bool = True, no_optimizations: bool = False, schema: None | SchemaDict = None, validate_output_schema: bool = True, streamable: bool = False) → Self[source]#

+LazyFrame.map(function: Callable[[DataFrame], DataFrame], *, predicate_pushdown: bool = True, projection_pushdown: bool = True, slice_pushdown: bool = True, no_optimizations: bool = False, schema: None | SchemaDict = None, validate_output_schema: bool = True, streamable: bool = False) → Self[source]#
 
Apply a custom function.

 
It is important that the function returns a Polars DataFrame.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.max.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.max.html
index 63cb330d19aa..bf3308800bc1 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.max.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.max.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.max#

 

 

-LazyFrame.max() → Self[source]#

+LazyFrame.max() → Self[source]#
 
Aggregate the columns in the LazyFrame to their maximum value.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.mean.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.mean.html
index b9d7a914beb1..93deb1c70c14 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.mean.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.mean.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.mean#

 

 

-LazyFrame.mean() → Self[source]#

+LazyFrame.mean() → Self[source]#
 
Aggregate the columns in the LazyFrame to their mean value.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.median.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.median.html
index 4b727b1a8174..7dae69535a06 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.median.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.median.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.median#

 

 

-LazyFrame.median() → Self[source]#

+LazyFrame.median() → Self[source]#
 
Aggregate the columns in the LazyFrame to their median value.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.melt.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.melt.html
index 41ce5bdb42de..9f457b0422d7 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.melt.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.melt.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.melt#

 

 

-LazyFrame.melt(id_vars: str | list[str] | None = None, value_vars: str | list[str] | None = None, variable_name: str | None = None, value_name: str | None = None, *, streamable: bool = True) → Self[source]#

+LazyFrame.melt(id_vars: str | list[str] | None = None, value_vars: str | list[str] | None = None, variable_name: str | None = None, value_name: str | None = None, *, streamable: bool = True) → Self[source]#
 
Unpivot a DataFrame from wide to long format.

 
Optionally leaves identifiers set.

 
This function is useful to massage a DataFrame into a format where one or more
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
index 06ecd038f792..9399e7f5d8fb 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.merge_sorted#

 

 

-LazyFrame.merge_sorted(other: LazyFrame, key: str) → Self[source]#

+LazyFrame.merge_sorted(other: LazyFrame, key: str) → Self[source]#
 
Take two sorted DataFrames and merge them by the sorted key.

 
The output of this operation will also be sorted.
 It is the callers responsibility that the frames are sorted
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.min.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.min.html
index dc98b6c50251..93f552bff795 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.min.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.min.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.min#

 

 

-LazyFrame.min() → Self[source]#

+LazyFrame.min() → Self[source]#
 
Aggregate the columns in the LazyFrame to their minimum value.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.null_count.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.null_count.html
index 740cb920f787..d033dc74a41f 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.null_count.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.null_count.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.null_count#

 

 

-LazyFrame.null_count() → Self[source]#

+LazyFrame.null_count() → Self[source]#
 
Aggregate the columns in the LazyFrame as the sum of their null value count.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.pipe.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.pipe.html
index 82cd1d38b264..5dbc572e5c65 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.pipe.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.pipe.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.pipe#

 

 

-LazyFrame.pipe(function: Callable[Concatenate[LazyFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]#

+LazyFrame.pipe(function: Callable[Concatenate[LazyFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]#
 
Offers a structured way to apply a sequence of user-defined functions (UDFs).

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.profile.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.profile.html
index 42e205e11645..7a9cd6cae2e2 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.profile.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.profile.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.profile#

 

 

-LazyFrame.profile(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, show_plot: bool = False, truncate_nodes: int = 0, figsize: tuple[int, int] = (18, 8), streaming: bool = False) → tuple[DataFrame, DataFrame][source]#

+LazyFrame.profile(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, show_plot: bool = False, truncate_nodes: int = 0, figsize: tuple[int, int] = (18, 8), streaming: bool = False) → tuple[DataFrame, DataFrame][source]#
 
Profile a LazyFrame.

 
This will run the query and return a tuple
 containing the materialized DataFrame and a DataFrame that
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.quantile.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.quantile.html
index 27a900bd80ec..d18584097a37 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.quantile.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.quantile.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.quantile#

 

 

-LazyFrame.quantile(quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]#

+LazyFrame.quantile(quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]#
 
Aggregate the columns in the LazyFrame to their quantile value.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.read_json.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.read_json.html
index 95c253e21f95..7938698a073d 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.read_json.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.read_json.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.read_json#

 

 

-classmethod LazyFrame.read_json(file: str | Path | IOBase) → Self[source]#

+classmethod LazyFrame.read_json(file: str | Path | IOBase) → Self[source]#
 
Read a logical plan from a JSON file to construct a LazyFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.rename.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.rename.html
index 41803e68c031..28cc2399b968 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.rename.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.rename.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.rename#

 

 

-LazyFrame.rename(mapping: dict[str, str]) → Self[source]#

+LazyFrame.rename(mapping: dict[str, str]) → Self[source]#
 
Rename column names.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.reverse.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.reverse.html
index 84603d1ed0f0..64d4608e798f 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.reverse.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.reverse.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.reverse#

 

 

-LazyFrame.reverse() → Self[source]#

+LazyFrame.reverse() → Self[source]#
 
Reverse the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.schema.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.schema.html
index 9b984b3a09a4..bf789002348e 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.schema.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.schema.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.schema#

 

 

-property LazyFrame.schema: SchemaDict[source]#

+property LazyFrame.schema: SchemaDict[source]#
 
Get a dict[column name, DataType].

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.select.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.select.html
index bbf05a1cb3f5..e2333d1a6bf1 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.select.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.select.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.select#

 

 

-LazyFrame.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]#

+LazyFrame.select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]#
 
Select columns from this LazyFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.set_sorted.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
index d7ac3aee52ba..a4cac760ce73 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.set_sorted#

 

 

-LazyFrame.set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → Self[source]#

+LazyFrame.set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → Self[source]#
 
Indicate that one or multiple columns are sorted.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift.html
index 612bccc1632d..4baa6c9f0d51 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.shift#

 

 

-LazyFrame.shift(periods: int) → Self[source]#

+LazyFrame.shift(periods: int) → Self[source]#
 
Shift the values by a given period.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
index e3bcb3c60294..65a60bc1eddb 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.shift_and_fill#

 

 

-LazyFrame.shift_and_fill(fill_value: Expr | int | str | float, *, periods: int = 1) → Self[source]#

+LazyFrame.shift_and_fill(fill_value: Expr | int | str | float, *, periods: int = 1) → Self[source]#
 
Shift the values by a given period and fill the resulting null values.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.show_graph.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.show_graph.html
index 69f6aa88c3b8..f772e521718c 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.show_graph.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.show_graph.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.show_graph#

 

 

-LazyFrame.show_graph(*, optimized: bool = True, show: bool = True, output_path: str | Path | None = None, raw_output: bool = False, figsize: tuple[float, float] = (16.0, 12.0), type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str | None[source]#

+LazyFrame.show_graph(*, optimized: bool = True, show: bool = True, output_path: str | Path | None = None, raw_output: bool = False, figsize: tuple[float, float] = (16.0, 12.0), type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str | None[source]#
 
Show a plot of the query plan. Note that you should have graphviz installed.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.slice.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.slice.html
index 95b73a744cbc..1b5461a8bc36 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.slice.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.slice.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.slice#

 

 

-LazyFrame.slice(offset: int, length: int | None = None) → Self[source]#

+LazyFrame.slice(offset: int, length: int | None = None) → Self[source]#
 
Get a slice of this DataFrame.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sort.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sort.html
index 3585379a3aa3..949c622ae226 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sort.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sort.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.sort#

 

 

-LazyFrame.sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#

+LazyFrame.sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#
 
Sort the dataframe by the given columns.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.std.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.std.html
index 84aee89c5a7b..9e7d2c8614c0 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.std.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.std.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.std#

 

 

-LazyFrame.std(ddof: int = 1) → Self[source]#

+LazyFrame.std(ddof: int = 1) → Self[source]#
 
Aggregate the columns in the LazyFrame to their standard deviation value.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sum.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sum.html
index 46db0c79966e..b6b0c3e00078 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sum.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.sum.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.sum#

 

 

-LazyFrame.sum() → Self[source]#

+LazyFrame.sum() → Self[source]#
 
Aggregate the columns in the LazyFrame to their sum value.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.tail.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.tail.html
index c0e208a6e810..78704169d8ef 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.tail.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.tail.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.tail#

 

 

-LazyFrame.tail(n: int = 5) → Self[source]#

+LazyFrame.tail(n: int = 5) → Self[source]#
 
Get the last n rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.take_every.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.take_every.html
index 07905c774c3f..9babe5f0d06b 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.take_every.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.take_every.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.take_every#

 

 

-LazyFrame.take_every(n: int) → Self[source]#

+LazyFrame.take_every(n: int) → Self[source]#
 
Take every nth row in the LazyFrame and return as a new LazyFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.top_k.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.top_k.html
index e124d32c4ba8..a89d44c2d5b8 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.top_k.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.top_k.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.top_k#

 

 

-LazyFrame.top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#

+LazyFrame.top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]#
 
Return the k largest elements.

 
If ‘descending=True` the smallest elements will be given.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unique.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unique.html
index a6832995658c..92c4e8bd6f1b 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unique.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unique.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.unique#

 

 

-LazyFrame.unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → Self[source]#

+LazyFrame.unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → Self[source]#
 
Drop duplicate rows from this dataframe.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unnest.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unnest.html
index 4c65b91ac270..fb7898fd7bed 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unnest.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.unnest.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.unnest#

 

 

-LazyFrame.unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]#

+LazyFrame.unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]#
 
Decompose struct columns into separate columns for each of their fields.

 
The new columns will be inserted into the dataframe at the location of the
 struct column.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.update.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.update.html
index 11871f87b1e0..d3d525ea1b65 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.update.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.update.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.update#

 

 

-LazyFrame.update(other: LazyFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → Self[source]#

+LazyFrame.update(other: LazyFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → Self[source]#
 
Update the values in this LazyFrame with the non-null values in other.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.var.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.var.html
index b4efeebb80f4..8df7d9704cda 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.var.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.var.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.var#

 

 

-LazyFrame.var(ddof: int = 1) → Self[source]#

+LazyFrame.var(ddof: int = 1) → Self[source]#
 
Aggregate the columns in the LazyFrame to their variance value.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.width.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.width.html
index 9d0ac93b63b7..c5ea0c02367d 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.width.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.width.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.width#

 

 

-property LazyFrame.width: int[source]#

+property LazyFrame.width: int[source]#
 
Get the width of the LazyFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_columns.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_columns.html
index 9202d0edba8f..a8801ca47128 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_columns.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_columns.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.with_columns#

 

 

-LazyFrame.with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]#

+LazyFrame.with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]#
 
Add columns to this DataFrame.

 
Added columns will replace existing columns with the same name.

 

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_context.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_context.html
index 6a22c8ba4770..a4b171f633fb 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_context.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_context.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.with_context#

 

 

-LazyFrame.with_context(other: Self | list[Self]) → Self[source]#

+LazyFrame.with_context(other: Self | list[Self]) → Self[source]#
 
Add an external context to the computation graph.

 
This allows expressions to also access columns from DataFrames
 that are not part of this one.

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_row_count.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
index 99b46542bb42..0d588bdcda65 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.with_row_count#

 

 

-LazyFrame.with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]#

+LazyFrame.with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]#
 
Add a column at index 0 that counts the rows.

 

 
Parameters:

diff --git a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.write_json.html b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.write_json.html
index 94f089ddcd12..94049807ef1d 100644
--- a/py-polars/html/reference/lazyframe/api/polars.LazyFrame.write_json.html
+++ b/py-polars/html/reference/lazyframe/api/polars.LazyFrame.write_json.html
@@ -1628,7 +1628,7 @@
 
polars.LazyFrame.write_json#

 

 

-LazyFrame.write_json(file: None = None) → str[source]#

+LazyFrame.write_json(file: None = None) → str[source]#
 

 LazyFrame.write_json(file: IOBase | str | Path) → None

 
Write the logical plan of this LazyFrame to a file or string in JSON format.

diff --git a/py-polars/html/reference/lazyframe/index.html b/py-polars/html/reference/lazyframe/index.html
index 86f25afa3d1d..a7a6c723d91b 100644
--- a/py-polars/html/reference/lazyframe/index.html
+++ b/py-polars/html/reference/lazyframe/index.html
@@ -1619,7 +1619,7 @@ 
LazyFrame
 

-class polars.LazyFrame(data: FrameInitTypes | None = None, schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, orient: Orientation | None = None, infer_schema_length: int | None = 100, nan_to_null: bool = False)[source]

+class polars.LazyFrame(data: FrameInitTypes | None = None, schema: SchemaDefinition | None = None, *, schema_overrides: SchemaDict | None = None, orient: Orientation | None = None, infer_schema_length: int | None = 100, nan_to_null: bool = False)[source]
 
Representation of a Lazy computation graph/query against a DataFrame.

 
This allows for whole-query optimisation in addition to parallelism, and
 is the preferred (and highest-performance) mode of operation for polars.

@@ -1975,7 +1975,7 @@ 
LazyFrame
 

-approx_unique() → Self[source]

+approx_unique() → Self[source]
 
Approx count unique values.

 
This is done using the HyperLogLog++ algorithm for cardinality estimation.

 
Examples

@@ -2000,7 +2000,7 @@ 
LazyFrame
 

-bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]

+bottom_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]
 
Return the k smallest elements.

 
If ‘descending=True` the largest elements will be given.

 

@@ -2072,13 +2072,13 @@ 
LazyFrame
 

-cache() → Self[source]

+cache() → Self[source]
 
Cache the result once the execution of the physical plan hits this node.

 

 
 

 

-clear(n: int = 0) → LazyFrame[source]

+clear(n: int = 0) → LazyFrame[source]
 
Create an empty copy of the current LazyFrame, with zero to ‘n’ rows.

 
Returns a copy with an identical schema but no data.

 

@@ -2130,7 +2130,7 @@ 
LazyFrame
 

-clone() → Self[source]

+clone() → Self[source]
 
Very cheap deepcopy/clone.

 

 
See also

@@ -2155,7 +2155,7 @@ 
LazyFrame
 

-collect(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]

+collect(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]
 
Collect into a DataFrame.

 
Note: use fetch() if you want to run your query on the first n rows
 only. This can be a huge time saver in debugging queries.

@@ -2213,7 +2213,7 @@ 
LazyFrame
 

-property columns: list[str][source]

+property columns: list[str][source]
 
Get column names.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -2231,7 +2231,7 @@ 
LazyFrame
 

-drop(columns: str | Collection[str], *more_columns: str) → Self[source]

+drop(columns: str | Collection[str], *more_columns: str) → Self[source]
 
Remove columns from the dataframe.

 

 
Parameters:

@@ -2297,7 +2297,7 @@ 
LazyFrame
 

-drop_nulls(subset: str | Collection[str] | None = None) → Self[source]

+drop_nulls(subset: str | Collection[str] | None = None) → Self[source]
 
Drop all rows that contain null values.

 
Returns a new LazyFrame.

 

@@ -2371,7 +2371,7 @@ 
LazyFrame
 

-property dtypes: list[PolarsDataType][source]

+property dtypes: list[PolarsDataType][source]
 
Get dtypes of columns in LazyFrame.

 

 
See also

@@ -2396,7 +2396,7 @@ 
LazyFrame
 

-explain(*, optimized: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str[source]

+explain(*, optimized: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str[source]
 
Create a string representation of the query plan.

 
Different optimizations can be turned on or off.

 

@@ -2443,7 +2443,7 @@ 
LazyFrame
 

-explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → Self[source]

+explode(columns: str | Sequence[str] | Expr | Sequence[Expr], *more_columns: str | Expr) → Self[source]
 
Explode the dataframe to long format by exploding the given columns.

 

 
Parameters:

@@ -2485,7 +2485,7 @@ 
LazyFrame
 

-fetch(n_rows: int = 500, *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]

+fetch(n_rows: int = 500, *, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → DataFrame[source]
 
Collect a small number of rows for debugging purposes.

 
Fetch is like a collect() operation, but it overwrites the number of rows
 read by every scan operation. This is a utility that helps debug a query on a
@@ -2548,7 +2548,7 @@ 
LazyFrame
 

-fill_nan(value: int | float | Expr | None) → Self[source]

+fill_nan(value: int | float | Expr | None) → Self[source]
 
Fill floating point NaN values.

 

 
Parameters:

@@ -2588,7 +2588,7 @@ 
LazyFrame
 

-fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → Self[source]

+fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None, *, matches_supertype: bool = True) → Self[source]
 
Fill null values using the specified value or strategy.

 

 
Parameters:

@@ -2670,7 +2670,7 @@ 
LazyFrame
 

-filter(predicate: IntoExpr) → Self[source]

+filter(predicate: IntoExpr) → Self[source]
 
Filter the rows in the LazyFrame based on a predicate expression.

 

 
Parameters:

@@ -2732,7 +2732,7 @@ 
LazyFrame
 

-first() → Self[source]

+first() → Self[source]
 
Get the first row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -2756,7 +2756,7 @@ 
LazyFrame
 

-classmethod from_json(json: str) → Self[source]

+classmethod from_json(json: str) → Self[source]
 
Read a logical plan from a JSON string to construct a LazyFrame.

 

 
Parameters:

@@ -2776,7 +2776,7 @@ 
LazyFrame
 

-groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → LazyGroupBy[source]

+groupby(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, maintain_order: bool = False) → LazyGroupBy[source]
 
Start a groupby operation.

 

 
Parameters:

@@ -2868,7 +2868,7 @@ 
LazyFrame
 

-groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → LazyGroupBy[source]

+groupby_dynamic(index_column: IntoExpr, *, every: str | timedelta, period: str | timedelta | None = None, offset: str | timedelta | None = None, truncate: bool = True, include_boundaries: bool = False, closed: ClosedInterval = 'left', by: IntoExpr | Iterable[IntoExpr] | None = None, start_by: StartBy = 'window', check_sorted: bool = True) → LazyGroupBy[source]
 
Group based on a time value (or index value of type Int32, Int64).

 
Time windows are calculated and rows are assigned to windows. Different from a
 normal groupby is that a row can be member of multiple groups. The time/index
@@ -3183,7 +3183,7 @@ 
LazyFrame
 

-groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → LazyGroupBy[source]

+groupby_rolling(index_column: IntoExpr, *, period: str | timedelta, offset: str | timedelta | None = None, closed: ClosedInterval = 'right', by: IntoExpr | Iterable[IntoExpr] | None = None, check_sorted: bool = True) → LazyGroupBy[source]
 
Create rolling groups based on a time, Int32, or Int64 column.

 
Different from a dynamic_groupby the windows are now determined by the
 individual values and are not of constant intervals. For constant intervals
@@ -3315,7 +3315,7 @@ 
LazyFrame
 

-head(n: int = 5) → Self[source]

+head(n: int = 5) → Self[source]
 
Get the first n rows.

 

 
Parameters:

@@ -3365,7 +3365,7 @@ 
LazyFrame
 

-inspect(fmt: str = '{}') → Self[source]

+inspect(fmt: str = '{}') → Self[source]
 
Inspect a node in the computation graph.

 
Print the value that this node in the computation graph evaluates to and passes
 on the value.

@@ -3383,7 +3383,7 @@ 
LazyFrame
 

-interpolate() → Self[source]

+interpolate() → Self[source]
 
Interpolate intermediate values. The interpolation method is linear.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3411,7 +3411,7 @@ 
LazyFrame
 

-join(other: LazyFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m', allow_parallel: bool = True, force_parallel: bool = False) → Self[source]

+join(other: LazyFrame, on: str | Expr | Sequence[str | Expr] | None = None, how: JoinStrategy = 'inner', *, left_on: str | Expr | Sequence[str | Expr] | None = None, right_on: str | Expr | Sequence[str | Expr] | None = None, suffix: str = '_right', validate: JoinValidation = 'm:m', allow_parallel: bool = True, force_parallel: bool = False) → Self[source]
 
Add a join operation to the Logical Plan.

 

 
Parameters:

@@ -3549,7 +3549,7 @@ 
LazyFrame
 

-join_asof(other: LazyFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → Self[source]

+join_asof(other: LazyFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) → Self[source]
 
Perform an asof join.

 
This is similar to a left-join except that we match on nearest key rather than
 equal keys.

@@ -3671,7 +3671,7 @@ 
LazyFrame
 

-last() → Self[source]

+last() → Self[source]
 
Get the last row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3695,7 +3695,7 @@ 
LazyFrame
 

-lazy() → Self[source]

+lazy() → Self[source]
 
Return lazy representation, i.e. itself.

 
Useful for writing code that expects either a DataFrame or
 LazyFrame.

@@ -3722,7 +3722,7 @@ 
LazyFrame
 

-limit(n: int = 5) → Self[source]

+limit(n: int = 5) → Self[source]
 
Get the first n rows.

 
Alias for LazyFrame.head().

 

@@ -3773,7 +3773,7 @@ 
LazyFrame
 

-map(function: Callable[[DataFrame], DataFrame], *, predicate_pushdown: bool = True, projection_pushdown: bool = True, slice_pushdown: bool = True, no_optimizations: bool = False, schema: None | SchemaDict = None, validate_output_schema: bool = True, streamable: bool = False) → Self[source]

+map(function: Callable[[DataFrame], DataFrame], *, predicate_pushdown: bool = True, projection_pushdown: bool = True, slice_pushdown: bool = True, no_optimizations: bool = False, schema: None | SchemaDict = None, validate_output_schema: bool = True, streamable: bool = False) → Self[source]
 
Apply a custom function.

 
It is important that the function returns a Polars DataFrame.

 

@@ -3836,7 +3836,7 @@ 
LazyFrame
 

-max() → Self[source]

+max() → Self[source]
 
Aggregate the columns in the LazyFrame to their maximum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3860,7 +3860,7 @@ 
LazyFrame
 

-mean() → Self[source]

+mean() → Self[source]
 
Aggregate the columns in the LazyFrame to their mean value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3884,7 +3884,7 @@ 
LazyFrame
 

-median() → Self[source]

+median() → Self[source]
 
Aggregate the columns in the LazyFrame to their median value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3908,7 +3908,7 @@ 
LazyFrame
 

-melt(id_vars: str | list[str] | None = None, value_vars: str | list[str] | None = None, variable_name: str | None = None, value_name: str | None = None, *, streamable: bool = True) → Self[source]

+melt(id_vars: str | list[str] | None = None, value_vars: str | list[str] | None = None, variable_name: str | None = None, value_name: str | None = None, *, streamable: bool = True) → Self[source]
 
Unpivot a DataFrame from wide to long format.

 
Optionally leaves identifiers set.

 
This function is useful to massage a DataFrame into a format where one or more
@@ -3962,7 +3962,7 @@ 
LazyFrame
 

-merge_sorted(other: LazyFrame, key: str) → Self[source]

+merge_sorted(other: LazyFrame, key: str) → Self[source]
 
Take two sorted DataFrames and merge them by the sorted key.

 
The output of this operation will also be sorted.
 It is the callers responsibility that the frames are sorted
@@ -4029,7 +4029,7 @@ 
LazyFrame
 

-min() → Self[source]

+min() → Self[source]
 
Aggregate the columns in the LazyFrame to their minimum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4053,7 +4053,7 @@ 
LazyFrame
 

-null_count() → Self[source]

+null_count() → Self[source]
 
Aggregate the columns in the LazyFrame as the sum of their null value count.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4078,7 +4078,7 @@ 
LazyFrame
 

-pipe(function: Callable[Concatenate[LazyFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]

+pipe(function: Callable[Concatenate[LazyFrame, P], T], *args: P.args, **kwargs: P.kwargs) → T[source]
 
Offers a structured way to apply a sequence of user-defined functions (UDFs).

 

 
Parameters:

@@ -4149,7 +4149,7 @@ 
LazyFrame
 

-profile(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, show_plot: bool = False, truncate_nodes: int = 0, figsize: tuple[int, int] = (18, 8), streaming: bool = False) → tuple[DataFrame, DataFrame][source]

+profile(*, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, show_plot: bool = False, truncate_nodes: int = 0, figsize: tuple[int, int] = (18, 8), streaming: bool = False) → tuple[DataFrame, DataFrame][source]
 
Profile a LazyFrame.

 
This will run the query and return a tuple
 containing the materialized DataFrame and a DataFrame that
@@ -4223,7 +4223,7 @@ 
LazyFrame
 

-quantile(quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]

+quantile(quantile: float | Expr, interpolation: RollingInterpolationMethod = 'nearest') → Self[source]
 
Aggregate the columns in the LazyFrame to their quantile value.

 

 
Parameters:

@@ -4257,7 +4257,7 @@ 
LazyFrame
 

-classmethod read_json(file: str | Path | IOBase) → Self[source]

+classmethod read_json(file: str | Path | IOBase) → Self[source]
 
Read a logical plan from a JSON file to construct a LazyFrame.

 

 
Parameters:

@@ -4277,7 +4277,7 @@ 
LazyFrame
 

-rename(mapping: dict[str, str]) → Self[source]

+rename(mapping: dict[str, str]) → Self[source]
 
Rename column names.

 

 
Parameters:

@@ -4315,7 +4315,7 @@ 
LazyFrame
 

-reverse() → Self[source]

+reverse() → Self[source]
 
Reverse the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4341,7 +4341,7 @@ 
LazyFrame
 

-property schema: SchemaDict[source]

+property schema: SchemaDict[source]
 
Get a dict[column name, DataType].

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4359,7 +4359,7 @@ 
LazyFrame
 

-select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]

+select(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]
 
Select columns from this LazyFrame.

 

 
Parameters:

@@ -4464,7 +4464,7 @@ 
LazyFrame
 

-set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → Self[source]

+set_sorted(column: str | Iterable[str], *more_columns: str, descending: bool = False) → Self[source]
 
Indicate that one or multiple columns are sorted.

 

 
Parameters:

@@ -4482,7 +4482,7 @@ 
LazyFrame
 

-shift(periods: int) → Self[source]

+shift(periods: int) → Self[source]
 
Shift the values by a given period.

 

 
Parameters:

@@ -4527,7 +4527,7 @@ 
LazyFrame
 

-shift_and_fill(fill_value: Expr | int | str | float, *, periods: int = 1) → Self[source]

+shift_and_fill(fill_value: Expr | int | str | float, *, periods: int = 1) → Self[source]
 
Shift the values by a given period and fill the resulting null values.

 

 
Parameters:

@@ -4574,7 +4574,7 @@ 
LazyFrame
 

-show_graph(*, optimized: bool = True, show: bool = True, output_path: str | Path | None = None, raw_output: bool = False, figsize: tuple[float, float] = (16.0, 12.0), type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str | None[source]

+show_graph(*, optimized: bool = True, show: bool = True, output_path: str | Path | None = None, raw_output: bool = False, figsize: tuple[float, float] = (16.0, 12.0), type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, slice_pushdown: bool = True, comm_subplan_elim: bool = True, comm_subexpr_elim: bool = True, streaming: bool = False) → str | None[source]
 
Show a plot of the query plan. Note that you should have graphviz installed.

 

 
Parameters:

@@ -4625,7 +4625,7 @@ 
LazyFrame
 

-sink_ipc(path: str | Path, *, compression: str | None = 'zstd', maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]

+sink_ipc(path: str | Path, *, compression: str | None = 'zstd', maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]
 
Persists a LazyFrame at the provided path.

 
This allows streaming results that are larger than RAM to be written to disk.

 

@@ -4668,7 +4668,7 @@ 
LazyFrame
 

-sink_parquet(path: str | Path, *, compression: str = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, data_pagesize_limit: int | None = None, maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]

+sink_parquet(path: str | Path, *, compression: str = 'zstd', compression_level: int | None = None, statistics: bool = False, row_group_size: int | None = None, data_pagesize_limit: int | None = None, maintain_order: bool = True, type_coercion: bool = True, predicate_pushdown: bool = True, projection_pushdown: bool = True, simplify_expression: bool = True, no_optimization: bool = False, slice_pushdown: bool = True) → DataFrame[source]
 
Persists a LazyFrame at the provided path.

 
This allows streaming results that are larger than RAM to be written to disk.

 

@@ -4732,7 +4732,7 @@ 
LazyFrame
 

-slice(offset: int, length: int | None = None) → Self[source]

+slice(offset: int, length: int | None = None) → Self[source]
 
Get a slice of this DataFrame.

 

 
Parameters:

@@ -4769,7 +4769,7 @@ 
LazyFrame
 

-sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]

+sort(by: IntoExpr | Iterable[IntoExpr], *more_by: IntoExpr, descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]
 
Sort the dataframe by the given columns.

 

 
Parameters:

@@ -4859,7 +4859,7 @@ 
LazyFrame
 

-std(ddof: int = 1) → Self[source]

+std(ddof: int = 1) → Self[source]
 
Aggregate the columns in the LazyFrame to their standard deviation value.

 

 
Parameters:

@@ -4902,7 +4902,7 @@ 
LazyFrame
 

-sum() → Self[source]

+sum() → Self[source]
 
Aggregate the columns in the LazyFrame to their sum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4926,7 +4926,7 @@ 
LazyFrame
 

-tail(n: int = 5) → Self[source]

+tail(n: int = 5) → Self[source]
 
Get the last n rows.

 

 
Parameters:

@@ -4972,7 +4972,7 @@ 
LazyFrame
 

-take_every(n: int) → Self[source]

+take_every(n: int) → Self[source]
 
Take every nth row in the LazyFrame and return as a new LazyFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4997,7 +4997,7 @@ 
LazyFrame
 

-top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]

+top_k(k: int, *, by: IntoExpr | Iterable[IntoExpr], descending: bool | Sequence[bool] = False, nulls_last: bool = False, maintain_order: bool = False) → Self[source]
 
Return the k largest elements.

 
If ‘descending=True` the smallest elements will be given.

 

@@ -5069,7 +5069,7 @@ 
LazyFrame
 

-unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → Self[source]

+unique(subset: str | Sequence[str] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False) → Self[source]
 
Drop duplicate rows from this dataframe.

 

 
Parameters:

@@ -5153,7 +5153,7 @@ 
LazyFrame
 

-unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]

+unnest(columns: str | Sequence[str], *more_columns: str) → Self[source]
 
Decompose struct columns into separate columns for each of their fields.

 
The new columns will be inserted into the dataframe at the location of the
 struct column.

@@ -5204,7 +5204,7 @@ 
LazyFrame
 

-update(other: LazyFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → Self[source]

+update(other: LazyFrame, on: str | Sequence[str] | None = None, how: Literal['left', 'inner'] = 'left') → Self[source]
 
Update the values in this LazyFrame with the non-null values in other.

 

 
Parameters:

@@ -5281,7 +5281,7 @@ 
LazyFrame
 

-var(ddof: int = 1) → Self[source]

+var(ddof: int = 1) → Self[source]
 
Aggregate the columns in the LazyFrame to their variance value.

 

 
Parameters:

@@ -5324,7 +5324,7 @@ 
LazyFrame
 

-property width: int[source]

+property width: int[source]
 
Get the width of the LazyFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -5341,7 +5341,7 @@ 
LazyFrame
 

-with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]

+with_columns(*exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr) → Self[source]
 
Add columns to this DataFrame.

 
Added columns will replace existing columns with the same name.

 

@@ -5486,7 +5486,7 @@ 
LazyFrame
 

-with_context(other: Self | list[Self]) → Self[source]

+with_context(other: Self | list[Self]) → Self[source]
 
Add an external context to the computation graph.

 
This allows expressions to also access columns from DataFrames
 that are not part of this one.

@@ -5542,7 +5542,7 @@ 
LazyFrame
 

-with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]

+with_row_count(name: str = 'row_nr', offset: int = 0) → Self[source]
 
Add a column at index 0 that counts the rows.

 

 
Parameters:

@@ -5583,7 +5583,7 @@ 
LazyFrame
 

-write_json(file: None = None) → str[source]

+write_json(file: None = None) → str[source]
 

 write_json(file: IOBase | str | Path) → None

 
Write the logical plan of this LazyFrame to a file or string in JSON format.

diff --git a/py-polars/html/reference/series/api/polars.Series.abs.html b/py-polars/html/reference/series/api/polars.Series.abs.html
index 876fe83aa2f4..50804fafc87e 100644
--- a/py-polars/html/reference/series/api/polars.Series.abs.html
+++ b/py-polars/html/reference/series/api/polars.Series.abs.html
@@ -1628,7 +1628,7 @@
 
polars.Series.abs#

 

 

-Series.abs() → Series[source]#

+Series.abs() → Series[source]#
 
Compute absolute values.

 
Same as abs(series).

 

diff --git a/py-polars/html/reference/series/api/polars.Series.alias.html b/py-polars/html/reference/series/api/polars.Series.alias.html
index c58954c7257b..b56536018f20 100644
--- a/py-polars/html/reference/series/api/polars.Series.alias.html
+++ b/py-polars/html/reference/series/api/polars.Series.alias.html
@@ -1628,7 +1628,7 @@
 
polars.Series.alias#

 

 

-Series.alias(name: str) → Series[source]#

+Series.alias(name: str) → Series[source]#
 
Rename the series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.all.html b/py-polars/html/reference/series/api/polars.Series.all.html
index 2c93562be4e6..86bcfd025255 100644
--- a/py-polars/html/reference/series/api/polars.Series.all.html
+++ b/py-polars/html/reference/series/api/polars.Series.all.html
@@ -1628,7 +1628,7 @@
 
polars.Series.all#

 

 

-Series.all(drop_nulls: bool = True) → bool | None[source]#

+Series.all(drop_nulls: bool = True) → bool | None[source]#
 
Check if all boolean values in the column are True.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.any.html b/py-polars/html/reference/series/api/polars.Series.any.html
index d0e0d1751040..e1f803740c01 100644
--- a/py-polars/html/reference/series/api/polars.Series.any.html
+++ b/py-polars/html/reference/series/api/polars.Series.any.html
@@ -1628,7 +1628,7 @@
 
polars.Series.any#

 

 

-Series.any(drop_nulls: bool = True) → bool | None[source]#

+Series.any(drop_nulls: bool = True) → bool | None[source]#
 
Check if any boolean value in the column is True.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.append.html b/py-polars/html/reference/series/api/polars.Series.append.html
index 3e36fc91e840..36e1f7d95d26 100644
--- a/py-polars/html/reference/series/api/polars.Series.append.html
+++ b/py-polars/html/reference/series/api/polars.Series.append.html
@@ -1628,7 +1628,7 @@
 
polars.Series.append#

 

 

-Series.append(other: Series, *, append_chunks: bool | None = None) → Self[source]#

+Series.append(other: Series, *, append_chunks: bool | None = None) → Self[source]#
 
Append a Series to this one.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.apply.html b/py-polars/html/reference/series/api/polars.Series.apply.html
index 103d8a8f75b4..c315167c2946 100644
--- a/py-polars/html/reference/series/api/polars.Series.apply.html
+++ b/py-polars/html/reference/series/api/polars.Series.apply.html
@@ -1628,7 +1628,7 @@
 
polars.Series.apply#

 

 

-Series.apply(function: Callable[[Any], Any], return_dtype: PolarsDataType | None = None, *, skip_nulls: bool = True) → Self[source]#

+Series.apply(function: Callable[[Any], Any], return_dtype: PolarsDataType | None = None, *, skip_nulls: bool = True) → Self[source]#
 
Apply a custom/user-defined function (UDF) over elements in this Series.

 

 
Warning

diff --git a/py-polars/html/reference/series/api/polars.Series.arccos.html b/py-polars/html/reference/series/api/polars.Series.arccos.html
index c0cd04cfd57c..3b821dc7ce3b 100644
--- a/py-polars/html/reference/series/api/polars.Series.arccos.html
+++ b/py-polars/html/reference/series/api/polars.Series.arccos.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arccos#

 

 

-Series.arccos() → Series[source]#

+Series.arccos() → Series[source]#
 
Compute the element-wise value for the inverse cosine.

 
Examples

 
>>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.arccosh.html b/py-polars/html/reference/series/api/polars.Series.arccosh.html
index 280c9a46bdc9..2fbb27d9e90f 100644
--- a/py-polars/html/reference/series/api/polars.Series.arccosh.html
+++ b/py-polars/html/reference/series/api/polars.Series.arccosh.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arccosh#

 

 

-Series.arccosh() → Series[source]#

+Series.arccosh() → Series[source]#
 
Compute the element-wise value for the inverse hyperbolic cosine.

 
Examples

 
>>> s = pl.Series("a", [5.0, 1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.arcsin.html b/py-polars/html/reference/series/api/polars.Series.arcsin.html
index 286e0222fe91..68dfb1b65294 100644
--- a/py-polars/html/reference/series/api/polars.Series.arcsin.html
+++ b/py-polars/html/reference/series/api/polars.Series.arcsin.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arcsin#

 

 

-Series.arcsin() → Series[source]#

+Series.arcsin() → Series[source]#
 
Compute the element-wise value for the inverse sine.

 
Examples

 
>>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.arcsinh.html b/py-polars/html/reference/series/api/polars.Series.arcsinh.html
index 6a3996ea8712..c88045b27f85 100644
--- a/py-polars/html/reference/series/api/polars.Series.arcsinh.html
+++ b/py-polars/html/reference/series/api/polars.Series.arcsinh.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arcsinh#

 

 

-Series.arcsinh() → Series[source]#

+Series.arcsinh() → Series[source]#
 
Compute the element-wise value for the inverse hyperbolic sine.

 
Examples

 
>>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.arctan.html b/py-polars/html/reference/series/api/polars.Series.arctan.html
index b21619adb201..06a243735067 100644
--- a/py-polars/html/reference/series/api/polars.Series.arctan.html
+++ b/py-polars/html/reference/series/api/polars.Series.arctan.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arctan#

 

 

-Series.arctan() → Series[source]#

+Series.arctan() → Series[source]#
 
Compute the element-wise value for the inverse tangent.

 
Examples

 
>>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.arctanh.html b/py-polars/html/reference/series/api/polars.Series.arctanh.html
index 2489b8d50b87..00cc970dd1f0 100644
--- a/py-polars/html/reference/series/api/polars.Series.arctanh.html
+++ b/py-polars/html/reference/series/api/polars.Series.arctanh.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arctanh#

 

 

-Series.arctanh() → Series[source]#

+Series.arctanh() → Series[source]#
 
Compute the element-wise value for the inverse hyperbolic tangent.

 
Examples

 
>>> s = pl.Series("a", [2.0, 1.0, 0.5, 0.0, -0.5, -1.0, -1.1])
diff --git a/py-polars/html/reference/series/api/polars.Series.arg_max.html b/py-polars/html/reference/series/api/polars.Series.arg_max.html
index 4fc4c595c63a..0f609a283f98 100644
--- a/py-polars/html/reference/series/api/polars.Series.arg_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.arg_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arg_max#

 

 

-Series.arg_max() → int | None[source]#

+Series.arg_max() → int | None[source]#
 
Get the index of the maximal value.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.arg_min.html b/py-polars/html/reference/series/api/polars.Series.arg_min.html
index a6ad1d14f372..f54907910c8c 100644
--- a/py-polars/html/reference/series/api/polars.Series.arg_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.arg_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arg_min#

 

 

-Series.arg_min() → int | None[source]#

+Series.arg_min() → int | None[source]#
 
Get the index of the minimal value.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.arg_sort.html b/py-polars/html/reference/series/api/polars.Series.arg_sort.html
index 031777d2c5b0..35e5c0d0f01a 100644
--- a/py-polars/html/reference/series/api/polars.Series.arg_sort.html
+++ b/py-polars/html/reference/series/api/polars.Series.arg_sort.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arg_sort#

 

 

-Series.arg_sort(*, descending: bool = False, nulls_last: bool = False) → Series[source]#

+Series.arg_sort(*, descending: bool = False, nulls_last: bool = False) → Series[source]#
 
Get the index values that would sort this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.arg_true.html b/py-polars/html/reference/series/api/polars.Series.arg_true.html
index 3831d202d7f6..9d938bf648c8 100644
--- a/py-polars/html/reference/series/api/polars.Series.arg_true.html
+++ b/py-polars/html/reference/series/api/polars.Series.arg_true.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arg_true#

 

 

-Series.arg_true() → Series[source]#

+Series.arg_true() → Series[source]#
 
Get index values where Boolean Series evaluate True.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.arg_unique.html b/py-polars/html/reference/series/api/polars.Series.arg_unique.html
index 568ebeb322fa..438b9a45adf7 100644
--- a/py-polars/html/reference/series/api/polars.Series.arg_unique.html
+++ b/py-polars/html/reference/series/api/polars.Series.arg_unique.html
@@ -1628,7 +1628,7 @@
 
polars.Series.arg_unique#

 

 

-Series.arg_unique() → Series[source]#

+Series.arg_unique() → Series[source]#
 
Get unique index as Series.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.bottom_k.html b/py-polars/html/reference/series/api/polars.Series.bottom_k.html
index 9bf0e1f6f8e6..e801af239ba8 100644
--- a/py-polars/html/reference/series/api/polars.Series.bottom_k.html
+++ b/py-polars/html/reference/series/api/polars.Series.bottom_k.html
@@ -1629,7 +1629,7 @@
 
polars.Series.bottom_k#

 

 

-Series.bottom_k(k: int = 5) → Series[source]#

+Series.bottom_k(k: int = 5) → Series[source]#
 
Return the k smallest elements.

 
This has time complexity:

 

diff --git a/py-polars/html/reference/series/api/polars.Series.cast.html b/py-polars/html/reference/series/api/polars.Series.cast.html
index eb6a183f8a29..3327c0d5fd41 100644
--- a/py-polars/html/reference/series/api/polars.Series.cast.html
+++ b/py-polars/html/reference/series/api/polars.Series.cast.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cast#

 

 

-Series.cast(dtype: PolarsDataType | type[int] | type[float] | type[str] | type[bool], *, strict: bool = True) → Self[source]#

+Series.cast(dtype: PolarsDataType | type[int] | type[float] | type[str] | type[bool], *, strict: bool = True) → Self[source]#
 
Cast between data types.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cbrt.html b/py-polars/html/reference/series/api/polars.Series.cbrt.html
index 62084c735138..758857ebc36f 100644
--- a/py-polars/html/reference/series/api/polars.Series.cbrt.html
+++ b/py-polars/html/reference/series/api/polars.Series.cbrt.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cbrt#

 

 

-Series.cbrt() → Series[source]#

+Series.cbrt() → Series[source]#
 
Compute the cube root of the elements.

 
Optimization for

 
>>> pl.Series([1, 2]) ** (1.0 / 3)
diff --git a/py-polars/html/reference/series/api/polars.Series.ceil.html b/py-polars/html/reference/series/api/polars.Series.ceil.html
index 349b8fcfbbf9..cb321ab6a4fb 100644
--- a/py-polars/html/reference/series/api/polars.Series.ceil.html
+++ b/py-polars/html/reference/series/api/polars.Series.ceil.html
@@ -1628,7 +1628,7 @@
 
polars.Series.ceil#

 

 

-Series.ceil() → Series[source]#

+Series.ceil() → Series[source]#
 
Rounds up to the nearest integer value.

 
Only works on floating point Series.

 
Examples

diff --git a/py-polars/html/reference/series/api/polars.Series.chunk_lengths.html b/py-polars/html/reference/series/api/polars.Series.chunk_lengths.html
index 4a93563b1eb3..f4ad6f89c7fe 100644
--- a/py-polars/html/reference/series/api/polars.Series.chunk_lengths.html
+++ b/py-polars/html/reference/series/api/polars.Series.chunk_lengths.html
@@ -1628,7 +1628,7 @@
 
polars.Series.chunk_lengths#

 

 

-Series.chunk_lengths() → list[int][source]#

+Series.chunk_lengths() → list[int][source]#
 
Get the length of each individual chunk.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.clear.html b/py-polars/html/reference/series/api/polars.Series.clear.html
index 23c6e79fb935..4b4ed85c115e 100644
--- a/py-polars/html/reference/series/api/polars.Series.clear.html
+++ b/py-polars/html/reference/series/api/polars.Series.clear.html
@@ -1628,7 +1628,7 @@
 
polars.Series.clear#

 

 

-Series.clear(n: int = 0) → Series[source]#

+Series.clear(n: int = 0) → Series[source]#
 
Create an empty copy of the current Series, with zero to ‘n’ elements.

 
The copy has an identical name/dtype, but no data.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.clip.html b/py-polars/html/reference/series/api/polars.Series.clip.html
index 2c8064b1d231..da3af8689055 100644
--- a/py-polars/html/reference/series/api/polars.Series.clip.html
+++ b/py-polars/html/reference/series/api/polars.Series.clip.html
@@ -1628,7 +1628,7 @@
 
polars.Series.clip#

 

 

-Series.clip(lower_bound: int | float, upper_bound: int | float) → Series[source]#

+Series.clip(lower_bound: int | float, upper_bound: int | float) → Series[source]#
 
Clip (limit) the values in an array to a min and max boundary.

 
Only works for numerical types.

 
If you want to clip other dtypes, consider writing a “when, then, otherwise”
diff --git a/py-polars/html/reference/series/api/polars.Series.clip_max.html b/py-polars/html/reference/series/api/polars.Series.clip_max.html
index 58e5c1ef481a..704333136431 100644
--- a/py-polars/html/reference/series/api/polars.Series.clip_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.clip_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.clip_max#

 

 

-Series.clip_max(upper_bound: int | float) → Series[source]#

+Series.clip_max(upper_bound: int | float) → Series[source]#
 
Clip (limit) the values in an array to a max boundary.

 
Only works for numerical types.

 
If you want to clip other dtypes, consider writing a “when, then, otherwise”
diff --git a/py-polars/html/reference/series/api/polars.Series.clip_min.html b/py-polars/html/reference/series/api/polars.Series.clip_min.html
index b1a908520d05..367a82be36ce 100644
--- a/py-polars/html/reference/series/api/polars.Series.clip_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.clip_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.clip_min#

 

 

-Series.clip_min(lower_bound: int | float) → Series[source]#

+Series.clip_min(lower_bound: int | float) → Series[source]#
 
Clip (limit) the values in an array to a min boundary.

 
Only works for numerical types.

 
If you want to clip other dtypes, consider writing a “when, then, otherwise”
diff --git a/py-polars/html/reference/series/api/polars.Series.clone.html b/py-polars/html/reference/series/api/polars.Series.clone.html
index cf9bed43005d..f49a8ff03dc1 100644
--- a/py-polars/html/reference/series/api/polars.Series.clone.html
+++ b/py-polars/html/reference/series/api/polars.Series.clone.html
@@ -1628,7 +1628,7 @@
 
polars.Series.clone#

 

 

-Series.clone() → Self[source]#

+Series.clone() → Self[source]#
 
Very cheap deepcopy/clone.

 

 
See also

diff --git a/py-polars/html/reference/series/api/polars.Series.cos.html b/py-polars/html/reference/series/api/polars.Series.cos.html
index 3adfc808c251..df2b8cecdd33 100644
--- a/py-polars/html/reference/series/api/polars.Series.cos.html
+++ b/py-polars/html/reference/series/api/polars.Series.cos.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cos#

 

 

-Series.cos() → Series[source]#

+Series.cos() → Series[source]#
 
Compute the element-wise value for the cosine.

 
Examples

 
>>> import math
diff --git a/py-polars/html/reference/series/api/polars.Series.cosh.html b/py-polars/html/reference/series/api/polars.Series.cosh.html
index 98dbea572af6..7aae39b8079c 100644
--- a/py-polars/html/reference/series/api/polars.Series.cosh.html
+++ b/py-polars/html/reference/series/api/polars.Series.cosh.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cosh#

 

 

-Series.cosh() → Series[source]#

+Series.cosh() → Series[source]#
 
Compute the element-wise value for the hyperbolic cosine.

 
Examples

 
>>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.cummax.html b/py-polars/html/reference/series/api/polars.Series.cummax.html
index 457969883d14..6b521a1e03c1 100644
--- a/py-polars/html/reference/series/api/polars.Series.cummax.html
+++ b/py-polars/html/reference/series/api/polars.Series.cummax.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cummax#

 

 

-Series.cummax(*, reverse: bool = False) → Series[source]#

+Series.cummax(*, reverse: bool = False) → Series[source]#
 
Get an array with the cumulative max computed at every element.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cummin.html b/py-polars/html/reference/series/api/polars.Series.cummin.html
index 7c324d63b8f6..4529c15fae99 100644
--- a/py-polars/html/reference/series/api/polars.Series.cummin.html
+++ b/py-polars/html/reference/series/api/polars.Series.cummin.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cummin#

 

 

-Series.cummin(*, reverse: bool = False) → Series[source]#

+Series.cummin(*, reverse: bool = False) → Series[source]#
 
Get an array with the cumulative min computed at every element.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cumprod.html b/py-polars/html/reference/series/api/polars.Series.cumprod.html
index 7b06d0c1200b..dae091b97fcc 100644
--- a/py-polars/html/reference/series/api/polars.Series.cumprod.html
+++ b/py-polars/html/reference/series/api/polars.Series.cumprod.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cumprod#

 

 

-Series.cumprod(*, reverse: bool = False) → Series[source]#

+Series.cumprod(*, reverse: bool = False) → Series[source]#
 
Get an array with the cumulative product computed at every element.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cumsum.html b/py-polars/html/reference/series/api/polars.Series.cumsum.html
index 224ac5ab4e35..abc7abc33ba5 100644
--- a/py-polars/html/reference/series/api/polars.Series.cumsum.html
+++ b/py-polars/html/reference/series/api/polars.Series.cumsum.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cumsum#

 

 

-Series.cumsum(*, reverse: bool = False) → Series[source]#

+Series.cumsum(*, reverse: bool = False) → Series[source]#
 
Get an array with the cumulative sum computed at every element.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cumulative_eval.html b/py-polars/html/reference/series/api/polars.Series.cumulative_eval.html
index 217406bacd26..64961aced276 100644
--- a/py-polars/html/reference/series/api/polars.Series.cumulative_eval.html
+++ b/py-polars/html/reference/series/api/polars.Series.cumulative_eval.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cumulative_eval#

 

 

-Series.cumulative_eval(expr: Expr, min_periods: int = 1, *, parallel: bool = False) → Series[source]#

+Series.cumulative_eval(expr: Expr, min_periods: int = 1, *, parallel: bool = False) → Series[source]#
 
Run an expression over a sliding window that increases 1 slot every iteration.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.cut.html b/py-polars/html/reference/series/api/polars.Series.cut.html
index e420977ab189..e72e63ca7155 100644
--- a/py-polars/html/reference/series/api/polars.Series.cut.html
+++ b/py-polars/html/reference/series/api/polars.Series.cut.html
@@ -1628,7 +1628,7 @@
 
polars.Series.cut#

 

 

-Series.cut(breaks: list[float], labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', *, series: bool = True, left_closed: bool = False, include_breaks: bool = False) → DataFrame | Series[source]#

+Series.cut(breaks: list[float], labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', *, series: bool = True, left_closed: bool = False, include_breaks: bool = False) → DataFrame | Series[source]#
 
Bin continuous values into discrete categories.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.describe.html b/py-polars/html/reference/series/api/polars.Series.describe.html
index dd529f901c32..22ee07083936 100644
--- a/py-polars/html/reference/series/api/polars.Series.describe.html
+++ b/py-polars/html/reference/series/api/polars.Series.describe.html
@@ -1628,7 +1628,7 @@
 
polars.Series.describe#

 

 

-Series.describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → DataFrame[source]#

+Series.describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → DataFrame[source]#
 
Quick summary statistics of a series.

 
Series with mixed datatypes will return summary statistics for the datatype of
 the first value.

diff --git a/py-polars/html/reference/series/api/polars.Series.diff.html b/py-polars/html/reference/series/api/polars.Series.diff.html
index c11d28c3df0d..4bdb92535bf2 100644
--- a/py-polars/html/reference/series/api/polars.Series.diff.html
+++ b/py-polars/html/reference/series/api/polars.Series.diff.html
@@ -1628,7 +1628,7 @@
 
polars.Series.diff#

 

 

-Series.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]#

+Series.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]#
 
Calculate the n-th discrete difference.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.dot.html b/py-polars/html/reference/series/api/polars.Series.dot.html
index 737909b34a63..207038f5baa8 100644
--- a/py-polars/html/reference/series/api/polars.Series.dot.html
+++ b/py-polars/html/reference/series/api/polars.Series.dot.html
@@ -1628,7 +1628,7 @@
 
polars.Series.dot#

 

 

-Series.dot(other: Series | Sequence[Any] | Array | ChunkedArray | ndarray | Series | DatetimeIndex) → float | None[source]#

+Series.dot(other: Series | Sequence[Any] | Array | ChunkedArray | ndarray | Series | DatetimeIndex) → float | None[source]#
 
Compute the dot/inner product between two Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.drop_nans.html b/py-polars/html/reference/series/api/polars.Series.drop_nans.html
index c8ab2609d4f3..4350c956545d 100644
--- a/py-polars/html/reference/series/api/polars.Series.drop_nans.html
+++ b/py-polars/html/reference/series/api/polars.Series.drop_nans.html
@@ -1628,7 +1628,7 @@
 
polars.Series.drop_nans#

 

 

-Series.drop_nans() → Series[source]#

+Series.drop_nans() → Series[source]#
 
Drop NaN values.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.drop_nulls.html b/py-polars/html/reference/series/api/polars.Series.drop_nulls.html
index 93d5c4f3c595..cf15334b7203 100644
--- a/py-polars/html/reference/series/api/polars.Series.drop_nulls.html
+++ b/py-polars/html/reference/series/api/polars.Series.drop_nulls.html
@@ -1628,7 +1628,7 @@
 
polars.Series.drop_nulls#

 

 

-Series.drop_nulls() → Series[source]#

+Series.drop_nulls() → Series[source]#
 
Drop all null values.

 
Creates a new Series that copies data from this Series without null values.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.entropy.html b/py-polars/html/reference/series/api/polars.Series.entropy.html
index 5e8c07840201..bc48ab45a810 100644
--- a/py-polars/html/reference/series/api/polars.Series.entropy.html
+++ b/py-polars/html/reference/series/api/polars.Series.entropy.html
@@ -1628,7 +1628,7 @@
 
polars.Series.entropy#

 

 

-Series.entropy(base: float = 2.718281828459045, *, normalize: bool = False) → float | None[source]#

+Series.entropy(base: float = 2.718281828459045, *, normalize: bool = False) → float | None[source]#
 
Computes the entropy.

 
Uses the formula -sum(pk * log(pk) where pk are discrete probabilities.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.estimated_size.html b/py-polars/html/reference/series/api/polars.Series.estimated_size.html
index 01b6b27cffc3..a95944db9fcb 100644
--- a/py-polars/html/reference/series/api/polars.Series.estimated_size.html
+++ b/py-polars/html/reference/series/api/polars.Series.estimated_size.html
@@ -1628,7 +1628,7 @@
 
polars.Series.estimated_size#

 

 

-Series.estimated_size(unit: SizeUnit = 'b') → int | float[source]#

+Series.estimated_size(unit: SizeUnit = 'b') → int | float[source]#
 
Return an estimation of the total (heap) allocated size of the Series.

 
Estimated size is given in the specified unit (bytes by default).

 
This estimation is the sum of the size of its buffers, validity, including
diff --git a/py-polars/html/reference/series/api/polars.Series.ewm_mean.html b/py-polars/html/reference/series/api/polars.Series.ewm_mean.html
index e8201a0e1a1a..14f37b7ef088 100644
--- a/py-polars/html/reference/series/api/polars.Series.ewm_mean.html
+++ b/py-polars/html/reference/series/api/polars.Series.ewm_mean.html
@@ -1629,7 +1629,7 @@
 
polars.Series.ewm_mean#

 

 

-Series.ewm_mean(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#

+Series.ewm_mean(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#
 
Exponentially-weighted moving average.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.ewm_std.html b/py-polars/html/reference/series/api/polars.Series.ewm_std.html
index 81f8723db6a4..8ce5f06c77ca 100644
--- a/py-polars/html/reference/series/api/polars.Series.ewm_std.html
+++ b/py-polars/html/reference/series/api/polars.Series.ewm_std.html
@@ -1629,7 +1629,7 @@
 
polars.Series.ewm_std#

 

 

-Series.ewm_std(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#

+Series.ewm_std(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#
 
Exponentially-weighted moving standard deviation.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.ewm_var.html b/py-polars/html/reference/series/api/polars.Series.ewm_var.html
index 036b24a1935f..68792bdd43e5 100644
--- a/py-polars/html/reference/series/api/polars.Series.ewm_var.html
+++ b/py-polars/html/reference/series/api/polars.Series.ewm_var.html
@@ -1629,7 +1629,7 @@
 
polars.Series.ewm_var#

 

 

-Series.ewm_var(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#

+Series.ewm_var(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]#
 
Exponentially-weighted moving variance.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.exp.html b/py-polars/html/reference/series/api/polars.Series.exp.html
index cb772a90615f..18e4f627ea9c 100644
--- a/py-polars/html/reference/series/api/polars.Series.exp.html
+++ b/py-polars/html/reference/series/api/polars.Series.exp.html
@@ -1628,7 +1628,7 @@
 
polars.Series.exp#

 

 

-Series.exp() → Series[source]#

+Series.exp() → Series[source]#
 
Compute the exponential, element-wise.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.explode.html b/py-polars/html/reference/series/api/polars.Series.explode.html
index 81227897ad17..96eaee458372 100644
--- a/py-polars/html/reference/series/api/polars.Series.explode.html
+++ b/py-polars/html/reference/series/api/polars.Series.explode.html
@@ -1628,7 +1628,7 @@
 
polars.Series.explode#

 

 

-Series.explode() → Series[source]#

+Series.explode() → Series[source]#
 
Explode a list Series.

 
This means that every item is expanded to a new row.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.extend.html b/py-polars/html/reference/series/api/polars.Series.extend.html
index ad08058ed4c4..2f7bac2f24c5 100644
--- a/py-polars/html/reference/series/api/polars.Series.extend.html
+++ b/py-polars/html/reference/series/api/polars.Series.extend.html
@@ -1628,7 +1628,7 @@
 
polars.Series.extend#

 

 

-Series.extend(other: Series) → Self[source]#

+Series.extend(other: Series) → Self[source]#
 
Extend the memory backed by this Series with the values from another.

 
Different from append, which adds the chunks from other to the chunks of
 this series, extend appends the data from other to the underlying memory
diff --git a/py-polars/html/reference/series/api/polars.Series.extend_constant.html b/py-polars/html/reference/series/api/polars.Series.extend_constant.html
index 840589a4e06f..3943bf3a3104 100644
--- a/py-polars/html/reference/series/api/polars.Series.extend_constant.html
+++ b/py-polars/html/reference/series/api/polars.Series.extend_constant.html
@@ -1628,7 +1628,7 @@
 
polars.Series.extend_constant#

 

 

-Series.extend_constant(value: PythonLiteral | None, n: int) → Series[source]#

+Series.extend_constant(value: PythonLiteral | None, n: int) → Series[source]#
 
Extremely fast method for extending the Series with ‘n’ copies of a value.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.fill_nan.html b/py-polars/html/reference/series/api/polars.Series.fill_nan.html
index dc54267ca3c2..19caeac34fec 100644
--- a/py-polars/html/reference/series/api/polars.Series.fill_nan.html
+++ b/py-polars/html/reference/series/api/polars.Series.fill_nan.html
@@ -1628,7 +1628,7 @@
 
polars.Series.fill_nan#

 

 

-Series.fill_nan(value: int | float | Expr | None) → Series[source]#

+Series.fill_nan(value: int | float | Expr | None) → Series[source]#
 
Fill floating point NaN value with a fill value.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.fill_null.html b/py-polars/html/reference/series/api/polars.Series.fill_null.html
index 0b922704c7d0..ed2b9ec10b2e 100644
--- a/py-polars/html/reference/series/api/polars.Series.fill_null.html
+++ b/py-polars/html/reference/series/api/polars.Series.fill_null.html
@@ -1628,7 +1628,7 @@
 
polars.Series.fill_null#

 

 

-Series.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None) → Series[source]#

+Series.fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None) → Series[source]#
 
Fill null values using the specified value or strategy.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.filter.html b/py-polars/html/reference/series/api/polars.Series.filter.html
index 3b1e8e31d386..513270d67d54 100644
--- a/py-polars/html/reference/series/api/polars.Series.filter.html
+++ b/py-polars/html/reference/series/api/polars.Series.filter.html
@@ -1628,7 +1628,7 @@
 
polars.Series.filter#

 

 

-Series.filter(predicate: Series | list[bool]) → Self[source]#

+Series.filter(predicate: Series | list[bool]) → Self[source]#
 
Filter elements by a boolean mask.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.floor.html b/py-polars/html/reference/series/api/polars.Series.floor.html
index 07ec9671bb6a..959d83c6e11d 100644
--- a/py-polars/html/reference/series/api/polars.Series.floor.html
+++ b/py-polars/html/reference/series/api/polars.Series.floor.html
@@ -1628,7 +1628,7 @@
 
polars.Series.floor#

 

 

-Series.floor() → Series[source]#

+Series.floor() → Series[source]#
 
Rounds down to the nearest integer value.

 
Only works on floating point Series.

 
Examples

diff --git a/py-polars/html/reference/series/api/polars.Series.get_chunks.html b/py-polars/html/reference/series/api/polars.Series.get_chunks.html
index 8b85e61d3ecf..4ad84e8dcc07 100644
--- a/py-polars/html/reference/series/api/polars.Series.get_chunks.html
+++ b/py-polars/html/reference/series/api/polars.Series.get_chunks.html
@@ -1628,7 +1628,7 @@
 
polars.Series.get_chunks#

 

 

-Series.get_chunks() → list[polars.series.series.Series][source]#

+Series.get_chunks() → list[polars.series.series.Series][source]#
 
Get the chunks of this Series as a list of Series.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.has_validity.html b/py-polars/html/reference/series/api/polars.Series.has_validity.html
index 2149833a561f..c78ea5787149 100644
--- a/py-polars/html/reference/series/api/polars.Series.has_validity.html
+++ b/py-polars/html/reference/series/api/polars.Series.has_validity.html
@@ -1628,7 +1628,7 @@
 
polars.Series.has_validity#

 

 

-Series.has_validity() → bool[source]#

+Series.has_validity() → bool[source]#
 
Return True if the Series has a validity bitmask.

 
If there is none, it means that there are no null values.
 Use this to swiftly assert a Series does not have null values.

diff --git a/py-polars/html/reference/series/api/polars.Series.hash.html b/py-polars/html/reference/series/api/polars.Series.hash.html
index 87177228be53..27598282e670 100644
--- a/py-polars/html/reference/series/api/polars.Series.hash.html
+++ b/py-polars/html/reference/series/api/polars.Series.hash.html
@@ -1628,7 +1628,7 @@
 
polars.Series.hash#

 

 

-Series.hash(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]#

+Series.hash(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]#
 
Hash the Series.

 
The hash value is of type UInt64.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.head.html b/py-polars/html/reference/series/api/polars.Series.head.html
index b2a417df9478..2d875f0cf93b 100644
--- a/py-polars/html/reference/series/api/polars.Series.head.html
+++ b/py-polars/html/reference/series/api/polars.Series.head.html
@@ -1628,7 +1628,7 @@
 
polars.Series.head#

 

 

-Series.head(n: int = 10) → Series[source]#

+Series.head(n: int = 10) → Series[source]#
 
Get the first n elements.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.hist.html b/py-polars/html/reference/series/api/polars.Series.hist.html
index 462803d0b08b..fde64e90c4e5 100644
--- a/py-polars/html/reference/series/api/polars.Series.hist.html
+++ b/py-polars/html/reference/series/api/polars.Series.hist.html
@@ -1628,7 +1628,7 @@
 
polars.Series.hist#

 

 

-Series.hist(bins: list[float] | None = None, *, bin_count: int | None = None) → DataFrame[source]#

+Series.hist(bins: list[float] | None = None, *, bin_count: int | None = None) → DataFrame[source]#
 
Bin values into buckets and count their occurrences.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.implode.html b/py-polars/html/reference/series/api/polars.Series.implode.html
index 4139b6264fa5..6c3179577895 100644
--- a/py-polars/html/reference/series/api/polars.Series.implode.html
+++ b/py-polars/html/reference/series/api/polars.Series.implode.html
@@ -1628,7 +1628,7 @@
 
polars.Series.implode#

 

 

-Series.implode() → Self[source]#

+Series.implode() → Self[source]#
 
Aggregate values into a list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.interpolate.html b/py-polars/html/reference/series/api/polars.Series.interpolate.html
index dcc4eb7c5cfd..b8b570abb34e 100644
--- a/py-polars/html/reference/series/api/polars.Series.interpolate.html
+++ b/py-polars/html/reference/series/api/polars.Series.interpolate.html
@@ -1628,7 +1628,7 @@
 
polars.Series.interpolate#

 

 

-Series.interpolate(method: InterpolationMethod = 'linear') → Series[source]#

+Series.interpolate(method: InterpolationMethod = 'linear') → Series[source]#
 
Fill null values using interpolation.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_between.html b/py-polars/html/reference/series/api/polars.Series.is_between.html
index c4c21135025d..c37be848bd51 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_between.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_between.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_between#

 

 

-Series.is_between(lower_bound: IntoExpr, upper_bound: IntoExpr, closed: ClosedInterval = 'both') → Series[source]#

+Series.is_between(lower_bound: IntoExpr, upper_bound: IntoExpr, closed: ClosedInterval = 'both') → Series[source]#
 
Get a boolean mask of the values that fall between the given start/end values.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_boolean.html b/py-polars/html/reference/series/api/polars.Series.is_boolean.html
index 1df1d6eb35c3..4c76656f9df0 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_boolean.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_boolean.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_boolean#

 

 

-Series.is_boolean() → bool[source]#

+Series.is_boolean() → bool[source]#
 
Check if this Series is a Boolean.

 
Examples

 
>>> s = pl.Series("a", [True, False, True])
diff --git a/py-polars/html/reference/series/api/polars.Series.is_duplicated.html b/py-polars/html/reference/series/api/polars.Series.is_duplicated.html
index e765dee3f33e..871f6ab0c551 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_duplicated.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_duplicated.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_duplicated#

 

 

-Series.is_duplicated() → Series[source]#

+Series.is_duplicated() → Series[source]#
 
Get mask of all duplicated values.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_empty.html b/py-polars/html/reference/series/api/polars.Series.is_empty.html
index d50cf71c23a2..15a473abaf33 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_empty.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_empty.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_empty#

 

 

-Series.is_empty() → bool[source]#

+Series.is_empty() → bool[source]#
 
Check if the Series is empty.

 
Examples

 
>>> s = pl.Series("a", [], dtype=pl.Float32)
diff --git a/py-polars/html/reference/series/api/polars.Series.is_finite.html b/py-polars/html/reference/series/api/polars.Series.is_finite.html
index 7bec185f20d3..581219c6def0 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_finite.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_finite.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_finite#

 

 

-Series.is_finite() → Series[source]#

+Series.is_finite() → Series[source]#
 
Returns a boolean Series indicating which values are finite.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_first.html b/py-polars/html/reference/series/api/polars.Series.is_first.html
index e3d7b0a6aa8d..36f313025a9d 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_first.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_first.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_first#

 

 

-Series.is_first() → Series[source]#

+Series.is_first() → Series[source]#
 
Get a mask of the first unique value.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_float.html b/py-polars/html/reference/series/api/polars.Series.is_float.html
index 3688c60ecc0c..cc9e6f7431e2 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_float.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_float.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_float#

 

 

-Series.is_float() → bool[source]#

+Series.is_float() → bool[source]#
 
Check if this Series has floating point numbers.

 
Examples

 
>>> s = pl.Series("a", [1.0, 2.0, 3.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.is_in.html b/py-polars/html/reference/series/api/polars.Series.is_in.html
index c63716a74574..99800f461277 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_in.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_in.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_in#

 

 

-Series.is_in(other: Series | Collection[Any]) → Series[source]#

+Series.is_in(other: Series | Collection[Any]) → Series[source]#
 
Check if elements of this Series are in the other Series.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_infinite.html b/py-polars/html/reference/series/api/polars.Series.is_infinite.html
index 7c45949a85b7..29e4f4516298 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_infinite.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_infinite.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_infinite#

 

 

-Series.is_infinite() → Series[source]#

+Series.is_infinite() → Series[source]#
 
Returns a boolean Series indicating which values are infinite.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_integer.html b/py-polars/html/reference/series/api/polars.Series.is_integer.html
index 024a347b0ec2..1966b1c3d063 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_integer.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_integer.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_integer#

 

 

-Series.is_integer(signed: bool | None = None) → bool[source]#

+Series.is_integer(signed: bool | None = None) → bool[source]#
 
Check if this Series datatype is an integer (signed or unsigned).

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_nan.html b/py-polars/html/reference/series/api/polars.Series.is_nan.html
index 331ae7c7276b..c93ac755ee26 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_nan.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_nan.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_nan#

 

 

-Series.is_nan() → Series[source]#

+Series.is_nan() → Series[source]#
 
Returns a boolean Series indicating which values are not NaN.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_not_nan.html b/py-polars/html/reference/series/api/polars.Series.is_not_nan.html
index 55a2644ea60b..b268d9deb215 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_not_nan.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_not_nan.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_not_nan#

 

 

-Series.is_not_nan() → Series[source]#

+Series.is_not_nan() → Series[source]#
 
Returns a boolean Series indicating which values are not NaN.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_not_null.html b/py-polars/html/reference/series/api/polars.Series.is_not_null.html
index 4c2b67b95800..779dfa8a6e95 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_not_null.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_not_null.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_not_null#

 

 

-Series.is_not_null() → Series[source]#

+Series.is_not_null() → Series[source]#
 
Returns a boolean Series indicating which values are not null.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_null.html b/py-polars/html/reference/series/api/polars.Series.is_null.html
index aca894beb759..af58d537a891 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_null.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_null.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_null#

 

 

-Series.is_null() → Series[source]#

+Series.is_null() → Series[source]#
 
Returns a boolean Series indicating which values are null.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_numeric.html b/py-polars/html/reference/series/api/polars.Series.is_numeric.html
index f1bb9a8e8e04..8c8dc495300e 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_numeric.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_numeric.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_numeric#

 

 

-Series.is_numeric() → bool[source]#

+Series.is_numeric() → bool[source]#
 
Check if this Series datatype is numeric.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.is_sorted.html b/py-polars/html/reference/series/api/polars.Series.is_sorted.html
index f1a6adccfef8..9c31a564ac03 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_sorted.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_sorted#

 

 

-Series.is_sorted(*, descending: bool = False) → bool[source]#

+Series.is_sorted(*, descending: bool = False) → bool[source]#
 
Check if the Series is sorted.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_temporal.html b/py-polars/html/reference/series/api/polars.Series.is_temporal.html
index eccca2f97764..7b1d90154fdf 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_temporal.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_temporal.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_temporal#

 

 

-Series.is_temporal(excluding: OneOrMoreDataTypes | None = None) → bool[source]#

+Series.is_temporal(excluding: OneOrMoreDataTypes | None = None) → bool[source]#
 
Check if this Series datatype is temporal.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_unique.html b/py-polars/html/reference/series/api/polars.Series.is_unique.html
index 7372f7fba1bd..cd9aeb2d8a3d 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_unique.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_unique.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_unique#

 

 

-Series.is_unique() → Series[source]#

+Series.is_unique() → Series[source]#
 
Get mask of all unique values.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.is_utf8.html b/py-polars/html/reference/series/api/polars.Series.is_utf8.html
index 28f205592f72..b0fa96e50032 100644
--- a/py-polars/html/reference/series/api/polars.Series.is_utf8.html
+++ b/py-polars/html/reference/series/api/polars.Series.is_utf8.html
@@ -1628,7 +1628,7 @@
 
polars.Series.is_utf8#

 

 

-Series.is_utf8() → bool[source]#

+Series.is_utf8() → bool[source]#
 
Check if this Series datatype is a Utf8.

 
Examples

 
>>> s = pl.Series("x", ["a", "b", "c"])
diff --git a/py-polars/html/reference/series/api/polars.Series.item.html b/py-polars/html/reference/series/api/polars.Series.item.html
index 4ad666163182..7115fbbfae31 100644
--- a/py-polars/html/reference/series/api/polars.Series.item.html
+++ b/py-polars/html/reference/series/api/polars.Series.item.html
@@ -1628,7 +1628,7 @@
 
polars.Series.item#

 

 

-Series.item(row: int | None = None) → Any[source]#

+Series.item(row: int | None = None) → Any[source]#
 
Return the series as a scalar, or return the element at the given row index.

 
If no row index is provided, this is equivalent to s[0], with a check
 that the shape is (1,). With a row index, this is equivalent to s[row].

diff --git a/py-polars/html/reference/series/api/polars.Series.kurtosis.html b/py-polars/html/reference/series/api/polars.Series.kurtosis.html
index 4bf0577e25bf..b2a78344e8e1 100644
--- a/py-polars/html/reference/series/api/polars.Series.kurtosis.html
+++ b/py-polars/html/reference/series/api/polars.Series.kurtosis.html
@@ -1628,7 +1628,7 @@
 
polars.Series.kurtosis#

 

 

-Series.kurtosis(*, fisher: bool = True, bias: bool = True) → float | None[source]#

+Series.kurtosis(*, fisher: bool = True, bias: bool = True) → float | None[source]#
 
Compute the kurtosis (Fisher or Pearson) of a dataset.

 
Kurtosis is the fourth central moment divided by the square of the
 variance. If Fisher’s definition is used, then 3.0 is subtracted from
diff --git a/py-polars/html/reference/series/api/polars.Series.len.html b/py-polars/html/reference/series/api/polars.Series.len.html
index 212ca37abf69..182b70a05808 100644
--- a/py-polars/html/reference/series/api/polars.Series.len.html
+++ b/py-polars/html/reference/series/api/polars.Series.len.html
@@ -1628,7 +1628,7 @@
 
polars.Series.len#

 

 

-Series.len() → int[source]#

+Series.len() → int[source]#
 
Length of this Series.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.limit.html b/py-polars/html/reference/series/api/polars.Series.limit.html
index eeaf8c69efd3..326a7e21d661 100644
--- a/py-polars/html/reference/series/api/polars.Series.limit.html
+++ b/py-polars/html/reference/series/api/polars.Series.limit.html
@@ -1628,7 +1628,7 @@
 
polars.Series.limit#

 

 

-Series.limit(n: int = 10) → Series[source]#

+Series.limit(n: int = 10) → Series[source]#
 
Get the first n elements.

 
Alias for Series.head().

 

diff --git a/py-polars/html/reference/series/api/polars.Series.list.all.html b/py-polars/html/reference/series/api/polars.Series.list.all.html
index 6d5be55b4550..38fcb2428047 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.all.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.all.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.all#

 

 

-Series.list.all() → Expr[source]#

+Series.list.all() → Expr[source]#
 
Evaluate whether all boolean values in a list are true.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/series/api/polars.Series.list.any.html b/py-polars/html/reference/series/api/polars.Series.list.any.html
index 816ad7232ba3..63343b0a18bc 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.any.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.any.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.any#

 

 

-Series.list.any() → Expr[source]#

+Series.list.any() → Expr[source]#
 
Evaluate whether any boolean value in a list is true.

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/html/reference/series/api/polars.Series.list.arg_max.html b/py-polars/html/reference/series/api/polars.Series.list.arg_max.html
index 8907860e916e..2861794d5bc9 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.arg_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.arg_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.arg_max#

 

 

-Series.list.arg_max() → Series[source]#

+Series.list.arg_max() → Series[source]#
 
Retrieve the index of the maximum value in every sublist.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.arg_min.html b/py-polars/html/reference/series/api/polars.Series.list.arg_min.html
index 530bb3736a27..3e07631d92e5 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.arg_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.arg_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.arg_min#

 

 

-Series.list.arg_min() → Series[source]#

+Series.list.arg_min() → Series[source]#
 
Retrieve the index of the minimal value in every sublist.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.concat.html b/py-polars/html/reference/series/api/polars.Series.list.concat.html
index 63622935efae..02e01f68acc1 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.concat.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.concat.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.concat#

 

 

-Series.list.concat(other: list[Series] | Series | list[Any]) → Series[source]#

+Series.list.concat(other: list[Series] | Series | list[Any]) → Series[source]#
 
Concat the arrays in a Series dtype List in linear time.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.contains.html b/py-polars/html/reference/series/api/polars.Series.list.contains.html
index fd4745071021..9b85a8834e6a 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.contains.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.contains.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.contains#

 

 

-Series.list.contains(item: float | str | bool | int | date | datetime) → Series[source]#

+Series.list.contains(item: float | str | bool | int | date | datetime) → Series[source]#
 
Check if sublists contain the given item.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.count_match.html b/py-polars/html/reference/series/api/polars.Series.list.count_match.html
index 6125f4740431..d2fff567e478 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.count_match.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.count_match.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.count_match#

 

 

-Series.list.count_match(element: float | str | bool | int | date | datetime | time | Expr) → Expr[source]#

+Series.list.count_match(element: float | str | bool | int | date | datetime | time | Expr) → Expr[source]#
 
Count how often the value produced by element occurs.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.diff.html b/py-polars/html/reference/series/api/polars.Series.list.diff.html
index 6ec3fba37c85..271f2301c289 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.diff.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.diff.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.diff#

 

 

-Series.list.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]#

+Series.list.diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]#
 
Calculate the n-th discrete difference of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.eval.html b/py-polars/html/reference/series/api/polars.Series.list.eval.html
index 294fb2b533de..4461d0b34f1a 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.eval.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.eval.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.eval#

 

 

-Series.list.eval(expr: Expr, *, parallel: bool = False) → Series[source]#

+Series.list.eval(expr: Expr, *, parallel: bool = False) → Series[source]#
 
Run any polars expression against the lists’ elements.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.explode.html b/py-polars/html/reference/series/api/polars.Series.list.explode.html
index fe4d9e44a7a0..155a77ea9fa7 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.explode.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.explode.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.explode#

 

 

-Series.list.explode() → Series[source]#

+Series.list.explode() → Series[source]#
 
Returns a column with a separate row for every list element.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.first.html b/py-polars/html/reference/series/api/polars.Series.list.first.html
index 187cb02a9c69..e00f40865d43 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.first.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.first.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.first#

 

 

-Series.list.first() → Series[source]#

+Series.list.first() → Series[source]#
 
Get the first value of the sublists.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.get.html b/py-polars/html/reference/series/api/polars.Series.list.get.html
index ebd01a9cf19b..051e0d890137 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.get.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.get.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.get#

 

 

-Series.list.get(index: int | Series | list[int]) → Series[source]#

+Series.list.get(index: int | Series | list[int]) → Series[source]#
 
Get the value by index in the sublists.

 
So index 0 would return the first item of every sublist
 and index -1 would return the last item of every sublist
diff --git a/py-polars/html/reference/series/api/polars.Series.list.head.html b/py-polars/html/reference/series/api/polars.Series.list.head.html
index 9ddf5d94ce1a..b2e3b79f22b2 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.head.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.head.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.head#

 

 

-Series.list.head(n: int | Expr = 5) → Series[source]#

+Series.list.head(n: int | Expr = 5) → Series[source]#
 
Slice the first n values of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.join.html b/py-polars/html/reference/series/api/polars.Series.list.join.html
index b791cf5f089f..8ab48c055ec1 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.join.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.join.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.join#

 

 

-Series.list.join(separator: str) → Series[source]#

+Series.list.join(separator: str) → Series[source]#
 
Join all string items in a sublist and place a separator between them.

 
This errors if inner type of list != Utf8.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.list.last.html b/py-polars/html/reference/series/api/polars.Series.list.last.html
index 701017424ea2..9493e874f136 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.last.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.last.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.last#

 

 

-Series.list.last() → Series[source]#

+Series.list.last() → Series[source]#
 
Get the last value of the sublists.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.lengths.html b/py-polars/html/reference/series/api/polars.Series.list.lengths.html
index 911f9986d108..2246ae48f098 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.lengths.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.lengths.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.lengths#

 

 

-Series.list.lengths() → Series[source]#

+Series.list.lengths() → Series[source]#
 
Get the length of the arrays as UInt32.

 
Examples

 
>>> s = pl.Series([[1, 2, 3], [5]])
diff --git a/py-polars/html/reference/series/api/polars.Series.list.max.html b/py-polars/html/reference/series/api/polars.Series.list.max.html
index e6b6b4662bf2..f1df548b8efc 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.max.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.max#

 

 

-Series.list.max() → Series[source]#

+Series.list.max() → Series[source]#
 
Compute the max value of the arrays in the list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.mean.html b/py-polars/html/reference/series/api/polars.Series.list.mean.html
index 4e7da8698d2c..b49e381ebbbd 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.mean.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.mean.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.mean#

 

 

-Series.list.mean() → Series[source]#

+Series.list.mean() → Series[source]#
 
Compute the mean value of the arrays in the list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.min.html b/py-polars/html/reference/series/api/polars.Series.list.min.html
index 562433e78804..21b81003a141 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.min.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.min#

 

 

-Series.list.min() → Series[source]#

+Series.list.min() → Series[source]#
 
Compute the min value of the arrays in the list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.reverse.html b/py-polars/html/reference/series/api/polars.Series.list.reverse.html
index 3a4974d6a413..902a7b43b8ef 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.reverse.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.reverse.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.reverse#

 

 

-Series.list.reverse() → Series[source]#

+Series.list.reverse() → Series[source]#
 
Reverse the arrays in the list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.set_difference.html b/py-polars/html/reference/series/api/polars.Series.list.set_difference.html
index 86b4405558bd..d5c69a1420ea 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.set_difference.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.set_difference.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.set_difference#

 

 

-Series.list.set_difference(other: Series) → Series[source]#

+Series.list.set_difference(other: Series) → Series[source]#
 
Compute the SET DIFFERENCE between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.set_intersection.html b/py-polars/html/reference/series/api/polars.Series.list.set_intersection.html
index 80493a5bc094..09b7940c364d 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.set_intersection.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.set_intersection.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.set_intersection#

 

 

-Series.list.set_intersection(other: Series) → Series[source]#

+Series.list.set_intersection(other: Series) → Series[source]#
 
Compute the SET INTERSECTION between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.set_symmetric_difference.html b/py-polars/html/reference/series/api/polars.Series.list.set_symmetric_difference.html
index 278e98228a70..fbc88b01f52f 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.set_symmetric_difference.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.set_symmetric_difference.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.set_symmetric_difference#

 

 

-Series.list.set_symmetric_difference(other: Series) → Series[source]#

+Series.list.set_symmetric_difference(other: Series) → Series[source]#
 
Compute the SET SYMMETRIC DIFFERENCE between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.set_union.html b/py-polars/html/reference/series/api/polars.Series.list.set_union.html
index 12696ab17a41..e6290cfbec19 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.set_union.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.set_union.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.set_union#

 

 

-Series.list.set_union(other: Series) → Series[source]#

+Series.list.set_union(other: Series) → Series[source]#
 
Compute the SET UNION between the elements in this list and the elements of other.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.shift.html b/py-polars/html/reference/series/api/polars.Series.list.shift.html
index 2fe6e14a6427..8ca56ec66899 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.shift.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.shift.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.shift#

 

 

-Series.list.shift(periods: int = 1) → Series[source]#

+Series.list.shift(periods: int = 1) → Series[source]#
 
Shift values by the given period.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.slice.html b/py-polars/html/reference/series/api/polars.Series.list.slice.html
index f2cf656a0279..221a80f21016 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.slice.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.slice.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.slice#

 

 

-Series.list.slice(offset: int | Expr, length: int | Expr | None = None) → Series[source]#

+Series.list.slice(offset: int | Expr, length: int | Expr | None = None) → Series[source]#
 
Slice every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.sort.html b/py-polars/html/reference/series/api/polars.Series.list.sort.html
index aad74c0f37c4..751fa8dd7eae 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.sort.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.sort.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.sort#

 

 

-Series.list.sort(*, descending: bool = False) → Series[source]#

+Series.list.sort(*, descending: bool = False) → Series[source]#
 
Sort the arrays in this column.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.sum.html b/py-polars/html/reference/series/api/polars.Series.list.sum.html
index eafe6d851245..d2436461282f 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.sum.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.sum.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.sum#

 

 

-Series.list.sum() → Series[source]#

+Series.list.sum() → Series[source]#
 
Sum all the arrays in the list.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.list.tail.html b/py-polars/html/reference/series/api/polars.Series.list.tail.html
index 3e23487c7b20..a56f3931614c 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.tail.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.tail.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.tail#

 

 

-Series.list.tail(n: int | Expr = 5) → Series[source]#

+Series.list.tail(n: int | Expr = 5) → Series[source]#
 
Slice the last n values of every sublist.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.take.html b/py-polars/html/reference/series/api/polars.Series.list.take.html
index 924d0b90682e..0fc8ac1df52a 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.take.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.take.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.take#

 

 

-Series.list.take(index: Series | list[int] | list[list[int]], *, null_on_oob: bool = False) → Series[source]#

+Series.list.take(index: Series | list[int] | list[list[int]], *, null_on_oob: bool = False) → Series[source]#
 
Take sublists by multiple indices.

 
The indices may be defined in a single column, or by sublists in another
 column of dtype List.

diff --git a/py-polars/html/reference/series/api/polars.Series.list.to_struct.html b/py-polars/html/reference/series/api/polars.Series.list.to_struct.html
index 3549bfdefafd..385b416c67bc 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.to_struct.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.to_struct.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.to_struct#

 

 

-Series.list.to_struct(n_field_strategy: ToStructStrategy = 'first_non_null', fields: Callable[[int], str] | Sequence[str] | None = None) → Series[source]#

+Series.list.to_struct(n_field_strategy: ToStructStrategy = 'first_non_null', fields: Callable[[int], str] | Sequence[str] | None = None) → Series[source]#
 
Convert the series of type List to a series of type Struct.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.list.unique.html b/py-polars/html/reference/series/api/polars.Series.list.unique.html
index 0f15f8104871..573bdd2c1d5c 100644
--- a/py-polars/html/reference/series/api/polars.Series.list.unique.html
+++ b/py-polars/html/reference/series/api/polars.Series.list.unique.html
@@ -1628,7 +1628,7 @@
 
polars.Series.list.unique#

 

 

-Series.list.unique(*, maintain_order: bool = False) → Series[source]#

+Series.list.unique(*, maintain_order: bool = False) → Series[source]#
 
Get the unique/distinct values in the list.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.log.html b/py-polars/html/reference/series/api/polars.Series.log.html
index 7948cf25a92a..c5d442c15a06 100644
--- a/py-polars/html/reference/series/api/polars.Series.log.html
+++ b/py-polars/html/reference/series/api/polars.Series.log.html
@@ -1628,7 +1628,7 @@
 
polars.Series.log#

 

 

-Series.log(base: float = 2.718281828459045) → Series[source]#

+Series.log(base: float = 2.718281828459045) → Series[source]#
 
Compute the logarithm to a given base.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.log10.html b/py-polars/html/reference/series/api/polars.Series.log10.html
index 5a4cc5800564..8f59bf68c924 100644
--- a/py-polars/html/reference/series/api/polars.Series.log10.html
+++ b/py-polars/html/reference/series/api/polars.Series.log10.html
@@ -1628,7 +1628,7 @@
 
polars.Series.log10#

 

 

-Series.log10() → Series[source]#

+Series.log10() → Series[source]#
 
Compute the base 10 logarithm of the input array, element-wise.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.log1p.html b/py-polars/html/reference/series/api/polars.Series.log1p.html
index f854bfa63cf2..a0886f28d662 100644
--- a/py-polars/html/reference/series/api/polars.Series.log1p.html
+++ b/py-polars/html/reference/series/api/polars.Series.log1p.html
@@ -1628,7 +1628,7 @@
 
polars.Series.log1p#

 

 

-Series.log1p() → Series[source]#

+Series.log1p() → Series[source]#
 
Compute the natural logarithm of the input array plus one, element-wise.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.lower_bound.html b/py-polars/html/reference/series/api/polars.Series.lower_bound.html
index b5503449033f..8f708a6313d8 100644
--- a/py-polars/html/reference/series/api/polars.Series.lower_bound.html
+++ b/py-polars/html/reference/series/api/polars.Series.lower_bound.html
@@ -1628,7 +1628,7 @@
 
polars.Series.lower_bound#

 

 

-Series.lower_bound() → Self[source]#

+Series.lower_bound() → Self[source]#
 
Return the lower bound of this Series’ dtype as a unit Series.

 

 
See also

diff --git a/py-polars/html/reference/series/api/polars.Series.map_dict.html b/py-polars/html/reference/series/api/polars.Series.map_dict.html
index 45949dccb26b..d26de551e930 100644
--- a/py-polars/html/reference/series/api/polars.Series.map_dict.html
+++ b/py-polars/html/reference/series/api/polars.Series.map_dict.html
@@ -1628,7 +1628,7 @@
 
polars.Series.map_dict#

 

 

-Series.map_dict(remapping: dict[Any, Any], *, default: Any = None, return_dtype: PolarsDataType | None = None) → Self[source]#

+Series.map_dict(remapping: dict[Any, Any], *, default: Any = None, return_dtype: PolarsDataType | None = None) → Self[source]#
 
Replace values in the Series using a remapping dictionary.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.max.html b/py-polars/html/reference/series/api/polars.Series.max.html
index 6a2b4febc44c..8dca01f536f5 100644
--- a/py-polars/html/reference/series/api/polars.Series.max.html
+++ b/py-polars/html/reference/series/api/polars.Series.max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.max#

 

 

-Series.max() → PythonLiteral | None[source]#

+Series.max() → PythonLiteral | None[source]#
 
Get the maximum value in this Series.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.mean.html b/py-polars/html/reference/series/api/polars.Series.mean.html
index ed7e27006e73..4bf5ae9d2e7c 100644
--- a/py-polars/html/reference/series/api/polars.Series.mean.html
+++ b/py-polars/html/reference/series/api/polars.Series.mean.html
@@ -1628,7 +1628,7 @@
 
polars.Series.mean#

 

 

-Series.mean() → int | float | None[source]#

+Series.mean() → int | float | None[source]#
 
Reduce this Series to the mean value.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.median.html b/py-polars/html/reference/series/api/polars.Series.median.html
index cdb34f8a5be4..89bc421af323 100644
--- a/py-polars/html/reference/series/api/polars.Series.median.html
+++ b/py-polars/html/reference/series/api/polars.Series.median.html
@@ -1628,7 +1628,7 @@
 
polars.Series.median#

 

 

-Series.median() → float | None[source]#

+Series.median() → float | None[source]#
 
Get the median of this Series.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.min.html b/py-polars/html/reference/series/api/polars.Series.min.html
index 08310cd5ccfa..bd80ff5a5c05 100644
--- a/py-polars/html/reference/series/api/polars.Series.min.html
+++ b/py-polars/html/reference/series/api/polars.Series.min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.min#

 

 

-Series.min() → PythonLiteral | None[source]#

+Series.min() → PythonLiteral | None[source]#
 
Get the minimal value in this Series.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.mode.html b/py-polars/html/reference/series/api/polars.Series.mode.html
index a8906f2a5d64..8dd4e669930d 100644
--- a/py-polars/html/reference/series/api/polars.Series.mode.html
+++ b/py-polars/html/reference/series/api/polars.Series.mode.html
@@ -1628,7 +1628,7 @@
 
polars.Series.mode#

 

 

-Series.mode() → Series[source]#

+Series.mode() → Series[source]#
 
Compute the most occurring value(s).

 
Can return multiple Values.

 
Examples

diff --git a/py-polars/html/reference/series/api/polars.Series.n_chunks.html b/py-polars/html/reference/series/api/polars.Series.n_chunks.html
index c2ba87928c7d..ded00c930358 100644
--- a/py-polars/html/reference/series/api/polars.Series.n_chunks.html
+++ b/py-polars/html/reference/series/api/polars.Series.n_chunks.html
@@ -1628,7 +1628,7 @@
 
polars.Series.n_chunks#

 

 

-Series.n_chunks() → int[source]#

+Series.n_chunks() → int[source]#
 
Get the number of chunks that this Series contains.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.n_unique.html b/py-polars/html/reference/series/api/polars.Series.n_unique.html
index 09fb7782018c..3b0ea93c010d 100644
--- a/py-polars/html/reference/series/api/polars.Series.n_unique.html
+++ b/py-polars/html/reference/series/api/polars.Series.n_unique.html
@@ -1628,7 +1628,7 @@
 
polars.Series.n_unique#

 

 

-Series.n_unique() → int[source]#

+Series.n_unique() → int[source]#
 
Count the number of unique values in this Series.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 2, 3])
diff --git a/py-polars/html/reference/series/api/polars.Series.nan_max.html b/py-polars/html/reference/series/api/polars.Series.nan_max.html
index 01356b6831bb..ea41857e2571 100644
--- a/py-polars/html/reference/series/api/polars.Series.nan_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.nan_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.nan_max#

 

 

-Series.nan_max() → int | float | date | datetime | timedelta | str[source]#

+Series.nan_max() → int | float | date | datetime | timedelta | str[source]#
 
Get maximum value, but propagate/poison encountered NaN values.

 
This differs from numpy’s nanmax as numpy defaults to propagating NaN values,
 whereas polars defaults to ignoring them.

diff --git a/py-polars/html/reference/series/api/polars.Series.nan_min.html b/py-polars/html/reference/series/api/polars.Series.nan_min.html
index a98bfcf2c93a..0bdd367951c8 100644
--- a/py-polars/html/reference/series/api/polars.Series.nan_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.nan_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.nan_min#

 

 

-Series.nan_min() → int | float | date | datetime | timedelta | str[source]#

+Series.nan_min() → int | float | date | datetime | timedelta | str[source]#
 
Get minimum value, but propagate/poison encountered NaN values.

 
This differs from numpy’s nanmax as numpy defaults to propagating NaN values,
 whereas polars defaults to ignoring them.

diff --git a/py-polars/html/reference/series/api/polars.Series.new_from_index.html b/py-polars/html/reference/series/api/polars.Series.new_from_index.html
index f7a4afc428fa..884b643c9cdc 100644
--- a/py-polars/html/reference/series/api/polars.Series.new_from_index.html
+++ b/py-polars/html/reference/series/api/polars.Series.new_from_index.html
@@ -1628,7 +1628,7 @@
 
polars.Series.new_from_index#

 

 

-Series.new_from_index(index: int, length: int) → Self[source]#

+Series.new_from_index(index: int, length: int) → Self[source]#
 
Create a new Series filled with values from the given index.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.null_count.html b/py-polars/html/reference/series/api/polars.Series.null_count.html
index 37d88b9cd317..af98643d34d1 100644
--- a/py-polars/html/reference/series/api/polars.Series.null_count.html
+++ b/py-polars/html/reference/series/api/polars.Series.null_count.html
@@ -1628,7 +1628,7 @@
 
polars.Series.null_count#

 

 

-Series.null_count() → int[source]#

+Series.null_count() → int[source]#
 
Count the null values in this Series.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.pct_change.html b/py-polars/html/reference/series/api/polars.Series.pct_change.html
index 9b6bc18c95bc..31091457103d 100644
--- a/py-polars/html/reference/series/api/polars.Series.pct_change.html
+++ b/py-polars/html/reference/series/api/polars.Series.pct_change.html
@@ -1628,7 +1628,7 @@
 
polars.Series.pct_change#

 

 

-Series.pct_change(n: int = 1) → Series[source]#

+Series.pct_change(n: int = 1) → Series[source]#
 
Computes percentage change between values.

 
Percentage change (as fraction) between current element and most-recent
 non-null element at least n period(s) before the current element.

diff --git a/py-polars/html/reference/series/api/polars.Series.peak_max.html b/py-polars/html/reference/series/api/polars.Series.peak_max.html
index 2746ec3d9d54..ba1cd25905cc 100644
--- a/py-polars/html/reference/series/api/polars.Series.peak_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.peak_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.peak_max#

 

 

-Series.peak_max() → Self[source]#

+Series.peak_max() → Self[source]#
 
Get a boolean mask of the local maximum peaks.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3, 4, 5])
diff --git a/py-polars/html/reference/series/api/polars.Series.peak_min.html b/py-polars/html/reference/series/api/polars.Series.peak_min.html
index 0f20731fc18d..4e150aabf515 100644
--- a/py-polars/html/reference/series/api/polars.Series.peak_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.peak_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.peak_min#

 

 

-Series.peak_min() → Self[source]#

+Series.peak_min() → Self[source]#
 
Get a boolean mask of the local minimum peaks.

 
Examples

 
>>> s = pl.Series("a", [4, 1, 3, 2, 5])
diff --git a/py-polars/html/reference/series/api/polars.Series.product.html b/py-polars/html/reference/series/api/polars.Series.product.html
index 989693207a4c..0c1af2d21360 100644
--- a/py-polars/html/reference/series/api/polars.Series.product.html
+++ b/py-polars/html/reference/series/api/polars.Series.product.html
@@ -1628,7 +1628,7 @@
 
polars.Series.product#

 

 

-Series.product() → int | float[source]#

+Series.product() → int | float[source]#
 
Reduce this Series to the product value.

 

 
diff --git a/py-polars/html/reference/series/api/polars.Series.qcut.html b/py-polars/html/reference/series/api/polars.Series.qcut.html
index f0a97b433906..e06020e3bebc 100644
--- a/py-polars/html/reference/series/api/polars.Series.qcut.html
+++ b/py-polars/html/reference/series/api/polars.Series.qcut.html
@@ -1628,7 +1628,7 @@
 
polars.Series.qcut#

 

 

-Series.qcut(q: list[float] | int, *, labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', series: bool = True, left_closed: bool = False, allow_duplicates: bool = False, include_breaks: bool = False) → DataFrame | Series[source]#

+Series.qcut(q: list[float] | int, *, labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', series: bool = True, left_closed: bool = False, allow_duplicates: bool = False, include_breaks: bool = False) → DataFrame | Series[source]#
 
Discretize continuous values into discrete categories based on their quantiles.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.quantile.html b/py-polars/html/reference/series/api/polars.Series.quantile.html
index a8dadd8f7d42..4a0d888cd710 100644
--- a/py-polars/html/reference/series/api/polars.Series.quantile.html
+++ b/py-polars/html/reference/series/api/polars.Series.quantile.html
@@ -1628,7 +1628,7 @@
 
polars.Series.quantile#

 

 

-Series.quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → float | None[source]#

+Series.quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → float | None[source]#
 
Get the quantile value of this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.rank.html b/py-polars/html/reference/series/api/polars.Series.rank.html
index 43fb6e1fa9d2..c5e7c8cff97a 100644
--- a/py-polars/html/reference/series/api/polars.Series.rank.html
+++ b/py-polars/html/reference/series/api/polars.Series.rank.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rank#

 

 

-Series.rank(method: RankMethod = 'average', *, descending: bool = False, seed: int | None = None) → Series[source]#

+Series.rank(method: RankMethod = 'average', *, descending: bool = False, seed: int | None = None) → Series[source]#
 
Assign ranks to data, dealing with ties appropriately.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.rechunk.html b/py-polars/html/reference/series/api/polars.Series.rechunk.html
index 2d475a541522..2759684e1477 100644
--- a/py-polars/html/reference/series/api/polars.Series.rechunk.html
+++ b/py-polars/html/reference/series/api/polars.Series.rechunk.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rechunk#

 

 

-Series.rechunk(*, in_place: bool = False) → Self[source]#

+Series.rechunk(*, in_place: bool = False) → Self[source]#
 
Create a single chunk of memory for this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.reinterpret.html b/py-polars/html/reference/series/api/polars.Series.reinterpret.html
index 8b2b2f56ed83..be4c85408275 100644
--- a/py-polars/html/reference/series/api/polars.Series.reinterpret.html
+++ b/py-polars/html/reference/series/api/polars.Series.reinterpret.html
@@ -1628,7 +1628,7 @@
 
polars.Series.reinterpret#

 

 

-Series.reinterpret(*, signed: bool = True) → Series[source]#

+Series.reinterpret(*, signed: bool = True) → Series[source]#
 
Reinterpret the underlying bits as a signed/unsigned integer.

 
This operation is only allowed for 64bit integers. For lower bits integers,
 you can safely use that cast operation.

diff --git a/py-polars/html/reference/series/api/polars.Series.rename.html b/py-polars/html/reference/series/api/polars.Series.rename.html
index 219404509fdf..131b9b02f26e 100644
--- a/py-polars/html/reference/series/api/polars.Series.rename.html
+++ b/py-polars/html/reference/series/api/polars.Series.rename.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rename#

 

 

-Series.rename(name: str, *, in_place: bool | None = None) → Series[source]#

+Series.rename(name: str, *, in_place: bool | None = None) → Series[source]#
 
Rename this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.reshape.html b/py-polars/html/reference/series/api/polars.Series.reshape.html
index afaa7bc8eced..a1261aa43515 100644
--- a/py-polars/html/reference/series/api/polars.Series.reshape.html
+++ b/py-polars/html/reference/series/api/polars.Series.reshape.html
@@ -1628,7 +1628,7 @@
 
polars.Series.reshape#

 

 

-Series.reshape(dimensions: tuple[int, ...]) → Series[source]#

+Series.reshape(dimensions: tuple[int, ...]) → Series[source]#
 
Reshape this Series to a flat Series or a Series of Lists.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.reverse.html b/py-polars/html/reference/series/api/polars.Series.reverse.html
index 1e8f0b3d4b7e..57fef799133b 100644
--- a/py-polars/html/reference/series/api/polars.Series.reverse.html
+++ b/py-polars/html/reference/series/api/polars.Series.reverse.html
@@ -1628,7 +1628,7 @@
 
polars.Series.reverse#

 

 

-Series.reverse() → Series[source]#

+Series.reverse() → Series[source]#
 
Return Series in reverse order.

 
Examples

 
>>> s = pl.Series("a", [1, 2, 3], dtype=pl.Int8)
diff --git a/py-polars/html/reference/series/api/polars.Series.rle.html b/py-polars/html/reference/series/api/polars.Series.rle.html
index eb605ec8e0e9..37324764c268 100644
--- a/py-polars/html/reference/series/api/polars.Series.rle.html
+++ b/py-polars/html/reference/series/api/polars.Series.rle.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rle#

 

 

-Series.rle() → Series[source]#

+Series.rle() → Series[source]#
 
Get the lengths of runs of identical values.

 

 
Returns:

diff --git a/py-polars/html/reference/series/api/polars.Series.rle_id.html b/py-polars/html/reference/series/api/polars.Series.rle_id.html
index d863d493f31f..dbf6efdbbe45 100644
--- a/py-polars/html/reference/series/api/polars.Series.rle_id.html
+++ b/py-polars/html/reference/series/api/polars.Series.rle_id.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rle_id#

 

 

-Series.rle_id() → Series[source]#

+Series.rle_id() → Series[source]#
 
Map values to run IDs.

 
Similar to RLE, but it maps each value to an ID corresponding to the run into
 which it falls. This is especially useful when you want to define groups by
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_apply.html b/py-polars/html/reference/series/api/polars.Series.rolling_apply.html
index bb2d1f8566f1..bd1f466aecb5 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_apply.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_apply.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_apply#

 

 

-Series.rolling_apply(function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_apply(function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Apply a custom rolling window function.

 
Prefer the specific rolling window functions over this one, as they are faster:

 

diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_max.html b/py-polars/html/reference/series/api/polars.Series.rolling_max.html
index ce852f5dd4a1..214b61e69cf8 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_max.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_max.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_max#

 

 

-Series.rolling_max(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_max(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Apply a rolling max (moving max) over the values in this array.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_mean.html b/py-polars/html/reference/series/api/polars.Series.rolling_mean.html
index 85e091fedb5d..70685cc50915 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_mean.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_mean.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_mean#

 

 

-Series.rolling_mean(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_mean(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Apply a rolling mean (moving mean) over the values in this array.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_median.html b/py-polars/html/reference/series/api/polars.Series.rolling_median.html
index 59fb8ff58f69..dbf69817906c 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_median.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_median.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_median#

 

 

-Series.rolling_median(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_median(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Compute a rolling median.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_min.html b/py-polars/html/reference/series/api/polars.Series.rolling_min.html
index b983110c6ad5..da26436a4112 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_min.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_min.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_min#

 

 

-Series.rolling_min(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_min(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Apply a rolling min (moving min) over the values in this array.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_quantile.html b/py-polars/html/reference/series/api/polars.Series.rolling_quantile.html
index 988bc01d70a0..dab63985bd23 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_quantile.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_quantile.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_quantile#

 

 

-Series.rolling_quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int = 2, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int = 2, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Compute a rolling quantile.

 
The window at a given row will include the row itself and the window_size - 1
 elements before it.

diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_skew.html b/py-polars/html/reference/series/api/polars.Series.rolling_skew.html
index f247d9dd03f5..6b64593eb93d 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_skew.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_skew.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_skew#

 

 

-Series.rolling_skew(window_size: int, *, bias: bool = True) → Series[source]#

+Series.rolling_skew(window_size: int, *, bias: bool = True) → Series[source]#
 
Compute a rolling skew.

 
The window at a given row includes the row itself and the
 window_size - 1 elements before it.

diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_std.html b/py-polars/html/reference/series/api/polars.Series.rolling_std.html
index f0e73775c107..b4986e65d8b2 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_std.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_std.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_std#

 

 

-Series.rolling_std(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]#

+Series.rolling_std(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]#
 
Compute a rolling std dev.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_sum.html b/py-polars/html/reference/series/api/polars.Series.rolling_sum.html
index 8239decd1cec..b10020b5657f 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_sum.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_sum.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_sum#

 

 

-Series.rolling_sum(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#

+Series.rolling_sum(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]#
 
Apply a rolling sum (moving sum) over the values in this array.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.rolling_var.html b/py-polars/html/reference/series/api/polars.Series.rolling_var.html
index 15150b5a1fbd..ac6d3e69871f 100644
--- a/py-polars/html/reference/series/api/polars.Series.rolling_var.html
+++ b/py-polars/html/reference/series/api/polars.Series.rolling_var.html
@@ -1628,7 +1628,7 @@
 
polars.Series.rolling_var#

 

 

-Series.rolling_var(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]#

+Series.rolling_var(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]#
 
Compute a rolling variance.

 
A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
diff --git a/py-polars/html/reference/series/api/polars.Series.round.html b/py-polars/html/reference/series/api/polars.Series.round.html
index 5660e048cd18..c2b0da8992ec 100644
--- a/py-polars/html/reference/series/api/polars.Series.round.html
+++ b/py-polars/html/reference/series/api/polars.Series.round.html
@@ -1628,7 +1628,7 @@
 
polars.Series.round#

 

 

-Series.round(decimals: int = 0) → Series[source]#

+Series.round(decimals: int = 0) → Series[source]#
 
Round underlying floating point data by decimals digits.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.sample.html b/py-polars/html/reference/series/api/polars.Series.sample.html
index 0d3a9d1f9523..37c94b203499 100644
--- a/py-polars/html/reference/series/api/polars.Series.sample.html
+++ b/py-polars/html/reference/series/api/polars.Series.sample.html
@@ -1628,7 +1628,7 @@
 
polars.Series.sample#

 

 

-Series.sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Series[source]#

+Series.sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Series[source]#
 
Sample from this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.search_sorted.html b/py-polars/html/reference/series/api/polars.Series.search_sorted.html
index c1d52b78bdcb..7532ee953faf 100644
--- a/py-polars/html/reference/series/api/polars.Series.search_sorted.html
+++ b/py-polars/html/reference/series/api/polars.Series.search_sorted.html
@@ -1629,7 +1629,7 @@
 
polars.Series.search_sorted#

 

 

-Series.search_sorted(element: int | float, side: SearchSortedSide = 'any') → int[source]#

+Series.search_sorted(element: int | float, side: SearchSortedSide = 'any') → int[source]#
 

 Series.search_sorted(element: Series | ndarray[Any, Any] | list[int] | list[float], side: SearchSortedSide = 'any') → Series

 
Find indices where elements should be inserted to maintain order.

diff --git a/py-polars/html/reference/series/api/polars.Series.series_equal.html b/py-polars/html/reference/series/api/polars.Series.series_equal.html
index cb5cebbea467..011cdaeb2eda 100644
--- a/py-polars/html/reference/series/api/polars.Series.series_equal.html
+++ b/py-polars/html/reference/series/api/polars.Series.series_equal.html
@@ -1628,7 +1628,7 @@
 
polars.Series.series_equal#

 

 

-Series.series_equal(other: Series, *, null_equal: bool = True, strict: bool = False) → bool[source]#

+Series.series_equal(other: Series, *, null_equal: bool = True, strict: bool = False) → bool[source]#
 
Check if series is equal with another Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.set.html b/py-polars/html/reference/series/api/polars.Series.set.html
index 1b960362e677..fac74cf0bd76 100644
--- a/py-polars/html/reference/series/api/polars.Series.set.html
+++ b/py-polars/html/reference/series/api/polars.Series.set.html
@@ -1628,7 +1628,7 @@
 
polars.Series.set#

 

 

-Series.set(filter: Series, value: int | float | str) → Series[source]#

+Series.set(filter: Series, value: int | float | str) → Series[source]#
 
Set masked values.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.set_at_idx.html b/py-polars/html/reference/series/api/polars.Series.set_at_idx.html
index c95e4b69ac30..bc03469a2236 100644
--- a/py-polars/html/reference/series/api/polars.Series.set_at_idx.html
+++ b/py-polars/html/reference/series/api/polars.Series.set_at_idx.html
@@ -1628,7 +1628,7 @@
 
polars.Series.set_at_idx#

 

 

-Series.set_at_idx(idx: Series | ndarray[Any, Any] | Sequence[int] | int, value: int | float | str | bool | Sequence[int] | Sequence[float] | Sequence[bool] | Sequence[str] | Sequence[date] | Sequence[datetime] | date | datetime | Series | None) → Series[source]#

+Series.set_at_idx(idx: Series | ndarray[Any, Any] | Sequence[int] | int, value: int | float | str | bool | Sequence[int] | Sequence[float] | Sequence[bool] | Sequence[str] | Sequence[date] | Sequence[datetime] | date | datetime | Series | None) → Series[source]#
 
Set values at the index locations.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.set_sorted.html b/py-polars/html/reference/series/api/polars.Series.set_sorted.html
index bbc407453e74..d64a69671b13 100644
--- a/py-polars/html/reference/series/api/polars.Series.set_sorted.html
+++ b/py-polars/html/reference/series/api/polars.Series.set_sorted.html
@@ -1628,7 +1628,7 @@
 
polars.Series.set_sorted#

 

 

-Series.set_sorted(*, descending: bool = False) → Self[source]#

+Series.set_sorted(*, descending: bool = False) → Self[source]#
 
Flags the Series as ‘sorted’.

 
Enables downstream code to user fast paths for sorted arrays.

 

diff --git a/py-polars/html/reference/series/api/polars.Series.shift.html b/py-polars/html/reference/series/api/polars.Series.shift.html
index 495bcffbc3d0..989935db1d0d 100644
--- a/py-polars/html/reference/series/api/polars.Series.shift.html
+++ b/py-polars/html/reference/series/api/polars.Series.shift.html
@@ -1628,7 +1628,7 @@
 
polars.Series.shift#

 

 

-Series.shift(periods: int = 1) → Series[source]#

+Series.shift(periods: int = 1) → Series[source]#
 
Shift the values by a given period.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.shift_and_fill.html b/py-polars/html/reference/series/api/polars.Series.shift_and_fill.html
index 32cfe382773f..311601fb76ec 100644
--- a/py-polars/html/reference/series/api/polars.Series.shift_and_fill.html
+++ b/py-polars/html/reference/series/api/polars.Series.shift_and_fill.html
@@ -1628,7 +1628,7 @@
 
polars.Series.shift_and_fill#

 

 

-Series.shift_and_fill(fill_value: int | Expr, *, periods: int = 1) → Series[source]#

+Series.shift_and_fill(fill_value: int | Expr, *, periods: int = 1) → Series[source]#
 
Shift the values by a given period and fill the resulting null values.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.shrink_dtype.html b/py-polars/html/reference/series/api/polars.Series.shrink_dtype.html
index 7e9eac24a3be..ad1eb2047391 100644
--- a/py-polars/html/reference/series/api/polars.Series.shrink_dtype.html
+++ b/py-polars/html/reference/series/api/polars.Series.shrink_dtype.html
@@ -1628,7 +1628,7 @@
 
polars.Series.shrink_dtype#

 

 

-Series.shrink_dtype() → Series[source]#

+Series.shrink_dtype() → Series[source]#
 
Shrink numeric columns to the minimal required datatype.

 
Shrink to the dtype needed to fit the extrema of this [Series].
 This can be used to reduce memory pressure.

diff --git a/py-polars/html/reference/series/api/polars.Series.shrink_to_fit.html b/py-polars/html/reference/series/api/polars.Series.shrink_to_fit.html
index 3ae8070f4dfe..586c7ec8f6f0 100644
--- a/py-polars/html/reference/series/api/polars.Series.shrink_to_fit.html
+++ b/py-polars/html/reference/series/api/polars.Series.shrink_to_fit.html
@@ -1628,7 +1628,7 @@
 
polars.Series.shrink_to_fit#

 

 

-Series.shrink_to_fit(*, in_place: bool = False) → Series[source]#

+Series.shrink_to_fit(*, in_place: bool = False) → Series[source]#
 
Shrink Series memory usage.

 
Shrinks the underlying array capacity to exactly fit the actual data.
 (Note that this function does not change the Series data type).

diff --git a/py-polars/html/reference/series/api/polars.Series.shuffle.html b/py-polars/html/reference/series/api/polars.Series.shuffle.html
index a31a33414dfe..8908517cbfc1 100644
--- a/py-polars/html/reference/series/api/polars.Series.shuffle.html
+++ b/py-polars/html/reference/series/api/polars.Series.shuffle.html
@@ -1628,7 +1628,7 @@
 
polars.Series.shuffle#

 

 

-Series.shuffle(seed: int | None = None) → Series[source]#

+Series.shuffle(seed: int | None = None) → Series[source]#
 
Shuffle the contents of this Series.

 

 
Parameters:

diff --git a/py-polars/html/reference/series/api/polars.Series.sign.html b/py-polars/html/reference/series/api/polars.Series.sign.html
index 33cb0d939584..05b374ba06f6 100644
--- a/py-polars/html/reference/series/api/polars.Series.sign.html
+++ b/py-polars/html/reference/series/api/polars.Series.sign.html
@@ -1628,7 +1628,7 @@
 
polars.Series.sign#

 

 

-Series.sign() → Series[source]#

+Series.sign() → Series[source]#
 
Compute the element-wise indication of the sign.

 
The returned values can be -1, 0, or 1:

 
    
diff --git a/py-polars/html/reference/series/api/polars.Series.sin.html b/py-polars/html/reference/series/api/polars.Series.sin.html
index 93f13d54f251..b131fef672d8 100644
--- a/py-polars/html/reference/series/api/polars.Series.sin.html
+++ b/py-polars/html/reference/series/api/polars.Series.sin.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.sin#
    
 
    
 
    
-Series.sin() → Series[source]#
    
+Series.sin() → Series[source]#
 
    Compute the element-wise value for the sine.
    
 
    Examples
    
 
    >>> import math
diff --git a/py-polars/html/reference/series/api/polars.Series.sinh.html b/py-polars/html/reference/series/api/polars.Series.sinh.html
index abaafd0cdf46..9abc85ed76ca 100644
--- a/py-polars/html/reference/series/api/polars.Series.sinh.html
+++ b/py-polars/html/reference/series/api/polars.Series.sinh.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.sinh#
    
 
    
 
    
-Series.sinh() → Series[source]#
    
+Series.sinh() → Series[source]#
 
    Compute the element-wise value for the hyperbolic sine.
    
 
    Examples
    
 
    >>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.skew.html b/py-polars/html/reference/series/api/polars.Series.skew.html
index 24399a209eb5..a5f92f733038 100644
--- a/py-polars/html/reference/series/api/polars.Series.skew.html
+++ b/py-polars/html/reference/series/api/polars.Series.skew.html
@@ -1629,7 +1629,7 @@
 
    polars.Series.skew#
    
 
    
 
    
-Series.skew(*, bias: bool = True) → float | None[source]#
    
+Series.skew(*, bias: bool = True) → float | None[source]#
 
    Compute the sample skewness of a data set.
    
 
    For normally distributed data, the skewness should be about zero. For
 unimodal continuous distributions, a skewness value greater than zero means
diff --git a/py-polars/html/reference/series/api/polars.Series.slice.html b/py-polars/html/reference/series/api/polars.Series.slice.html
index f3c38b65842d..f0afa1611c6e 100644
--- a/py-polars/html/reference/series/api/polars.Series.slice.html
+++ b/py-polars/html/reference/series/api/polars.Series.slice.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.slice#
    
 
    
 
    
-Series.slice(offset: int, length: int | None = None) → Series[source]#
    
+Series.slice(offset: int, length: int | None = None) → Series[source]#
 
    Get a slice of this Series.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.sort.html b/py-polars/html/reference/series/api/polars.Series.sort.html
index 715d0f120a5e..98a3617951e5 100644
--- a/py-polars/html/reference/series/api/polars.Series.sort.html
+++ b/py-polars/html/reference/series/api/polars.Series.sort.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.sort#
    
 
    
 
    
-Series.sort(*, descending: bool = False, in_place: bool = False) → Self[source]#
    
+Series.sort(*, descending: bool = False, in_place: bool = False) → Self[source]#
 
    Sort this Series.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.sqrt.html b/py-polars/html/reference/series/api/polars.Series.sqrt.html
index b81f991c338e..07e2ac38d351 100644
--- a/py-polars/html/reference/series/api/polars.Series.sqrt.html
+++ b/py-polars/html/reference/series/api/polars.Series.sqrt.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.sqrt#
    
 
    
 
    
-Series.sqrt() → Series[source]#
    
+Series.sqrt() → Series[source]#
 
    Compute the square root of the elements.
    
 
    Syntactic sugar for
    
 
    >>> pl.Series([1, 2]) ** 0.5
diff --git a/py-polars/html/reference/series/api/polars.Series.std.html b/py-polars/html/reference/series/api/polars.Series.std.html
index 689c9a0d6d2d..513dabe02223 100644
--- a/py-polars/html/reference/series/api/polars.Series.std.html
+++ b/py-polars/html/reference/series/api/polars.Series.std.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.std#
    
 
    
 
    
-Series.std(ddof: int = 1) → float | None[source]#
    
+Series.std(ddof: int = 1) → float | None[source]#
 
    Get the standard deviation of this Series.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.concat.html b/py-polars/html/reference/series/api/polars.Series.str.concat.html
index 02da642d09bb..a32d6b6f3850 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.concat.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.concat.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.concat#
    
 
    
 
    
-Series.str.concat(delimiter: str = '-') → Series[source]#
    
+Series.str.concat(delimiter: str = '-') → Series[source]#
 
    Vertically concat the values in the Series to a single string value.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.contains.html b/py-polars/html/reference/series/api/polars.Series.str.contains.html
index 78d3b0e40a44..2dd5c51fb0f0 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.contains.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.contains.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.contains#
    
 
    
 
    
-Series.str.contains(pattern: str | Expr, *, literal: bool = False, strict: bool = True) → Series[source]#
    
+Series.str.contains(pattern: str | Expr, *, literal: bool = False, strict: bool = True) → Series[source]#
 
    Check if strings in Series contain a substring that matches a regex.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.count_match.html b/py-polars/html/reference/series/api/polars.Series.str.count_match.html
index 7e2ee87020de..6830c31ca486 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.count_match.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.count_match.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.count_match#
    
 
    
 
    
-Series.str.count_match(pattern: str) → Series[source]#
    
+Series.str.count_match(pattern: str) → Series[source]#
 
    Count all successive non-overlapping regex matches.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.decode.html b/py-polars/html/reference/series/api/polars.Series.str.decode.html
index d6adf134f6c4..b253a684b031 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.decode.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.decode.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.decode#
    
 
    
 
    
-Series.str.decode(encoding: TransferEncoding, *, strict: bool = True) → Series[source]#
    
+Series.str.decode(encoding: TransferEncoding, *, strict: bool = True) → Series[source]#
 
    Decode a value using the provided encoding.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.encode.html b/py-polars/html/reference/series/api/polars.Series.str.encode.html
index 003c33d26fde..564741b88ef8 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.encode.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.encode.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.encode#
    
 
    
 
    
-Series.str.encode(encoding: TransferEncoding) → Series[source]#
    
+Series.str.encode(encoding: TransferEncoding) → Series[source]#
 
    Encode a value using the provided encoding.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.ends_with.html b/py-polars/html/reference/series/api/polars.Series.str.ends_with.html
index 9ccc9e484ad4..36d92c76582a 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.ends_with.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.ends_with.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.ends_with#
    
 
    
 
    
-Series.str.ends_with(suffix: str | Expr) → Series[source]#
    
+Series.str.ends_with(suffix: str | Expr) → Series[source]#
 
    Check if string values end with a substring.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.explode.html b/py-polars/html/reference/series/api/polars.Series.str.explode.html
index 8364d11accec..52d5ae61ae05 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.explode.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.explode.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.explode#
    
 
    
 
    
-Series.str.explode() → Series[source]#
    
+Series.str.explode() → Series[source]#
 
    Returns a column with a separate row for every string character.
    
 
    
 
    Returns:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.extract.html b/py-polars/html/reference/series/api/polars.Series.str.extract.html
index 849c5fa13291..4f7ee37e2784 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.extract.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.extract.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.extract#
    
 
    
 
    
-Series.str.extract(pattern: str, group_index: int = 1) → Series[source]#
    
+Series.str.extract(pattern: str, group_index: int = 1) → Series[source]#
 
    Extract the target capture group from provided patterns.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.extract_all.html b/py-polars/html/reference/series/api/polars.Series.str.extract_all.html
index 2a20d8b9aaa5..398a5f4b4c5d 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.extract_all.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.extract_all.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.extract_all#
    
 
    
 
    
-Series.str.extract_all(pattern: str | Series) → Series[source]#
    
+Series.str.extract_all(pattern: str | Series) → Series[source]#
 
    Extract all matches for the given regex pattern.
    
 
    Extract each successive non-overlapping regex match in an individual string
 as a list. Extracted matches contain null if the original value is null
diff --git a/py-polars/html/reference/series/api/polars.Series.str.json_extract.html b/py-polars/html/reference/series/api/polars.Series.str.json_extract.html
index bf4015b8b081..ea846baeedba 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.json_extract.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.json_extract.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.json_extract#
    
 
    
 
    
-Series.str.json_extract(dtype: PolarsDataType | None = None, infer_schema_length: int | None = 100) → Series[source]#
    
+Series.str.json_extract(dtype: PolarsDataType | None = None, infer_schema_length: int | None = 100) → Series[source]#
 
    Parse string values as JSON.
    
 
    Throw errors if encounter invalid JSON strings.
    
 
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.json_path_match.html b/py-polars/html/reference/series/api/polars.Series.str.json_path_match.html
index 6b5703e4006e..256796a40680 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.json_path_match.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.json_path_match.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.json_path_match#
    
 
    
 
    
-Series.str.json_path_match(json_path: str) → Series[source]#
    
+Series.str.json_path_match(json_path: str) → Series[source]#
 
    Extract the first match of json string with provided JSONPath expression.
    
 
    Throw errors if encounter invalid json strings.
 All return value will be casted to Utf8 regardless of the original value.
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.lengths.html b/py-polars/html/reference/series/api/polars.Series.str.lengths.html
index c233ef0e391a..6f3084ea0dce 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.lengths.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.lengths.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.lengths#
    
 
    
 
    
-Series.str.lengths() → Series[source]#
    
+Series.str.lengths() → Series[source]#
 
    Get length of the string values in the Series (as number of bytes).
    
 
    
 
    Returns:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.ljust.html b/py-polars/html/reference/series/api/polars.Series.str.ljust.html
index 32d12de046dc..62211cdbb586 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.ljust.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.ljust.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.ljust#
    
 
    
 
    
-Series.str.ljust(width: int, fill_char: str = ' ') → Series[source]#
    
+Series.str.ljust(width: int, fill_char: str = ' ') → Series[source]#
 
    Return the string left justified in a string of length width.
    
 
    Padding is done using the specified fill_char. The original string is
 returned if width is less than or equal to``len(s)``.
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.lstrip.html b/py-polars/html/reference/series/api/polars.Series.str.lstrip.html
index 537c6749bbfe..9529e0f44367 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.lstrip.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.lstrip.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.lstrip#
    
 
    
 
    
-Series.str.lstrip(characters: str | None = None) → Series[source]#
    
+Series.str.lstrip(characters: str | None = None) → Series[source]#
 
    Remove leading characters.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.n_chars.html b/py-polars/html/reference/series/api/polars.Series.str.n_chars.html
index abfaee15429d..3b794638156d 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.n_chars.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.n_chars.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.n_chars#
    
 
    
 
    
-Series.str.n_chars() → Series[source]#
    
+Series.str.n_chars() → Series[source]#
 
    Get length of the string values in the Series (as number of chars).
    
 
    
 
    Returns:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.parse_int.html b/py-polars/html/reference/series/api/polars.Series.str.parse_int.html
index 0daedde8d1f5..91f83ed2ab02 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.parse_int.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.parse_int.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.parse_int#
    
 
    
 
    
-Series.str.parse_int(radix: int = 2, *, strict: bool = True) → Series[source]#
    
+Series.str.parse_int(radix: int = 2, *, strict: bool = True) → Series[source]#
 
    Parse integers with base radix from strings.
    
 
    By default base 2. ParseError/Overflows become Nulls.
    
 
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.replace.html b/py-polars/html/reference/series/api/polars.Series.str.replace.html
index 0dcf2be95bf2..4b16d8fc9c38 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.replace.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.replace.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.replace#
    
 
    
 
    
-Series.str.replace(pattern: str, value: str, *, literal: bool = False, n: int = 1) → Series[source]#
    
+Series.str.replace(pattern: str, value: str, *, literal: bool = False, n: int = 1) → Series[source]#
 
    Replace first matching regex/literal substring with a new string value.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.replace_all.html b/py-polars/html/reference/series/api/polars.Series.str.replace_all.html
index ec2a0044f5bc..5f626eb3ef08 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.replace_all.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.replace_all.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.replace_all#
    
 
    
 
    
-Series.str.replace_all(pattern: str, value: str, *, literal: bool = False) → Series[source]#
    
+Series.str.replace_all(pattern: str, value: str, *, literal: bool = False) → Series[source]#
 
    Replace all matching regex/literal substrings with a new string value.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.rjust.html b/py-polars/html/reference/series/api/polars.Series.str.rjust.html
index 7ff0dd93edea..3df864822ef9 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.rjust.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.rjust.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.rjust#
    
 
    
 
    
-Series.str.rjust(width: int, fill_char: str = ' ') → Series[source]#
    
+Series.str.rjust(width: int, fill_char: str = ' ') → Series[source]#
 
    Return the string right justified in a string of length width.
    
 
    Padding is done using the specified fill_char. The original string is
 returned if width is less than or equal to len(s).
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.rstrip.html b/py-polars/html/reference/series/api/polars.Series.str.rstrip.html
index 5a08ec5d4cd3..ed1236b5204e 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.rstrip.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.rstrip.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.rstrip#
    
 
    
 
    
-Series.str.rstrip(characters: str | None = None) → Series[source]#
    
+Series.str.rstrip(characters: str | None = None) → Series[source]#
 
    Remove trailing characters.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.slice.html b/py-polars/html/reference/series/api/polars.Series.str.slice.html
index ab141cce4567..ef0a7ccf7064 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.slice.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.slice.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.slice#
    
 
    
 
    
-Series.str.slice(offset: int, length: int | None = None) → Series[source]#
    
+Series.str.slice(offset: int, length: int | None = None) → Series[source]#
 
    Create subslices of the string values of a Utf8 Series.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.split.html b/py-polars/html/reference/series/api/polars.Series.str.split.html
index d5f1e0800b92..3f466eec686f 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.split.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.split.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.split#
    
 
    
 
    
-Series.str.split(by: str, *, inclusive: bool = False) → Series[source]#
    
+Series.str.split(by: str, *, inclusive: bool = False) → Series[source]#
 
    Split the string by a substring.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.split_exact.html b/py-polars/html/reference/series/api/polars.Series.str.split_exact.html
index e166f65ded2a..07e3460faa6d 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.split_exact.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.split_exact.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.split_exact#
    
 
    
 
    
-Series.str.split_exact(by: str, n: int, *, inclusive: bool = False) → Series[source]#
    
+Series.str.split_exact(by: str, n: int, *, inclusive: bool = False) → Series[source]#
 
    Split the string by a substring using n splits.
    
 
    Results in a struct of n+1 fields.
    
 
    If it cannot make n splits, the remaining field elements will be null.
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.splitn.html b/py-polars/html/reference/series/api/polars.Series.str.splitn.html
index a3892fa49258..99b036a0ee1c 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.splitn.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.splitn.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.splitn#
    
 
    
 
    
-Series.str.splitn(by: str, n: int) → Series[source]#
    
+Series.str.splitn(by: str, n: int) → Series[source]#
 
    Split the string by a substring, restricted to returning at most n items.
    
 
    If the number of possible splits is less than n-1, the remaining field
 elements will be null. If the number of possible splits is n-1 or greater,
diff --git a/py-polars/html/reference/series/api/polars.Series.str.starts_with.html b/py-polars/html/reference/series/api/polars.Series.str.starts_with.html
index 12a69df56ec9..0e527c3a695d 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.starts_with.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.starts_with.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.starts_with#
    
 
    
 
    
-Series.str.starts_with(prefix: str | Expr) → Series[source]#
    
+Series.str.starts_with(prefix: str | Expr) → Series[source]#
 
    Check if string values start with a substring.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.strip.html b/py-polars/html/reference/series/api/polars.Series.str.strip.html
index 37da1309db9b..f9d415cb6e2b 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.strip.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.strip.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.strip#
    
 
    
 
    
-Series.str.strip(characters: str | None = None) → Series[source]#
    
+Series.str.strip(characters: str | None = None) → Series[source]#
 
    Remove leading and trailing characters.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.strptime.html b/py-polars/html/reference/series/api/polars.Series.str.strptime.html
index 36fad48803b3..902f13c0e1f8 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.strptime.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.strptime.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.strptime#
    
 
    
 
    
-Series.str.strptime(dtype: PolarsTemporalType, format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Series[source]#
    
+Series.str.strptime(dtype: PolarsTemporalType, format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Series[source]#
 
    Convert a Utf8 column into a Date/Datetime/Time column.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_date.html b/py-polars/html/reference/series/api/polars.Series.str.to_date.html
index e1d50c04ed72..63a8aaf13482 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_date.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_date.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_date#
    
 
    
 
    
-Series.str.to_date(format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True) → Series[source]#
    
+Series.str.to_date(format: str | None = None, *, strict: bool = True, exact: bool = True, cache: bool = True) → Series[source]#
 
    Convert a Utf8 column into a Date column.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_datetime.html b/py-polars/html/reference/series/api/polars.Series.str.to_datetime.html
index 9b9ad88ce688..b2464d9fdc46 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_datetime.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_datetime.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_datetime#
    
 
    
 
    
-Series.str.to_datetime(format: str | None = None, *, time_unit: TimeUnit | None = None, time_zone: str | None = None, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Series[source]#
    
+Series.str.to_datetime(format: str | None = None, *, time_unit: TimeUnit | None = None, time_zone: str | None = None, strict: bool = True, exact: bool = True, cache: bool = True, utc: bool | None = None) → Series[source]#
 
    Convert a Utf8 column into a Datetime column.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_decimal.html b/py-polars/html/reference/series/api/polars.Series.str.to_decimal.html
index 5e90abbdf71d..3b6d8d038c9b 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_decimal.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_decimal.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_decimal#
    
 
    
 
    
-Series.str.to_decimal(inference_length: int = 100) → Series[source]#
    
+Series.str.to_decimal(inference_length: int = 100) → Series[source]#
 
    Convert a Utf8 column into a Decimal column.
    
 
    This method infers the needed parameters precision and scale.
    
 
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_lowercase.html b/py-polars/html/reference/series/api/polars.Series.str.to_lowercase.html
index 52b9bc075ab9..4636befd2815 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_lowercase.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_lowercase.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_lowercase#
    
 
    
 
    
-Series.str.to_lowercase() → Series[source]#
    
+Series.str.to_lowercase() → Series[source]#
 
    Modify the strings to their lowercase equivalent.
    
 
    Examples
    
 
    >>> s = pl.Series("foo", ["CAT", "DOG"])
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_time.html b/py-polars/html/reference/series/api/polars.Series.str.to_time.html
index c97520e94668..13a8469c001e 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_time.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_time.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_time#
    
 
    
 
    
-Series.str.to_time(format: str | None = None, *, strict: bool = True, cache: bool = True) → Series[source]#
    
+Series.str.to_time(format: str | None = None, *, strict: bool = True, cache: bool = True) → Series[source]#
 
    Convert a Utf8 column into a Time column.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_titlecase.html b/py-polars/html/reference/series/api/polars.Series.str.to_titlecase.html
index d109f7a4c58a..b3d953ece2a0 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_titlecase.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_titlecase.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_titlecase#
    
 
    
 
    
-Series.str.to_titlecase() → Series[source]#
    
+Series.str.to_titlecase() → Series[source]#
 
    Modify the strings to their titlecase equivalent.
    
 
    Examples
    
 
    >>> s = pl.Series("sing", ["welcome to my world", "THERE'S NO TURNING BACK"])
diff --git a/py-polars/html/reference/series/api/polars.Series.str.to_uppercase.html b/py-polars/html/reference/series/api/polars.Series.str.to_uppercase.html
index 9de24834522f..3c4d2a9f2550 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.to_uppercase.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.to_uppercase.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.to_uppercase#
    
 
    
 
    
-Series.str.to_uppercase() → Series[source]#
    
+Series.str.to_uppercase() → Series[source]#
 
    Modify the strings to their uppercase equivalent.
    
 
    Examples
    
 
    >>> s = pl.Series("foo", ["cat", "dog"])
diff --git a/py-polars/html/reference/series/api/polars.Series.str.zfill.html b/py-polars/html/reference/series/api/polars.Series.str.zfill.html
index 485cc2fe1af6..bf15bc2f533e 100644
--- a/py-polars/html/reference/series/api/polars.Series.str.zfill.html
+++ b/py-polars/html/reference/series/api/polars.Series.str.zfill.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.str.zfill#
    
 
    
 
    
-Series.str.zfill(alignment: int) → Series[source]#
    
+Series.str.zfill(alignment: int) → Series[source]#
 
    Fills the string with zeroes.
    
 
    Return a copy of the string left filled with ASCII ‘0’ digits to make a string
 of length width.
    
diff --git a/py-polars/html/reference/series/api/polars.Series.sum.html b/py-polars/html/reference/series/api/polars.Series.sum.html
index 3d61fe9de7d8..5b56836c4a0f 100644
--- a/py-polars/html/reference/series/api/polars.Series.sum.html
+++ b/py-polars/html/reference/series/api/polars.Series.sum.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.sum#
    
 
    
 
    
-Series.sum() → int | float[source]#
    
+Series.sum() → int | float[source]#
 
    Reduce this Series to the sum value.
    
 
    Notes
    
 
    Dtypes in {Int8, UInt8, Int16, UInt16} are cast to
diff --git a/py-polars/html/reference/series/api/polars.Series.tail.html b/py-polars/html/reference/series/api/polars.Series.tail.html
index d76e4e8dc32a..53b496e22364 100644
--- a/py-polars/html/reference/series/api/polars.Series.tail.html
+++ b/py-polars/html/reference/series/api/polars.Series.tail.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.tail#
    
 
    
 
    
-Series.tail(n: int = 10) → Series[source]#
    
+Series.tail(n: int = 10) → Series[source]#
 
    Get the last n elements.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.take.html b/py-polars/html/reference/series/api/polars.Series.take.html
index aab745c3291a..731f563f7bab 100644
--- a/py-polars/html/reference/series/api/polars.Series.take.html
+++ b/py-polars/html/reference/series/api/polars.Series.take.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.take#
    
 
    
 
    
-Series.take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) → Series[source]#
    
+Series.take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) → Series[source]#
 
    Take values by index.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.take_every.html b/py-polars/html/reference/series/api/polars.Series.take_every.html
index 587a1eb8b56f..289c5d4ea1af 100644
--- a/py-polars/html/reference/series/api/polars.Series.take_every.html
+++ b/py-polars/html/reference/series/api/polars.Series.take_every.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.take_every#
    
 
    
 
    
-Series.take_every(n: int) → Series[source]#
    
+Series.take_every(n: int) → Series[source]#
 
    Take every nth value in the Series and return as new Series.
    
 
    Examples
    
 
    >>> s = pl.Series("a", [1, 2, 3, 4])
diff --git a/py-polars/html/reference/series/api/polars.Series.tan.html b/py-polars/html/reference/series/api/polars.Series.tan.html
index b6fc25cd2578..1802de9664c9 100644
--- a/py-polars/html/reference/series/api/polars.Series.tan.html
+++ b/py-polars/html/reference/series/api/polars.Series.tan.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.tan#
    
 
    
 
    
-Series.tan() → Series[source]#
    
+Series.tan() → Series[source]#
 
    Compute the element-wise value for the tangent.
    
 
    Examples
    
 
    >>> import math
diff --git a/py-polars/html/reference/series/api/polars.Series.tanh.html b/py-polars/html/reference/series/api/polars.Series.tanh.html
index 8a950b147099..552af018b6b2 100644
--- a/py-polars/html/reference/series/api/polars.Series.tanh.html
+++ b/py-polars/html/reference/series/api/polars.Series.tanh.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.tanh#
    
 
    
 
    
-Series.tanh() → Series[source]#
    
+Series.tanh() → Series[source]#
 
    Compute the element-wise value for the hyperbolic tangent.
    
 
    Examples
    
 
    >>> s = pl.Series("a", [1.0, 0.0, -1.0])
diff --git a/py-polars/html/reference/series/api/polars.Series.to_arrow.html b/py-polars/html/reference/series/api/polars.Series.to_arrow.html
index b70594688495..b9f7f28bdc08 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_arrow.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_arrow.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_arrow#
    
 
    
 
    
-Series.to_arrow() → Array[source]#
    
+Series.to_arrow() → Array[source]#
 
    Get the underlying Arrow Array.
    
 
    If the Series contains only a single chunk this operation is zero copy.
    
 
    Examples
    
diff --git a/py-polars/html/reference/series/api/polars.Series.to_dummies.html b/py-polars/html/reference/series/api/polars.Series.to_dummies.html
index b9720d02e964..1443a57b7f68 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_dummies.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_dummies.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_dummies#
    
 
    
 
    
-Series.to_dummies(separator: str = '_') → DataFrame[source]#
    
+Series.to_dummies(separator: str = '_') → DataFrame[source]#
 
    Get dummy/indicator variables.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.to_frame.html b/py-polars/html/reference/series/api/polars.Series.to_frame.html
index fbfe8e507abe..4456ebfcfc5d 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_frame.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_frame.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_frame#
    
 
    
 
    
-Series.to_frame(name: str | None = None) → DataFrame[source]#
    
+Series.to_frame(name: str | None = None) → DataFrame[source]#
 
    Cast this Series to a DataFrame.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.to_init_repr.html b/py-polars/html/reference/series/api/polars.Series.to_init_repr.html
index ad64f2fe89fc..775feaa16f1b 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_init_repr.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_init_repr.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_init_repr#
    
 
    
 
    
-Series.to_init_repr(n: int = 1000) → str[source]#
    
+Series.to_init_repr(n: int = 1000) → str[source]#
 
    Convert Series to instantiatable string representation.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.to_list.html b/py-polars/html/reference/series/api/polars.Series.to_list.html
index b6079ca637c8..0b607d798e30 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_list.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_list.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_list#
    
 
    
 
    
-Series.to_list(*, use_pyarrow: bool = False) → list[Any][source]#
    
+Series.to_list(*, use_pyarrow: bool = False) → list[Any][source]#
 
    Convert this Series to a Python List. This operation clones data.
    
 
    
 
    Parameters:
    
diff --git a/py-polars/html/reference/series/api/polars.Series.to_numpy.html b/py-polars/html/reference/series/api/polars.Series.to_numpy.html
index 2239bf16b74b..19eb805dc5a3 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_numpy.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_numpy.html
@@ -1628,7 +1628,7 @@
 
    polars.Series.to_numpy#
    
 
    
 
    
-Series.to_numpy(*args: Any, zero_copy_only: bool = False, writable: bool = False, use_pyarrow: bool = True) → ndarray[Any, Any][source]#
    
+Series.to_numpy(*args: Any, zero_copy_only: bool = False, writable: bool = False, use_pyarrow: bool = True) → ndarray[Any, Any][source]#
 
    Convert this Series to numpy.
    
 
    This operation may clone data but is completely safe. Note that:
    
 
      
diff --git a/py-polars/html/reference/series/api/polars.Series.to_pandas.html b/py-polars/html/reference/series/api/polars.Series.to_pandas.html
index ccb1eab8c3db..564afe99d176 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_pandas.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_pandas.html
@@ -1628,7 +1628,7 @@
 
      polars.Series.to_pandas#
      
 
      
 
      
-Series.to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → pd.Series[Any][source]#
      
+Series.to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → pd.Series[Any][source]#
 
      Convert this Series to a pandas Series.
      
 
      This requires that pandas and pyarrow are installed.
 This operation clones data, unless use_pyarrow_extension_array=True.
      
diff --git a/py-polars/html/reference/series/api/polars.Series.to_physical.html b/py-polars/html/reference/series/api/polars.Series.to_physical.html
index c2c810c560e7..d421d64f349c 100644
--- a/py-polars/html/reference/series/api/polars.Series.to_physical.html
+++ b/py-polars/html/reference/series/api/polars.Series.to_physical.html
@@ -1628,7 +1628,7 @@
 
      polars.Series.to_physical#
      
 
      
 
      
-Series.to_physical() → Series[source]#
      
+Series.to_physical() → Series[source]#
 
      Cast to physical representation of the logical dtype.
      
 
        
 
      • polars.datatypes.Date() -> polars.datatypes.Int32()
        • 
diff --git a/py-polars/html/reference/series/api/polars.Series.top_k.html b/py-polars/html/reference/series/api/polars.Series.top_k.html
index a6ab7a41d17b..d9b9ad08890f 100644
--- a/py-polars/html/reference/series/api/polars.Series.top_k.html
+++ b/py-polars/html/reference/series/api/polars.Series.top_k.html
@@ -1629,7 +1629,7 @@
 
        polars.Series.top_k#
        
 
        
 
        
-Series.top_k(k: int = 5) → Series[source]#
        
+Series.top_k(k: int = 5) → Series[source]#
 
        Return the k largest elements.
        
 
        This has time complexity:
        
 
        
diff --git a/py-polars/html/reference/series/api/polars.Series.unique.html b/py-polars/html/reference/series/api/polars.Series.unique.html
index f6b6059403d6..3cd3a5070c44 100644
--- a/py-polars/html/reference/series/api/polars.Series.unique.html
+++ b/py-polars/html/reference/series/api/polars.Series.unique.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.unique#
        
 
        
 
        
-Series.unique(*, maintain_order: bool = False) → Series[source]#
        
+Series.unique(*, maintain_order: bool = False) → Series[source]#
 
        Get unique elements in series.
        
 
        
 
        Parameters:
        
diff --git a/py-polars/html/reference/series/api/polars.Series.unique_counts.html b/py-polars/html/reference/series/api/polars.Series.unique_counts.html
index e3f848084b0c..ee35b0aaa1b5 100644
--- a/py-polars/html/reference/series/api/polars.Series.unique_counts.html
+++ b/py-polars/html/reference/series/api/polars.Series.unique_counts.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.unique_counts#
        
 
        
 
        
-Series.unique_counts() → Series[source]#
        
+Series.unique_counts() → Series[source]#
 
        Return a count of the unique values in the order of appearance.
        
 
        Examples
        
 
        >>> s = pl.Series("id", ["a", "b", "b", "c", "c", "c"])
diff --git a/py-polars/html/reference/series/api/polars.Series.upper_bound.html b/py-polars/html/reference/series/api/polars.Series.upper_bound.html
index 72fabc3428ce..caa7f089d00e 100644
--- a/py-polars/html/reference/series/api/polars.Series.upper_bound.html
+++ b/py-polars/html/reference/series/api/polars.Series.upper_bound.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.upper_bound#
        
 
        
 
        
-Series.upper_bound() → Self[source]#
        
+Series.upper_bound() → Self[source]#
 
        Return the upper bound of this Series’ dtype as a unit Series.
        
 
        
 
        See also
        
diff --git a/py-polars/html/reference/series/api/polars.Series.value_counts.html b/py-polars/html/reference/series/api/polars.Series.value_counts.html
index d01c056ecde1..629987dc84f1 100644
--- a/py-polars/html/reference/series/api/polars.Series.value_counts.html
+++ b/py-polars/html/reference/series/api/polars.Series.value_counts.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.value_counts#
        
 
        
 
        
-Series.value_counts(*, sort: bool = False) → DataFrame[source]#
        
+Series.value_counts(*, sort: bool = False) → DataFrame[source]#
 
        Count the unique values in a Series.
        
 
        
 
        Parameters:
        
diff --git a/py-polars/html/reference/series/api/polars.Series.var.html b/py-polars/html/reference/series/api/polars.Series.var.html
index a836f7f3b611..f93030ce1de1 100644
--- a/py-polars/html/reference/series/api/polars.Series.var.html
+++ b/py-polars/html/reference/series/api/polars.Series.var.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.var#
        
 
        
 
        
-Series.var(ddof: int = 1) → float | None[source]#
        
+Series.var(ddof: int = 1) → float | None[source]#
 
        Get variance of this Series.
        
 
        
 
        Parameters:
        
diff --git a/py-polars/html/reference/series/api/polars.Series.view.html b/py-polars/html/reference/series/api/polars.Series.view.html
index e951ba57bead..13cf7cc608c2 100644
--- a/py-polars/html/reference/series/api/polars.Series.view.html
+++ b/py-polars/html/reference/series/api/polars.Series.view.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.view#
        
 
        
 
        
-Series.view(*, ignore_nulls: bool = False) → SeriesView[source]#
        
+Series.view(*, ignore_nulls: bool = False) → SeriesView[source]#
 
        Get a view into this Series data with a numpy array.
        
 
        This operation doesn’t clone data, but does not include missing values.
 Don’t use this unless you know what you are doing.
        
diff --git a/py-polars/html/reference/series/api/polars.Series.zip_with.html b/py-polars/html/reference/series/api/polars.Series.zip_with.html
index bf30869ad89e..83da9d02eec8 100644
--- a/py-polars/html/reference/series/api/polars.Series.zip_with.html
+++ b/py-polars/html/reference/series/api/polars.Series.zip_with.html
@@ -1628,7 +1628,7 @@
 
        polars.Series.zip_with#
        
 
        
 
        
-Series.zip_with(mask: Series, other: Series) → Self[source]#
        
+Series.zip_with(mask: Series, other: Series) → Self[source]#
 
        Take values from self or other based on the given mask.
        
 
        Where mask evaluates true, take values from self. Where mask evaluates false,
 take values from other.
        
diff --git a/py-polars/html/reference/series/index.html b/py-polars/html/reference/series/index.html
index bd7f3c126681..769345c49fc9 100644
--- a/py-polars/html/reference/series/index.html
+++ b/py-polars/html/reference/series/index.html
@@ -1620,7 +1620,7 @@ 
        Series
 
        
 
        
-class polars.Series(name: str | ArrayLike | None = None, values: ArrayLike | None = None, dtype: PolarsDataType | None = None, *, strict: bool = True, nan_to_null: bool = False, dtype_if_empty: PolarsDataType | None = None)[source]
        
+class polars.Series(name: str | ArrayLike | None = None, values: ArrayLike | None = None, dtype: PolarsDataType | None = None, *, strict: bool = True, nan_to_null: bool = False, dtype_if_empty: PolarsDataType | None = None)[source]
 
        A Series represents a single column in a polars DataFrame.
        
 
        
 
        Parameters:
        
@@ -2218,14 +2218,14 @@ 
        Series
 
        
 
        
-abs() → Series[source]
        
+abs() → Series[source]
 
        Compute absolute values.
        
 
        Same as abs(series).
        
 
        
 
 
        
 
        
-alias(name: str) → Series[source]
        
+alias(name: str) → Series[source]
 
        Rename the series.
        
 
        
 
        Parameters:
        
@@ -2251,7 +2251,7 @@ 
        Series
 
        
-all(drop_nulls: bool = True) → bool | None[source]
        
+all(drop_nulls: bool = True) → bool | None[source]
 
        Check if all boolean values in the column are True.
        
 
        
 
        Returns:
        
@@ -2265,7 +2265,7 @@ 
        Series
 
        
-any(drop_nulls: bool = True) → bool | None[source]
        
+any(drop_nulls: bool = True) → bool | None[source]
 
        Check if any boolean value in the column is True.
        
 
        
 
        Returns:
        
@@ -2279,7 +2279,7 @@ 
        Series
 
        
-append(other: Series, *, append_chunks: bool | None = None) → Self[source]
        
+append(other: Series, *, append_chunks: bool | None = None) → Self[source]
 
        Append a Series to this one.
        
 
        
 
        Parameters:
        
@@ -2347,7 +2347,7 @@ 
        Series
 
        
-apply(function: Callable[[Any], Any], return_dtype: PolarsDataType | None = None, *, skip_nulls: bool = True) → Self[source]
        
+apply(function: Callable[[Any], Any], return_dtype: PolarsDataType | None = None, *, skip_nulls: bool = True) → Self[source]
 
        Apply a custom/user-defined function (UDF) over elements in this Series.
        
 
        
 
        Warning
        
@@ -2412,7 +2412,7 @@ 
        Series
 
        
-arccos() → Series[source]
        
+arccos() → Series[source]
 
        Compute the element-wise value for the inverse cosine.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -2430,7 +2430,7 @@ 
        Series
 
        
-arccosh() → Series[source]
        
+arccosh() → Series[source]
 
        Compute the element-wise value for the inverse hyperbolic cosine.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [5.0, 1.0, 0.0, -1.0])
@@ -2449,7 +2449,7 @@ 
        Series
 
        
-arcsin() → Series[source]
        
+arcsin() → Series[source]
 
        Compute the element-wise value for the inverse sine.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -2467,7 +2467,7 @@ 
        Series
 
        
-arcsinh() → Series[source]
        
+arcsinh() → Series[source]
 
        Compute the element-wise value for the inverse hyperbolic sine.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -2485,7 +2485,7 @@ 
        Series
 
        
-arctan() → Series[source]
        
+arctan() → Series[source]
 
        Compute the element-wise value for the inverse tangent.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -2503,7 +2503,7 @@ 
        Series
 
        
-arctanh() → Series[source]
        
+arctanh() → Series[source]
 
        Compute the element-wise value for the inverse hyperbolic tangent.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [2.0, 1.0, 0.5, 0.0, -0.5, -1.0, -1.1])
@@ -2525,7 +2525,7 @@ 
        Series
 
        
-arg_max() → int | None[source]
        
+arg_max() → int | None[source]
 
        Get the index of the maximal value.
        
 
        
 
        Returns:
        
@@ -2544,7 +2544,7 @@ 
        Series
 
        
-arg_min() → int | None[source]
        
+arg_min() → int | None[source]
 
        Get the index of the minimal value.
        
 
        
 
        Returns:
        
@@ -2563,7 +2563,7 @@ 
        Series
 
        
-arg_sort(*, descending: bool = False, nulls_last: bool = False) → Series[source]
        
+arg_sort(*, descending: bool = False, nulls_last: bool = False) → Series[source]
 
        Get the index values that would sort this Series.
        
 
        
 
        Parameters:
        
@@ -2593,7 +2593,7 @@ 
        Series
 
        
-arg_true() → Series[source]
        
+arg_true() → Series[source]
 
        Get index values where Boolean Series evaluate True.
        
 
        
 
        Returns:
        
@@ -2617,7 +2617,7 @@ 
        Series
 
        
-arg_unique() → Series[source]
        
+arg_unique() → Series[source]
 
        Get unique index as Series.
        
 
        
 
        Returns:
        
@@ -2642,7 +2642,7 @@ 
        Series
 
        
-bottom_k(k: int = 5) → Series[source]
        
+bottom_k(k: int = 5) → Series[source]
 
        Return the k smallest elements.
        
 
        This has time complexity:
        
 
        
@@ -2677,7 +2677,7 @@ 
        Series
 
        
-cast(dtype: PolarsDataType | type[int] | type[float] | type[str] | type[bool], *, strict: bool = True) → Self[source]
        
+cast(dtype: PolarsDataType | type[int] | type[float] | type[str] | type[bool], *, strict: bool = True) → Self[source]
 
        Cast between data types.
        
 
        
 
        Parameters:
        
@@ -2715,7 +2715,7 @@ 
        Series
 
        
-cbrt() → Series[source]
        
+cbrt() → Series[source]
 
        Compute the cube root of the elements.
        
 
        Optimization for
        
 
        >>> pl.Series([1, 2]) ** (1.0 / 3)
@@ -2731,7 +2731,7 @@ 
        Series
 
        
-ceil() → Series[source]
        
+ceil() → Series[source]
 
        Rounds up to the nearest integer value.
        
 
        Only works on floating point Series.
        
 
        Examples
        
@@ -2750,7 +2750,7 @@ 
        Series
 
        
-chunk_lengths() → list[int][source]
        
+chunk_lengths() → list[int][source]
 
        Get the length of each individual chunk.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -2771,7 +2771,7 @@ 
        Series
 
        
-clear(n: int = 0) → Series[source]
        
+clear(n: int = 0) → Series[source]
 
        Create an empty copy of the current Series, with zero to ‘n’ elements.
        
 
        The copy has an identical name/dtype, but no data.
        
 
        
@@ -2811,7 +2811,7 @@ 
        Series
 
        
-clip(lower_bound: int | float, upper_bound: int | float) → Series[source]
        
+clip(lower_bound: int | float, upper_bound: int | float) → Series[source]
 
        Clip (limit) the values in an array to a min and max boundary.
        
 
        Only works for numerical types.
        
 
        If you want to clip other dtypes, consider writing a “when, then, otherwise”
@@ -2843,7 +2843,7 @@ 
        Series
 
        
-clip_max(upper_bound: int | float) → Series[source]
        
+clip_max(upper_bound: int | float) → Series[source]
 
        Clip (limit) the values in an array to a max boundary.
        
 
        Only works for numerical types.
        
 
        If you want to clip other dtypes, consider writing a “when, then, otherwise”
@@ -2860,7 +2860,7 @@ 
        Series
 
        
-clip_min(lower_bound: int | float) → Series[source]
        
+clip_min(lower_bound: int | float) → Series[source]
 
        Clip (limit) the values in an array to a min boundary.
        
 
        Only works for numerical types.
        
 
        If you want to clip other dtypes, consider writing a “when, then, otherwise”
@@ -2877,7 +2877,7 @@ 
        Series
 
        
-clone() → Self[source]
        
+clone() → Self[source]
 
        Very cheap deepcopy/clone.
        
 
        
 
        See also
        
@@ -2902,7 +2902,7 @@ 
        Series
 
        
-cos() → Series[source]
        
+cos() → Series[source]
 
        Compute the element-wise value for the cosine.
        
 
        Examples
        
 
        >>> import math
@@ -2921,7 +2921,7 @@ 
        Series
 
        
-cosh() → Series[source]
        
+cosh() → Series[source]
 
        Compute the element-wise value for the hyperbolic cosine.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -2939,7 +2939,7 @@ 
        Series
 
        
-cummax(*, reverse: bool = False) → Series[source]
        
+cummax(*, reverse: bool = False) → Series[source]
 
        Get an array with the cumulative max computed at every element.
        
 
        
 
        Parameters:
        
@@ -2965,7 +2965,7 @@ 
        Series
 
        
-cummin(*, reverse: bool = False) → Series[source]
        
+cummin(*, reverse: bool = False) → Series[source]
 
        Get an array with the cumulative min computed at every element.
        
 
        
 
        Parameters:
        
@@ -2991,7 +2991,7 @@ 
        Series
 
        
-cumprod(*, reverse: bool = False) → Series[source]
        
+cumprod(*, reverse: bool = False) → Series[source]
 
        Get an array with the cumulative product computed at every element.
        
 
        
 
        Parameters:
        
@@ -3020,7 +3020,7 @@ 
        Series
 
        
-cumsum(*, reverse: bool = False) → Series[source]
        
+cumsum(*, reverse: bool = False) → Series[source]
 
        Get an array with the cumulative sum computed at every element.
        
 
        
 
        Parameters:
        
@@ -3049,7 +3049,7 @@ 
        Series
 
        
-cumulative_eval(expr: Expr, min_periods: int = 1, *, parallel: bool = False) → Series[source]
        
+cumulative_eval(expr: Expr, min_periods: int = 1, *, parallel: bool = False) → Series[source]
 
        Run an expression over a sliding window that increases 1 slot every iteration.
        
 
        
 
        Parameters:
        
@@ -3090,7 +3090,7 @@ 
        Series
 
        
-cut(breaks: list[float], labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', *, series: bool = True, left_closed: bool = False, include_breaks: bool = False) → DataFrame | Series[source]
        
+cut(breaks: list[float], labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', *, series: bool = True, left_closed: bool = False, include_breaks: bool = False) → DataFrame | Series[source]
 
        Bin continuous values into discrete categories.
        
 
        
 
        Parameters:
        
@@ -3180,7 +3180,7 @@ 
        Series
 
        
-describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → DataFrame[source]
        
+describe(percentiles: Sequence[float] | float | None = (0.25, 0.75)) → DataFrame[source]
 
        Quick summary statistics of a series.
        
 
        Series with mixed datatypes will return summary statistics for the datatype of
 the first value.
        
@@ -3238,7 +3238,7 @@ 
        Series
 
        
-diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]
        
+diff(n: int = 1, null_behavior: NullBehavior = 'ignore') → Series[source]
 
        Calculate the n-th discrete difference.
        
 
        
 
        Parameters:
        
@@ -3290,7 +3290,7 @@ 
        Series
 
        
-dot(other: Series | Sequence[Any] | Array | ChunkedArray | ndarray | Series | DatetimeIndex) → float | None[source]
        
+dot(other: Series | Sequence[Any] | Array | ChunkedArray | ndarray | Series | DatetimeIndex) → float | None[source]
 
        Compute the dot/inner product between two Series.
        
 
        
 
        Parameters:
        
@@ -3311,20 +3311,20 @@ 
        Series
 
        
-drop_nans() → Series[source]
        
+drop_nans() → Series[source]
 
        Drop NaN values.
        
 
        
 
 
        
 
        
-drop_nulls() → Series[source]
        
+drop_nulls() → Series[source]
 
        Drop all null values.
        
 
        Creates a new Series that copies data from this Series without null values.
        
 
        
 
 
        
 
        
-entropy(base: float = 2.718281828459045, *, normalize: bool = False) → float | None[source]
        
+entropy(base: float = 2.718281828459045, *, normalize: bool = False) → float | None[source]
 
        Computes the entropy.
        
 
        Uses the formula -sum(pk * log(pk) where pk are discrete probabilities.
        
 
        
@@ -3350,13 +3350,13 @@ 
        Series
 
        
-eq(other: Any) → Self | Expr[source]
        
+eq(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series == other.
        
 
        
 
 
        
 
        
-eq_missing(other: Any) → Self[source]
        
+eq_missing(other: Any) → Self[source]
 
        
 eq_missing(other: Expr) → Expr
        
 
        Method equivalent of equality operator expr == other where None == None`.
        
@@ -3373,7 +3373,7 @@ 
        Series
 
        
-estimated_size(unit: SizeUnit = 'b') → int | float[source]
        
+estimated_size(unit: SizeUnit = 'b') → int | float[source]
 
        Return an estimation of the total (heap) allocated size of the Series.
        
 
        Estimated size is given in the specified unit (bytes by default).
        
 
        This estimation is the sum of the size of its buffers, validity, including
@@ -3404,7 +3404,7 @@ 
        Series
 
        
-ewm_mean(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
        
+ewm_mean(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
 
        Exponentially-weighted moving average.
        
 
        
 
        Parameters:
        
@@ -3474,7 +3474,7 @@ 
        Series
 
        
-ewm_std(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
        
+ewm_std(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
 
        Exponentially-weighted moving standard deviation.
        
 
        
 
        Parameters:
        
@@ -3559,7 +3559,7 @@ 
        Series
 
        
-ewm_var(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
        
+ewm_var(com: float | None = None, span: float | None = None, half_life: float | None = None, alpha: float | None = None, *, adjust: bool = True, bias: bool = False, min_periods: int = 1, ignore_nulls: bool = True) → Series[source]
 
        Exponentially-weighted moving variance.
        
 
        
 
        Parameters:
        
@@ -3644,13 +3644,13 @@ 
        Series
 
        
-exp() → Series[source]
        
+exp() → Series[source]
 
        Compute the exponential, element-wise.
        
 
        
 
 
        
 
        
-explode() → Series[source]
        
+explode() → Series[source]
 
        Explode a list Series.
        
 
        This means that every item is expanded to a new row.
        
 
        
@@ -3674,7 +3674,7 @@ 
        Series
 
        
-extend(other: Series) → Self[source]
        
+extend(other: Series) → Self[source]
 
        Extend the memory backed by this Series with the values from another.
        
 
        Different from append, which adds the chunks from other to the chunks of
 this series, extend appends the data from other to the underlying memory
@@ -3731,7 +3731,7 @@ 
        Series
 
        
-extend_constant(value: PythonLiteral | None, n: int) → Series[source]
        
+extend_constant(value: PythonLiteral | None, n: int) → Series[source]
 
        Extremely fast method for extending the Series with ‘n’ copies of a value.
        
 
        
 
        Parameters:
        
@@ -3762,7 +3762,7 @@ 
        Series
 
        
-fill_nan(value: int | float | Expr | None) → Series[source]
        
+fill_nan(value: int | float | Expr | None) → Series[source]
 
        Fill floating point NaN value with a fill value.
        
 
        
 
        Parameters:
        
@@ -3789,7 +3789,7 @@ 
        Series
 
        
-fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None) → Series[source]
        
+fill_null(value: Any | None = None, strategy: FillNullStrategy | None = None, limit: int | None = None) → Series[source]
 
        Fill null values using the specified value or strategy.
        
 
        
 
        Parameters:
        
@@ -3839,7 +3839,7 @@ 
        Series
 
        
-filter(predicate: Series | list[bool]) → Self[source]
        
+filter(predicate: Series | list[bool]) → Self[source]
 
        Filter elements by a boolean mask.
        
 
        
 
        Parameters:
        
@@ -3865,7 +3865,7 @@ 
        Series
 
        
-floor() → Series[source]
        
+floor() → Series[source]
 
        Rounds down to the nearest integer value.
        
 
        Only works on floating point Series.
        
 
        Examples
        
@@ -3884,25 +3884,25 @@ 
        Series
 
        
-ge(other: Any) → Self | Expr[source]
        
+ge(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series >= other.
        
 
        
 
 
        
 
        
-get_chunks() → list[polars.series.series.Series][source]
        
+get_chunks() → list[polars.series.series.Series][source]
 
        Get the chunks of this Series as a list of Series.
        
 
        
 
 
        
 
        
-gt(other: Any) → Self | Expr[source]
        
+gt(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series > other.
        
 
        
 
 
        
 
        
-has_validity() → bool[source]
        
+has_validity() → bool[source]
 
        Return True if the Series has a validity bitmask.
        
 
        If there is none, it means that there are no null values.
 Use this to swiftly assert a Series does not have null values.
        
@@ -3910,7 +3910,7 @@ 
        Series
 
        
-hash(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]
        
+hash(seed: int = 0, seed_1: int | None = None, seed_2: int | None = None, seed_3: int | None = None) → Series[source]
 
        Hash the Series.
        
 
        The hash value is of type UInt64.
        
 
        
@@ -3943,7 +3943,7 @@ 
        Series
 
        
-head(n: int = 10) → Series[source]
        
+head(n: int = 10) → Series[source]
 
        Get the first n elements.
        
 
        
 
        Parameters:
        
@@ -3986,7 +3986,7 @@ 
        Series
 
        
-hist(bins: list[float] | None = None, *, bin_count: int | None = None) → DataFrame[source]
        
+hist(bins: list[float] | None = None, *, bin_count: int | None = None) → DataFrame[source]
 
        Bin values into buckets and count their occurrences.
        
 
        
 
        Parameters:
        
@@ -4031,13 +4031,13 @@ 
        Series
 
        
-implode() → Self[source]
        
+implode() → Self[source]
 
        Aggregate values into a list.
        
 
        
 
 
        
 
        
-interpolate(method: InterpolationMethod = 'linear') → Series[source]
        
+interpolate(method: InterpolationMethod = 'linear') → Series[source]
 
        Fill null values using interpolation.
        
 
        
 
        Parameters:
        
@@ -4065,7 +4065,7 @@ 
        Series
 
        
-is_between(lower_bound: IntoExpr, upper_bound: IntoExpr, closed: ClosedInterval = 'both') → Series[source]
        
+is_between(lower_bound: IntoExpr, upper_bound: IntoExpr, closed: ClosedInterval = 'both') → Series[source]
 
        Get a boolean mask of the values that fall between the given start/end values.
        
 
        
 
        Parameters:
        
@@ -4126,7 +4126,7 @@ 
        Series
 
        
-is_boolean() → bool[source]
        
+is_boolean() → bool[source]
 
        Check if this Series is a Boolean.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [True, False, True])
@@ -4138,7 +4138,7 @@ 
        Series
 
        
-is_duplicated() → Series[source]
        
+is_duplicated() → Series[source]
 
        Get mask of all duplicated values.
        
 
        
 
        Returns:
        
@@ -4165,7 +4165,7 @@ 
        Series
 
        
-is_empty() → bool[source]
        
+is_empty() → bool[source]
 
        Check if the Series is empty.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [], dtype=pl.Float32)
@@ -4177,7 +4177,7 @@ 
        Series
 
        
-is_finite() → Series[source]
        
+is_finite() → Series[source]
 
        Returns a boolean Series indicating which values are finite.
        
 
        
 
        Returns:
        
@@ -4204,7 +4204,7 @@ 
        Series
 
        
-is_first() → Series[source]
        
+is_first() → Series[source]
 
        Get a mask of the first unique value.
        
 
        
 
        Returns:
        
@@ -4218,7 +4218,7 @@ 
        Series
 
        
-is_float() → bool[source]
        
+is_float() → bool[source]
 
        Check if this Series has floating point numbers.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1.0, 2.0, 3.0])
@@ -4230,7 +4230,7 @@ 
        Series
 
        
-is_in(other: Series | Collection[Any]) → Series[source]
        
+is_in(other: Series | Collection[Any]) → Series[source]
 
        Check if elements of this Series are in the other Series.
        
 
        
 
        Returns:
        
@@ -4285,7 +4285,7 @@ 
        Series
 
        
-is_infinite() → Series[source]
        
+is_infinite() → Series[source]
 
        Returns a boolean Series indicating which values are infinite.
        
 
        
 
        Returns:
        
@@ -4312,7 +4312,7 @@ 
        Series
 
        
-is_integer(signed: bool | None = None) → bool[source]
        
+is_integer(signed: bool | None = None) → bool[source]
 
        Check if this Series datatype is an integer (signed or unsigned).
        
 
        
 
        Parameters:
        
@@ -4340,7 +4340,7 @@ 
        Series
 
        
-is_nan() → Series[source]
        
+is_nan() → Series[source]
 
        Returns a boolean Series indicating which values are not NaN.
        
 
        
 
        Returns:
        
@@ -4368,7 +4368,7 @@ 
        Series
 
        
-is_not_nan() → Series[source]
        
+is_not_nan() → Series[source]
 
        Returns a boolean Series indicating which values are not NaN.
        
 
        
 
        Returns:
        
@@ -4396,7 +4396,7 @@ 
        Series
 
        
-is_not_null() → Series[source]
        
+is_not_null() → Series[source]
 
        Returns a boolean Series indicating which values are not null.
        
 
        
 
        Returns:
        
@@ -4423,7 +4423,7 @@ 
        Series
 
        
-is_null() → Series[source]
        
+is_null() → Series[source]
 
        Returns a boolean Series indicating which values are null.
        
 
        
 
        Returns:
        
@@ -4450,7 +4450,7 @@ 
        Series
 
        
-is_numeric() → bool[source]
        
+is_numeric() → bool[source]
 
        Check if this Series datatype is numeric.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4462,7 +4462,7 @@ 
        Series
 
        
-is_sorted(*, descending: bool = False) → bool[source]
        
+is_sorted(*, descending: bool = False) → bool[source]
 
        Check if the Series is sorted.
        
 
        
 
        Parameters:
        
@@ -4476,7 +4476,7 @@ 
        Series
 
        
-is_temporal(excluding: OneOrMoreDataTypes | None = None) → bool[source]
        
+is_temporal(excluding: OneOrMoreDataTypes | None = None) → bool[source]
 
        Check if this Series datatype is temporal.
        
 
        
 
        Parameters:
        
@@ -4499,7 +4499,7 @@ 
        Series
 
        
-is_unique() → Series[source]
        
+is_unique() → Series[source]
 
        Get mask of all unique values.
        
 
        
 
        Returns:
        
@@ -4526,7 +4526,7 @@ 
        Series
 
        
-is_utf8() → bool[source]
        
+is_utf8() → bool[source]
 
        Check if this Series datatype is a Utf8.
        
 
        Examples
        
 
        >>> s = pl.Series("x", ["a", "b", "c"])
@@ -4538,7 +4538,7 @@ 
        Series
 
        
-item(row: int | None = None) → Any[source]
        
+item(row: int | None = None) → Any[source]
 
        Return the series as a scalar, or return the element at the given row index.
        
 
        If no row index is provided, this is equivalent to s[0], with a check
 that the shape is (1,). With a row index, this is equivalent to s[row].
        
@@ -4555,7 +4555,7 @@ 
        Series
 
        
-kurtosis(*, fisher: bool = True, bias: bool = True) → float | None[source]
        
+kurtosis(*, fisher: bool = True, bias: bool = True) → float | None[source]
 
        Compute the kurtosis (Fisher or Pearson) of a dataset.
        
 
        Kurtosis is the fourth central moment divided by the square of the
 variance. If Fisher’s definition is used, then 3.0 is subtracted from
@@ -4578,13 +4578,13 @@ 
        Series
 
        
-le(other: Any) → Self | Expr[source]
        
+le(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series <= other.
        
 
        
 
 
        
 
        
-len() → int[source]
        
+len() → int[source]
 
        Length of this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4596,7 +4596,7 @@ 
        Series
 
        
-limit(n: int = 10) → Series[source]
        
+limit(n: int = 10) → Series[source]
 
        Get the first n elements.
        
 
        Alias for Series.head().
        
 
        
@@ -4618,25 +4618,25 @@ 
        Series
 
        
-log(base: float = 2.718281828459045) → Series[source]
        
+log(base: float = 2.718281828459045) → Series[source]
 
        Compute the logarithm to a given base.
        
 
        
 
 
        
 
        
-log10() → Series[source]
        
+log10() → Series[source]
 
        Compute the base 10 logarithm of the input array, element-wise.
        
 
        
 
 
        
 
        
-log1p() → Series[source]
        
+log1p() → Series[source]
 
        Compute the natural logarithm of the input array plus one, element-wise.
        
 
        
 
 
        
 
        
-lower_bound() → Self[source]
        
+lower_bound() → Self[source]
 
        Return the lower bound of this Series’ dtype as a unit Series.
        
 
        
 
        See also
        
@@ -4668,13 +4668,13 @@ 
        Series
 
        
-lt(other: Any) → Self | Expr[source]
        
+lt(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series < other.
        
 
        
 
 
        
 
        
-map_dict(remapping: dict[Any, Any], *, default: Any = None, return_dtype: PolarsDataType | None = None) → Self[source]
        
+map_dict(remapping: dict[Any, Any], *, default: Any = None, return_dtype: PolarsDataType | None = None) → Self[source]
 
        Replace values in the Series using a remapping dictionary.
        
 
        
 
        Parameters:
        
@@ -4750,7 +4750,7 @@ 
        Series
 
        
-max() → PythonLiteral | None[source]
        
+max() → PythonLiteral | None[source]
 
        Get the maximum value in this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4762,7 +4762,7 @@ 
        Series
 
        
-mean() → int | float | None[source]
        
+mean() → int | float | None[source]
 
        Reduce this Series to the mean value.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4774,7 +4774,7 @@ 
        Series
 
        
-median() → float | None[source]
        
+median() → float | None[source]
 
        Get the median of this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4786,7 +4786,7 @@ 
        Series
 
        
-min() → PythonLiteral | None[source]
        
+min() → PythonLiteral | None[source]
 
        Get the minimal value in this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4798,7 +4798,7 @@ 
        Series
 
        
-mode() → Series[source]
        
+mode() → Series[source]
 
        Compute the most occurring value(s).
        
 
        Can return multiple Values.
        
 
        Examples
        
@@ -4815,7 +4815,7 @@ 
        Series
 
        
-n_chunks() → int[source]
        
+n_chunks() → int[source]
 
        Get the number of chunks that this Series contains.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4838,7 +4838,7 @@ 
        Series
 
        
-n_unique() → int[source]
        
+n_unique() → int[source]
 
        Count the number of unique values in this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 2, 3])
@@ -4850,7 +4850,7 @@ 
        Series
 
        
-nan_max() → int | float | date | datetime | timedelta | str[source]
        
+nan_max() → int | float | date | datetime | timedelta | str[source]
 
        Get maximum value, but propagate/poison encountered NaN values.
        
 
        This differs from numpy’s nanmax as numpy defaults to propagating NaN values,
 whereas polars defaults to ignoring them.
        
@@ -4858,7 +4858,7 @@ 
        Series
 
        
-nan_min() → int | float | date | datetime | timedelta | str[source]
        
+nan_min() → int | float | date | datetime | timedelta | str[source]
 
        Get minimum value, but propagate/poison encountered NaN values.
        
 
        This differs from numpy’s nanmax as numpy defaults to propagating NaN values,
 whereas polars defaults to ignoring them.
        
@@ -4866,13 +4866,13 @@ 
        Series
 
        
-ne(other: Any) → Self | Expr[source]
        
+ne(other: Any) → Self | Expr[source]
 
        Method equivalent of operator expression series != other.
        
 
        
 
 
        
 
        
-ne_missing(other: Expr) → Expr[source]
        
+ne_missing(other: Expr) → Expr[source]
 
        
 ne_missing(other: Any) → Self
        
 
        Method equivalent of equality operator expr != other where None == None`.
        
@@ -4889,19 +4889,19 @@ 
        Series
 
        
-new_from_index(index: int, length: int) → Self[source]
        
+new_from_index(index: int, length: int) → Self[source]
 
        Create a new Series filled with values from the given index.
        
 
        
 
 
        
 
        
-null_count() → int[source]
        
+null_count() → int[source]
 
        Count the null values in this Series.
        
 
        
 
 
        
 
        
-pct_change(n: int = 1) → Series[source]
        
+pct_change(n: int = 1) → Series[source]
 
        Computes percentage change between values.
        
 
        Percentage change (as fraction) between current element and most-recent
 non-null element at least n period(s) before the current element.
        
@@ -4953,7 +4953,7 @@ 
        Series
 
        
-peak_max() → Self[source]
        
+peak_max() → Self[source]
 
        Get a boolean mask of the local maximum peaks.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3, 4, 5])
@@ -4973,7 +4973,7 @@ 
        Series
 
        
-peak_min() → Self[source]
        
+peak_min() → Self[source]
 
        Get a boolean mask of the local minimum peaks.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [4, 1, 3, 2, 5])
@@ -4993,7 +4993,7 @@ 
        Series
 
        
-pow(exponent: int | float | None | Series) → Series[source]
        
+pow(exponent: int | float | None | Series) → Series[source]
 
        Raise to the power of the given exponent.
        
 
        
 
        Parameters:
        
@@ -5020,13 +5020,13 @@ 
        Series
 
        
-product() → int | float[source]
        
+product() → int | float[source]
 
        Reduce this Series to the product value.
        
 
        
 
 
        
 
        
-qcut(q: list[float] | int, *, labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', series: bool = True, left_closed: bool = False, allow_duplicates: bool = False, include_breaks: bool = False) → DataFrame | Series[source]
        
+qcut(q: list[float] | int, *, labels: list[str] | None = None, break_point_label: str = 'break_point', category_label: str = 'category', series: bool = True, left_closed: bool = False, allow_duplicates: bool = False, include_breaks: bool = False) → DataFrame | Series[source]
 
        Discretize continuous values into discrete categories based on their quantiles.
        
 
        
 
        Parameters:
        
@@ -5130,7 +5130,7 @@ 
        Series
 
        
-quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → float | None[source]
        
+quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → float | None[source]
 
        Get the quantile value of this Series.
        
 
        
 
        Parameters:
        
@@ -5152,7 +5152,7 @@ 
        Series
 
        
-rank(method: RankMethod = 'average', *, descending: bool = False, seed: int | None = None) → Series[source]
        
+rank(method: RankMethod = 'average', *, descending: bool = False, seed: int | None = None) → Series[source]
 
        Assign ranks to data, dealing with ties appropriately.
        
 
        
 
        Parameters:
        
@@ -5216,7 +5216,7 @@ 
        Series
 
        
-rechunk(*, in_place: bool = False) → Self[source]
        
+rechunk(*, in_place: bool = False) → Self[source]
 
        Create a single chunk of memory for this Series.
        
 
        
 
        Parameters:
        
@@ -5230,7 +5230,7 @@ 
        Series
 
        
-reinterpret(*, signed: bool = True) → Series[source]
        
+reinterpret(*, signed: bool = True) → Series[source]
 
        Reinterpret the underlying bits as a signed/unsigned integer.
        
 
        This operation is only allowed for 64bit integers. For lower bits integers,
 you can safely use that cast operation.
        
@@ -5246,7 +5246,7 @@ 
        Series
 
        
-rename(name: str, *, in_place: bool | None = None) → Series[source]
        
+rename(name: str, *, in_place: bool | None = None) → Series[source]
 
        Rename this Series.
        
 
        
 
        Parameters:
        
@@ -5274,7 +5274,7 @@ 
        Series
 
        
-reshape(dimensions: tuple[int, ...]) → Series[source]
        
+reshape(dimensions: tuple[int, ...]) → Series[source]
 
        Reshape this Series to a flat Series or a Series of Lists.
        
 
        
 
        Parameters:
        
@@ -5317,7 +5317,7 @@ 
        Series
 
        
-reverse() → Series[source]
        
+reverse() → Series[source]
 
        Return Series in reverse order.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3], dtype=pl.Int8)
@@ -5335,7 +5335,7 @@ 
        Series
 
        
-rle() → Series[source]
        
+rle() → Series[source]
 
        Get the lengths of runs of identical values.
        
 
        
 
        Returns:
        
@@ -5367,7 +5367,7 @@ 
        Series
 
        
-rle_id() → Series[source]
        
+rle_id() → Series[source]
 
        Map values to run IDs.
        
 
        Similar to RLE, but it maps each value to an ID corresponding to the run into
 which it falls. This is especially useful when you want to define groups by
@@ -5406,7 +5406,7 @@ 
        Series
 
        
-rolling_apply(function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_apply(function: Callable[[Series], Any], window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Apply a custom rolling window function.
        
 
        Prefer the specific rolling window functions over this one, as they are faster:
        
 
        
@@ -5456,7 +5456,7 @@ 
        Series
 
        
-rolling_max(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_max(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Apply a rolling max (moving max) over the values in this array.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5497,7 +5497,7 @@ 
        Series
 
        
-rolling_mean(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_mean(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Apply a rolling mean (moving mean) over the values in this array.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5538,7 +5538,7 @@ 
        Series
 
        
-rolling_median(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_median(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Compute a rolling median.
        
 
        
 
        Parameters:
        
@@ -5577,7 +5577,7 @@ 
        Series
 
        
-rolling_min(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_min(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Apply a rolling min (moving min) over the values in this array.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5618,7 +5618,7 @@ 
        Series
 
        
-rolling_quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int = 2, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest', window_size: int = 2, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Compute a rolling quantile.
        
 
        The window at a given row will include the row itself and the window_size - 1
 elements before it.
        
@@ -5672,7 +5672,7 @@ 
        Series
 
        
-rolling_skew(window_size: int, *, bias: bool = True) → Series[source]
        
+rolling_skew(window_size: int, *, bias: bool = True) → Series[source]
 
        Compute a rolling skew.
        
 
        The window at a given row includes the row itself and the
 window_size - 1 elements before it.
        
@@ -5707,7 +5707,7 @@ 
        Series
 
        
-rolling_std(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]
        
+rolling_std(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]
 
        Compute a rolling std dev.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5751,7 +5751,7 @@ 
        Series
 
        
-rolling_sum(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
        
+rolling_sum(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False) → Series[source]
 
        Apply a rolling sum (moving sum) over the values in this array.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5792,7 +5792,7 @@ 
        Series
 
        
-rolling_var(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]
        
+rolling_var(window_size: int, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, ddof: int = 1) → Series[source]
 
        Compute a rolling variance.
        
 
        A window of length window_size will traverse the array. The values that fill
 this window will (optionally) be multiplied with the weights given by the
@@ -5836,7 +5836,7 @@ 
        Series
 
        
-round(decimals: int = 0) → Series[source]
        
+round(decimals: int = 0) → Series[source]
 
        Round underlying floating point data by decimals digits.
        
 
        
 
        Parameters:
        
@@ -5862,7 +5862,7 @@ 
        Series
 
        
-sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Series[source]
        
+sample(n: int | None = None, *, fraction: float | None = None, with_replacement: bool = False, shuffle: bool = False, seed: int | None = None) → Series[source]
 
        Sample from this Series.
        
 
        
 
        Parameters:
        
@@ -5897,7 +5897,7 @@ 
        Series
 
        
-search_sorted(element: int | float, side: SearchSortedSide = 'any') → int[source]
        
+search_sorted(element: int | float, side: SearchSortedSide = 'any') → int[source]
 
        
 search_sorted(element: Series | ndarray[Any, Any] | list[int] | list[float], side: SearchSortedSide = 'any') → Series
        
 
        Find indices where elements should be inserted to maintain order.
        
@@ -5919,7 +5919,7 @@ 
        Series
 
        
-series_equal(other: Series, *, null_equal: bool = True, strict: bool = False) → bool[source]
        
+series_equal(other: Series, *, null_equal: bool = True, strict: bool = False) → bool[source]
 
        Check if series is equal with another Series.
        
 
        
 
        Parameters:
        
@@ -5947,7 +5947,7 @@ 
        Series
 
        
-set(filter: Series, value: int | float | str) → Series[source]
        
+set(filter: Series, value: int | float | str) → Series[source]
 
        Set masked values.
        
 
        
 
        Parameters:
        
@@ -5995,7 +5995,7 @@ 
        Series
 
        
-set_at_idx(idx: Series | ndarray[Any, Any] | Sequence[int] | int, value: int | float | str | bool | Sequence[int] | Sequence[float] | Sequence[bool] | Sequence[str] | Sequence[date] | Sequence[datetime] | date | datetime | Series | None) → Series[source]
        
+set_at_idx(idx: Series | ndarray[Any, Any] | Sequence[int] | int, value: int | float | str | bool | Sequence[int] | Sequence[float] | Sequence[bool] | Sequence[str] | Sequence[date] | Sequence[datetime] | date | datetime | Series | None) → Series[source]
 
        Set values at the index locations.
        
 
        
 
        Parameters:
        
@@ -6049,7 +6049,7 @@ 
        Series
 
        
-set_sorted(*, descending: bool = False) → Self[source]
        
+set_sorted(*, descending: bool = False) → Self[source]
 
        Flags the Series as ‘sorted’.
        
 
        Enables downstream code to user fast paths for sorted arrays.
        
 
        
@@ -6075,7 +6075,7 @@ 
        Series
 
        
-shift(periods: int = 1) → Series[source]
        
+shift(periods: int = 1) → Series[source]
 
        Shift the values by a given period.
        
 
        
 
        Parameters:
        
@@ -6109,7 +6109,7 @@ 
        Series
 
        
-shift_and_fill(fill_value: int | Expr, *, periods: int = 1) → Series[source]
        
+shift_and_fill(fill_value: int | Expr, *, periods: int = 1) → Series[source]
 
        Shift the values by a given period and fill the resulting null values.
        
 
        
 
        Parameters:
        
@@ -6125,7 +6125,7 @@ 
        Series
 
        
-shrink_dtype() → Series[source]
        
+shrink_dtype() → Series[source]
 
        Shrink numeric columns to the minimal required datatype.
        
 
        Shrink to the dtype needed to fit the extrema of this [Series].
 This can be used to reduce memory pressure.
        
@@ -6133,7 +6133,7 @@ 
        Series
 
        
-shrink_to_fit(*, in_place: bool = False) → Series[source]
        
+shrink_to_fit(*, in_place: bool = False) → Series[source]
 
        Shrink Series memory usage.
        
 
        Shrinks the underlying array capacity to exactly fit the actual data.
 (Note that this function does not change the Series data type).
        
@@ -6141,7 +6141,7 @@ 
        Series
 
        
-shuffle(seed: int | None = None) → Series[source]
        
+shuffle(seed: int | None = None) → Series[source]
 
        Shuffle the contents of this Series.
        
 
        
 
        Parameters:
        
@@ -6168,7 +6168,7 @@ 
        Series
 
        
-sign() → Series[source]
        
+sign() → Series[source]
 
        Compute the element-wise indication of the sign.
        
 
        The returned values can be -1, 0, or 1:
        
 
          
@@ -6195,7 +6195,7 @@ 
          Series
 
          
-sin() → Series[source]
          
+sin() → Series[source]
 
          Compute the element-wise value for the sine.
          
 
          Examples
          
 
          >>> import math
@@ -6214,7 +6214,7 @@ 
          Series
 
          
-sinh() → Series[source]
          
+sinh() → Series[source]
 
          Compute the element-wise value for the hyperbolic sine.
          
 
          Examples
          
 
          >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -6232,7 +6232,7 @@ 
          Series
 
          
-skew(*, bias: bool = True) → float | None[source]
          
+skew(*, bias: bool = True) → float | None[source]
 
          Compute the sample skewness of a data set.
          
 
          For normally distributed data, the skewness should be about zero. For
 unimodal continuous distributions, a skewness value greater than zero means
@@ -6267,7 +6267,7 @@ 
          Series
 
          
-slice(offset: int, length: int | None = None) → Series[source]
          
+slice(offset: int, length: int | None = None) → Series[source]
 
          Get a slice of this Series.
          
 
          
 
          Parameters:
          
@@ -6295,7 +6295,7 @@ 
          Series
 
          
-sort(*, descending: bool = False, in_place: bool = False) → Self[source]
          
+sort(*, descending: bool = False, in_place: bool = False) → Self[source]
 
          Sort this Series.
          
 
          
 
          Parameters:
          
@@ -6333,7 +6333,7 @@ 
          Series
 
          
-sqrt() → Series[source]
          
+sqrt() → Series[source]
 
          Compute the square root of the elements.
          
 
          Syntactic sugar for
          
 
          >>> pl.Series([1, 2]) ** 0.5
@@ -6349,7 +6349,7 @@ 
          Series
 
          
-std(ddof: int = 1) → float | None[source]
          
+std(ddof: int = 1) → float | None[source]
 
          Get the standard deviation of this Series.
          
 
          
 
          Parameters:
          
@@ -6371,7 +6371,7 @@ 
          Series
 
          
-sum() → int | float[source]
          
+sum() → int | float[source]
 
          Reduce this Series to the sum value.
          
 
          Notes
          
 
          Dtypes in {Int8, UInt8, Int16, UInt16} are cast to
@@ -6386,7 +6386,7 @@ 
          Series
 
          
-tail(n: int = 10) → Series[source]
          
+tail(n: int = 10) → Series[source]
 
          Get the last n elements.
          
 
          
 
          Parameters:
          
@@ -6429,7 +6429,7 @@ 
          Series
 
          
-take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) → Series[source]
          
+take(indices: int | list[int] | Expr | Series | np.ndarray[Any, Any]) → Series[source]
 
          Take values by index.
          
 
          
 
          Parameters:
          
@@ -6454,7 +6454,7 @@ 
          Series
 
          
-take_every(n: int) → Series[source]
          
+take_every(n: int) → Series[source]
 
          Take every nth value in the Series and return as new Series.
          
 
          Examples
          
 
          >>> s = pl.Series("a", [1, 2, 3, 4])
@@ -6471,7 +6471,7 @@ 
          Series
 
          
-tan() → Series[source]
          
+tan() → Series[source]
 
          Compute the element-wise value for the tangent.
          
 
          Examples
          
 
          >>> import math
@@ -6490,7 +6490,7 @@ 
          Series
 
          
-tanh() → Series[source]
          
+tanh() → Series[source]
 
          Compute the element-wise value for the hyperbolic tangent.
          
 
          Examples
          
 
          >>> s = pl.Series("a", [1.0, 0.0, -1.0])
@@ -6508,7 +6508,7 @@ 
          Series
 
          
-to_arrow() → Array[source]
          
+to_arrow() → Array[source]
 
          Get the underlying Arrow Array.
          
 
          If the Series contains only a single chunk this operation is zero copy.
          
 
          Examples
          
@@ -6527,7 +6527,7 @@ 
          Series
 
          
-to_dummies(separator: str = '_') → DataFrame[source]
          
+to_dummies(separator: str = '_') → DataFrame[source]
 
          Get dummy/indicator variables.
          
 
          
 
          Parameters:
          
@@ -6556,7 +6556,7 @@ 
          Series
 
          
-to_frame(name: str | None = None) → DataFrame[source]
          
+to_frame(name: str | None = None) → DataFrame[source]
 
          Cast this Series to a DataFrame.
          
 
          
 
          Parameters:
          
@@ -6598,7 +6598,7 @@ 
          Series
 
          
-to_init_repr(n: int = 1000) → str[source]
          
+to_init_repr(n: int = 1000) → str[source]
 
          Convert Series to instantiatable string representation.
          
 
          
 
          Parameters:
          
@@ -6635,7 +6635,7 @@ 
          Series
 
          
-to_list(*, use_pyarrow: bool = False) → list[Any][source]
          
+to_list(*, use_pyarrow: bool = False) → list[Any][source]
 
          Convert this Series to a Python List. This operation clones data.
          
 
          
 
          Parameters:
          
@@ -6657,7 +6657,7 @@ 
          Series
 
          
-to_numpy(*args: Any, zero_copy_only: bool = False, writable: bool = False, use_pyarrow: bool = True) → ndarray[Any, Any][source]
          
+to_numpy(*args: Any, zero_copy_only: bool = False, writable: bool = False, use_pyarrow: bool = True) → ndarray[Any, Any][source]
 
          Convert this Series to numpy.
          
 
          This operation may clone data but is completely safe. Note that:
          
 
            
@@ -6701,7 +6701,7 @@ 
            Series
 
            
-to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → pd.Series[Any][source]
            
+to_pandas(*args: Any, use_pyarrow_extension_array: bool = False, **kwargs: Any) → pd.Series[Any][source]
 
            Convert this Series to a pandas Series.
            
 
            This requires that pandas and pyarrow are installed.
 This operation clones data, unless use_pyarrow_extension_array=True.
            
@@ -6752,7 +6752,7 @@ 
            Series
 
            
-to_physical() → Series[source]
            
+to_physical() → Series[source]
 
            Cast to physical representation of the logical dtype.
            
 
              
 
            • polars.datatypes.Date() -> polars.datatypes.Int32()
              • 
@@ -6783,7 +6783,7 @@ 
              Series
 
              
-top_k(k: int = 5) → Series[source]
              
+top_k(k: int = 5) → Series[source]
 
              Return the k largest elements.
              
 
              This has time complexity:
              
 
              
@@ -6818,7 +6818,7 @@ 
              Series
 
              
-unique(*, maintain_order: bool = False) → Series[source]
              
+unique(*, maintain_order: bool = False) → Series[source]
 
              Get unique elements in series.
              
 
              
 
              Parameters:
              
@@ -6844,7 +6844,7 @@ 
              Series
 
              
-unique_counts() → Series[source]
              
+unique_counts() → Series[source]
 
              Return a count of the unique values in the order of appearance.
              
 
              Examples
              
 
              >>> s = pl.Series("id", ["a", "b", "b", "c", "c", "c"])
@@ -6862,7 +6862,7 @@ 
              Series
 
              
-upper_bound() → Self[source]
              
+upper_bound() → Self[source]
 
              Return the upper bound of this Series’ dtype as a unit Series.
              
 
              
 
              See also
              
@@ -6894,7 +6894,7 @@ 
              Series
 
              
-value_counts(*, sort: bool = False) → DataFrame[source]
              
+value_counts(*, sort: bool = False) → DataFrame[source]
 
              Count the unique values in a Series.
              
 
              
 
              Parameters:
              
@@ -6923,7 +6923,7 @@ 
              Series
 
              
-var(ddof: int = 1) → float | None[source]
              
+var(ddof: int = 1) → float | None[source]
 
              Get variance of this Series.
              
 
              
 
              Parameters:
              
@@ -6945,7 +6945,7 @@ 
              Series
 
              
-view(*, ignore_nulls: bool = False) → SeriesView[source]
              
+view(*, ignore_nulls: bool = False) → SeriesView[source]
 
              Get a view into this Series data with a numpy array.
              
 
              This operation doesn’t clone data, but does not include missing values.
 Don’t use this unless you know what you are doing.
              
@@ -6968,7 +6968,7 @@ 
              Series
 
              
-zip_with(mask: Series, other: Series) → Self[source]
              
+zip_with(mask: Series, other: Series) → Self[source]
 
              Take values from self or other based on the given mask.
              
 
              Where mask evaluates true, take values from self. Where mask evaluates false,
 take values from other.
              
diff --git a/py-polars/html/reference/sql.html b/py-polars/html/reference/sql.html
index 7153fef7a289..1b98c8d6cc2a 100644
--- a/py-polars/html/reference/sql.html
+++ b/py-polars/html/reference/sql.html
@@ -1620,11 +1620,11 @@
 
              SQL#
              
 
              
 
              
-class polars.SQLContext[source]#
              
+class polars.SQLContext[source]#
 
              Run SQL queries against DataFrame/LazyFrame data.
              
 
              
 
              
-__init__(frames: Mapping[str, DataFrame | LazyFrame] | None = None, *, register_globals: bool | int = False, eager_execution: Literal[False] = False, **named_frames: DataFrame | LazyFrame) → None[source]#
              
+__init__(frames: Mapping[str, DataFrame | LazyFrame] | None = None, *, register_globals: bool | int = False, eager_execution: Literal[False] = False, **named_frames: DataFrame | LazyFrame) → None[source]#
 
              
 __init__(frames: Mapping[str, DataFrame | LazyFrame] | None = None, *, register_globals: bool | int = False, eager_execution: Literal[True], **named_frames: DataFrame | LazyFrame) → None
              
 
              Initialise a new SQLContext.
              
@@ -1669,13 +1669,13 @@ 
              SQL#
 
              Note: can be used as a context manager.
              
 
              
 
              
-__enter__() → SQLContext[FrameType][source]#
              
+__enter__() → SQLContext[FrameType][source]#
 
              Track currently registered tables on scope entry; supports nested scopes.
              
 
              
 
 
              
 
              
-__exit__(exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None) → None[source]#
              
+__exit__(exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None) → None[source]#
 
              Unregister any tables created within the given scope on context exit.
              
 
              
 
              See also
              
diff --git a/py-polars/html/searchindex.js b/py-polars/html/searchindex.js
index 2d83639db34e..e82d2fe549b7 100644
--- a/py-polars/html/searchindex.js
+++ b/py-polars/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["index", "reference/api", "reference/api/polars.Array", "reference/api/polars.Binary", "reference/api/polars.Boolean", "reference/api/polars.Categorical", "reference/api/polars.Config.activate_decimals", "reference/api/polars.Config.load", "reference/api/polars.Config.restore_defaults", "reference/api/polars.Config.save", "reference/api/polars.Config.set_ascii_tables", "reference/api/polars.Config.set_fmt_float", "reference/api/polars.Config.set_fmt_str_lengths", "reference/api/polars.Config.set_streaming_chunk_size", "reference/api/polars.Config.set_tbl_cell_alignment", "reference/api/polars.Config.set_tbl_cols", "reference/api/polars.Config.set_tbl_column_data_type_inline", "reference/api/polars.Config.set_tbl_dataframe_shape_below", "reference/api/polars.Config.set_tbl_formatting", "reference/api/polars.Config.set_tbl_hide_column_data_types", "reference/api/polars.Config.set_tbl_hide_column_names", "reference/api/polars.Config.set_tbl_hide_dataframe_shape", "reference/api/polars.Config.set_tbl_hide_dtype_separator", "reference/api/polars.Config.set_tbl_rows", "reference/api/polars.Config.set_tbl_width_chars", "reference/api/polars.Config.set_verbose", "reference/api/polars.Config.state", "reference/api/polars.DataFrame.write_avro", "reference/api/polars.DataFrame.write_csv", "reference/api/polars.DataFrame.write_database", "reference/api/polars.DataFrame.write_delta", "reference/api/polars.DataFrame.write_excel", "reference/api/polars.DataFrame.write_ipc", "reference/api/polars.DataFrame.write_json", "reference/api/polars.DataFrame.write_ndjson", "reference/api/polars.DataFrame.write_parquet", "reference/api/polars.DataType", "reference/api/polars.Date", "reference/api/polars.Datetime", "reference/api/polars.Decimal", "reference/api/polars.Duration", "reference/api/polars.Float32", "reference/api/polars.Float64", "reference/api/polars.Int16", "reference/api/polars.Int32", "reference/api/polars.Int64", "reference/api/polars.Int8", "reference/api/polars.LazyFrame.sink_ipc", "reference/api/polars.LazyFrame.sink_parquet", "reference/api/polars.List", "reference/api/polars.Null", "reference/api/polars.Object", "reference/api/polars.SQLContext.execute", "reference/api/polars.SQLContext.register", "reference/api/polars.SQLContext.register_globals", "reference/api/polars.SQLContext.register_many", "reference/api/polars.SQLContext.tables", "reference/api/polars.SQLContext.unregister", "reference/api/polars.StringCache", "reference/api/polars.Struct", "reference/api/polars.Time", "reference/api/polars.UInt16", "reference/api/polars.UInt32", "reference/api/polars.UInt64", "reference/api/polars.UInt8", "reference/api/polars.Unknown", "reference/api/polars.Utf8", "reference/api/polars.align_frames", "reference/api/polars.api.register_dataframe_namespace", "reference/api/polars.api.register_expr_namespace", "reference/api/polars.api.register_lazyframe_namespace", "reference/api/polars.api.register_series_namespace", "reference/api/polars.build_info", "reference/api/polars.collect_all", "reference/api/polars.concat", "reference/api/polars.enable_string_cache", "reference/api/polars.exceptions.ArrowError", "reference/api/polars.exceptions.ColumnNotFoundError", "reference/api/polars.exceptions.ComputeError", "reference/api/polars.exceptions.DuplicateError", "reference/api/polars.exceptions.InvalidOperationError", "reference/api/polars.exceptions.NoDataError", "reference/api/polars.exceptions.NoRowsReturnedError", "reference/api/polars.exceptions.PolarsPanicError", "reference/api/polars.exceptions.RowsError", "reference/api/polars.exceptions.SchemaError", "reference/api/polars.exceptions.SchemaFieldNotFoundError", "reference/api/polars.exceptions.ShapeError", "reference/api/polars.exceptions.StructFieldNotFoundError", "reference/api/polars.exceptions.TooManyRowsReturnedError", "reference/api/polars.from_arrow", "reference/api/polars.from_dataframe", "reference/api/polars.from_dict", "reference/api/polars.from_dicts", "reference/api/polars.from_numpy", "reference/api/polars.from_pandas", "reference/api/polars.from_records", "reference/api/polars.from_repr", "reference/api/polars.get_index_type", "reference/api/polars.io.csv.batched_reader.BatchedCsvReader.next_batches", "reference/api/polars.read_avro", "reference/api/polars.read_csv", "reference/api/polars.read_csv_batched", "reference/api/polars.read_database", "reference/api/polars.read_delta", "reference/api/polars.read_excel", "reference/api/polars.read_ipc", "reference/api/polars.read_ipc_schema", "reference/api/polars.read_json", "reference/api/polars.read_ndjson", "reference/api/polars.read_parquet", "reference/api/polars.read_parquet_schema", "reference/api/polars.scan_csv", "reference/api/polars.scan_delta", "reference/api/polars.scan_ipc", "reference/api/polars.scan_ndjson", "reference/api/polars.scan_parquet", "reference/api/polars.scan_pyarrow_dataset", "reference/api/polars.show_versions", "reference/api/polars.testing.assert_frame_equal", "reference/api/polars.testing.assert_series_equal", "reference/api/polars.testing.parametric.column", "reference/api/polars.testing.parametric.columns", "reference/api/polars.testing.parametric.create_list_strategy", "reference/api/polars.testing.parametric.dataframes", "reference/api/polars.testing.parametric.load_profile", "reference/api/polars.testing.parametric.series", "reference/api/polars.testing.parametric.set_profile", "reference/api/polars.threadpool_size", "reference/api/polars.using_string_cache", "reference/config", "reference/dataframe/aggregation", "reference/dataframe/api/polars.DataFrame.__dataframe__", "reference/dataframe/api/polars.DataFrame.apply", "reference/dataframe/api/polars.DataFrame.bottom_k", "reference/dataframe/api/polars.DataFrame.clear", "reference/dataframe/api/polars.DataFrame.clone", "reference/dataframe/api/polars.DataFrame.columns", "reference/dataframe/api/polars.DataFrame.corr", "reference/dataframe/api/polars.DataFrame.describe", "reference/dataframe/api/polars.DataFrame.drop", "reference/dataframe/api/polars.DataFrame.drop_in_place", "reference/dataframe/api/polars.DataFrame.drop_nulls", "reference/dataframe/api/polars.DataFrame.dtypes", "reference/dataframe/api/polars.DataFrame.estimated_size", "reference/dataframe/api/polars.DataFrame.explode", "reference/dataframe/api/polars.DataFrame.extend", "reference/dataframe/api/polars.DataFrame.fill_nan", "reference/dataframe/api/polars.DataFrame.fill_null", "reference/dataframe/api/polars.DataFrame.filter", "reference/dataframe/api/polars.DataFrame.find_idx_by_name", "reference/dataframe/api/polars.DataFrame.flags", "reference/dataframe/api/polars.DataFrame.fold", "reference/dataframe/api/polars.DataFrame.frame_equal", "reference/dataframe/api/polars.DataFrame.get_column", "reference/dataframe/api/polars.DataFrame.get_columns", "reference/dataframe/api/polars.DataFrame.glimpse", "reference/dataframe/api/polars.DataFrame.groupby", "reference/dataframe/api/polars.DataFrame.groupby_dynamic", "reference/dataframe/api/polars.DataFrame.groupby_rolling", "reference/dataframe/api/polars.DataFrame.hash_rows", "reference/dataframe/api/polars.DataFrame.head", "reference/dataframe/api/polars.DataFrame.height", "reference/dataframe/api/polars.DataFrame.hstack", "reference/dataframe/api/polars.DataFrame.insert_at_idx", "reference/dataframe/api/polars.DataFrame.interpolate", "reference/dataframe/api/polars.DataFrame.is_duplicated", "reference/dataframe/api/polars.DataFrame.is_empty", "reference/dataframe/api/polars.DataFrame.is_unique", "reference/dataframe/api/polars.DataFrame.item", "reference/dataframe/api/polars.DataFrame.iter_rows", "reference/dataframe/api/polars.DataFrame.iter_slices", "reference/dataframe/api/polars.DataFrame.join", "reference/dataframe/api/polars.DataFrame.join_asof", "reference/dataframe/api/polars.DataFrame.lazy", "reference/dataframe/api/polars.DataFrame.limit", "reference/dataframe/api/polars.DataFrame.max", "reference/dataframe/api/polars.DataFrame.mean", "reference/dataframe/api/polars.DataFrame.median", "reference/dataframe/api/polars.DataFrame.melt", "reference/dataframe/api/polars.DataFrame.merge_sorted", "reference/dataframe/api/polars.DataFrame.min", "reference/dataframe/api/polars.DataFrame.n_chunks", "reference/dataframe/api/polars.DataFrame.n_unique", "reference/dataframe/api/polars.DataFrame.null_count", "reference/dataframe/api/polars.DataFrame.partition_by", "reference/dataframe/api/polars.DataFrame.pipe", "reference/dataframe/api/polars.DataFrame.pivot", "reference/dataframe/api/polars.DataFrame.product", "reference/dataframe/api/polars.DataFrame.quantile", "reference/dataframe/api/polars.DataFrame.rechunk", "reference/dataframe/api/polars.DataFrame.rename", "reference/dataframe/api/polars.DataFrame.replace", "reference/dataframe/api/polars.DataFrame.replace_at_idx", "reference/dataframe/api/polars.DataFrame.reverse", "reference/dataframe/api/polars.DataFrame.row", "reference/dataframe/api/polars.DataFrame.rows", "reference/dataframe/api/polars.DataFrame.rows_by_key", "reference/dataframe/api/polars.DataFrame.sample", "reference/dataframe/api/polars.DataFrame.schema", "reference/dataframe/api/polars.DataFrame.select", "reference/dataframe/api/polars.DataFrame.set_sorted", "reference/dataframe/api/polars.DataFrame.shape", "reference/dataframe/api/polars.DataFrame.shift", "reference/dataframe/api/polars.DataFrame.shift_and_fill", "reference/dataframe/api/polars.DataFrame.shrink_to_fit", "reference/dataframe/api/polars.DataFrame.slice", "reference/dataframe/api/polars.DataFrame.sort", "reference/dataframe/api/polars.DataFrame.std", "reference/dataframe/api/polars.DataFrame.sum", "reference/dataframe/api/polars.DataFrame.tail", "reference/dataframe/api/polars.DataFrame.take_every", "reference/dataframe/api/polars.DataFrame.to_arrow", "reference/dataframe/api/polars.DataFrame.to_dict", "reference/dataframe/api/polars.DataFrame.to_dicts", "reference/dataframe/api/polars.DataFrame.to_dummies", "reference/dataframe/api/polars.DataFrame.to_init_repr", "reference/dataframe/api/polars.DataFrame.to_numpy", "reference/dataframe/api/polars.DataFrame.to_pandas", "reference/dataframe/api/polars.DataFrame.to_series", "reference/dataframe/api/polars.DataFrame.to_struct", "reference/dataframe/api/polars.DataFrame.top_k", "reference/dataframe/api/polars.DataFrame.transpose", "reference/dataframe/api/polars.DataFrame.unique", "reference/dataframe/api/polars.DataFrame.unnest", "reference/dataframe/api/polars.DataFrame.unstack", "reference/dataframe/api/polars.DataFrame.update", "reference/dataframe/api/polars.DataFrame.upsample", "reference/dataframe/api/polars.DataFrame.var", "reference/dataframe/api/polars.DataFrame.vstack", "reference/dataframe/api/polars.DataFrame.width", "reference/dataframe/api/polars.DataFrame.with_columns", "reference/dataframe/api/polars.DataFrame.with_row_count", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.__iter__", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.agg", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.all", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.apply", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.count", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.first", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.head", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.last", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.max", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.mean", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.median", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.min", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.n_unique", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.quantile", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.sum", "reference/dataframe/api/polars.dataframe.groupby.GroupBy.tail", "reference/dataframe/attributes", "reference/dataframe/computation", "reference/dataframe/descriptive", "reference/dataframe/export", "reference/dataframe/groupby", "reference/dataframe/index", "reference/dataframe/miscellaneous", "reference/dataframe/modify_select", "reference/datatypes", "reference/exceptions", "reference/expressions/aggregation", "reference/expressions/api/polars.Expr.abs", "reference/expressions/api/polars.Expr.add", "reference/expressions/api/polars.Expr.agg_groups", "reference/expressions/api/polars.Expr.alias", "reference/expressions/api/polars.Expr.all", "reference/expressions/api/polars.Expr.and_", "reference/expressions/api/polars.Expr.any", "reference/expressions/api/polars.Expr.append", "reference/expressions/api/polars.Expr.apply", "reference/expressions/api/polars.Expr.approx_unique", "reference/expressions/api/polars.Expr.arccos", "reference/expressions/api/polars.Expr.arccosh", "reference/expressions/api/polars.Expr.arcsin", "reference/expressions/api/polars.Expr.arcsinh", "reference/expressions/api/polars.Expr.arctan", "reference/expressions/api/polars.Expr.arctanh", "reference/expressions/api/polars.Expr.arg_max", "reference/expressions/api/polars.Expr.arg_min", "reference/expressions/api/polars.Expr.arg_sort", "reference/expressions/api/polars.Expr.arg_true", "reference/expressions/api/polars.Expr.arg_unique", "reference/expressions/api/polars.Expr.arr.max", "reference/expressions/api/polars.Expr.arr.min", "reference/expressions/api/polars.Expr.arr.sum", "reference/expressions/api/polars.Expr.arr.unique", "reference/expressions/api/polars.Expr.backward_fill", "reference/expressions/api/polars.Expr.bin.contains", "reference/expressions/api/polars.Expr.bin.decode", "reference/expressions/api/polars.Expr.bin.encode", "reference/expressions/api/polars.Expr.bin.ends_with", "reference/expressions/api/polars.Expr.bin.starts_with", "reference/expressions/api/polars.Expr.bottom_k", "reference/expressions/api/polars.Expr.cache", "reference/expressions/api/polars.Expr.cast", "reference/expressions/api/polars.Expr.cat.get_categories", "reference/expressions/api/polars.Expr.cat.set_ordering", "reference/expressions/api/polars.Expr.cbrt", "reference/expressions/api/polars.Expr.ceil", "reference/expressions/api/polars.Expr.clip", "reference/expressions/api/polars.Expr.clip_max", "reference/expressions/api/polars.Expr.clip_min", "reference/expressions/api/polars.Expr.cos", "reference/expressions/api/polars.Expr.cosh", "reference/expressions/api/polars.Expr.count", "reference/expressions/api/polars.Expr.cumcount", "reference/expressions/api/polars.Expr.cummax", "reference/expressions/api/polars.Expr.cummin", "reference/expressions/api/polars.Expr.cumprod", "reference/expressions/api/polars.Expr.cumsum", "reference/expressions/api/polars.Expr.cumulative_eval", "reference/expressions/api/polars.Expr.cut", "reference/expressions/api/polars.Expr.degrees", "reference/expressions/api/polars.Expr.diff", "reference/expressions/api/polars.Expr.dot", "reference/expressions/api/polars.Expr.drop_nans", "reference/expressions/api/polars.Expr.drop_nulls", "reference/expressions/api/polars.Expr.dt.base_utc_offset", "reference/expressions/api/polars.Expr.dt.cast_time_unit", "reference/expressions/api/polars.Expr.dt.combine", "reference/expressions/api/polars.Expr.dt.convert_time_zone", "reference/expressions/api/polars.Expr.dt.date", "reference/expressions/api/polars.Expr.dt.datetime", "reference/expressions/api/polars.Expr.dt.day", "reference/expressions/api/polars.Expr.dt.days", "reference/expressions/api/polars.Expr.dt.dst_offset", "reference/expressions/api/polars.Expr.dt.epoch", "reference/expressions/api/polars.Expr.dt.hour", "reference/expressions/api/polars.Expr.dt.hours", "reference/expressions/api/polars.Expr.dt.is_leap_year", "reference/expressions/api/polars.Expr.dt.iso_year", "reference/expressions/api/polars.Expr.dt.microsecond", "reference/expressions/api/polars.Expr.dt.microseconds", "reference/expressions/api/polars.Expr.dt.millisecond", "reference/expressions/api/polars.Expr.dt.milliseconds", "reference/expressions/api/polars.Expr.dt.minute", "reference/expressions/api/polars.Expr.dt.minutes", "reference/expressions/api/polars.Expr.dt.month", "reference/expressions/api/polars.Expr.dt.month_end", "reference/expressions/api/polars.Expr.dt.month_start", "reference/expressions/api/polars.Expr.dt.nanosecond", "reference/expressions/api/polars.Expr.dt.nanoseconds", "reference/expressions/api/polars.Expr.dt.offset_by", "reference/expressions/api/polars.Expr.dt.ordinal_day", "reference/expressions/api/polars.Expr.dt.quarter", "reference/expressions/api/polars.Expr.dt.replace_time_zone", "reference/expressions/api/polars.Expr.dt.round", "reference/expressions/api/polars.Expr.dt.second", "reference/expressions/api/polars.Expr.dt.seconds", "reference/expressions/api/polars.Expr.dt.strftime", "reference/expressions/api/polars.Expr.dt.time", "reference/expressions/api/polars.Expr.dt.timestamp", "reference/expressions/api/polars.Expr.dt.to_string", "reference/expressions/api/polars.Expr.dt.truncate", "reference/expressions/api/polars.Expr.dt.week", "reference/expressions/api/polars.Expr.dt.weekday", "reference/expressions/api/polars.Expr.dt.with_time_unit", "reference/expressions/api/polars.Expr.dt.year", "reference/expressions/api/polars.Expr.entropy", "reference/expressions/api/polars.Expr.eq", "reference/expressions/api/polars.Expr.eq_missing", "reference/expressions/api/polars.Expr.ewm_mean", "reference/expressions/api/polars.Expr.ewm_std", "reference/expressions/api/polars.Expr.ewm_var", "reference/expressions/api/polars.Expr.exclude", "reference/expressions/api/polars.Expr.exp", "reference/expressions/api/polars.Expr.explode", "reference/expressions/api/polars.Expr.extend_constant", "reference/expressions/api/polars.Expr.fill_nan", "reference/expressions/api/polars.Expr.fill_null", "reference/expressions/api/polars.Expr.filter", "reference/expressions/api/polars.Expr.first", "reference/expressions/api/polars.Expr.flatten", "reference/expressions/api/polars.Expr.floor", "reference/expressions/api/polars.Expr.floordiv", "reference/expressions/api/polars.Expr.forward_fill", "reference/expressions/api/polars.Expr.from_json", "reference/expressions/api/polars.Expr.ge", "reference/expressions/api/polars.Expr.gt", "reference/expressions/api/polars.Expr.hash", "reference/expressions/api/polars.Expr.head", "reference/expressions/api/polars.Expr.implode", "reference/expressions/api/polars.Expr.inspect", "reference/expressions/api/polars.Expr.interpolate", 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185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202, 203, 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 254, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 588, 589, 591, 592, 593, 594, 595, 596, 597, 599, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 615, 616, 619, 620, 621, 622, 623, 624, 625, 627, 628, 629, 630, 631, 639, 650, 653, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 693, 694, 695, 696, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 735, 741, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 766, 767, 769, 770, 772, 773, 774, 775, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 839, 840, 841, 842, 843, 846, 847, 848, 849, 850, 852, 855, 856, 857, 860, 861, 862, 863, 864, 865, 867, 868, 869, 870, 871, 872, 873, 874, 875, 877, 878, 879, 880, 882, 885, 886, 892, 893, 894, 897, 898, 900, 905, 906, 908, 909, 910, 911, 913, 915, 920, 921, 922, 923, 924, 925, 926, 927, 928, 934, 935, 936, 938, 939, 940, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 961, 962, 963, 964, 966, 970, 971, 972, 973, 975, 976, 978, 980, 981, 982, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1051, 1058], "dtype": [2, 31, 38, 70, 74, 75, 90, 92, 94, 96, 97, 101, 102, 108, 109, 112, 121, 122, 123, 124, 126, 132, 158, 159, 173, 216, 217, 218, 254, 268, 293, 294, 298, 299, 300, 307, 308, 355, 363, 405, 430, 436, 438, 440, 476, 477, 483, 484, 485, 486, 487, 489, 490, 491, 498, 518, 535, 548, 550, 554, 559, 568, 570, 581, 593, 597, 601, 602, 604, 605, 613, 616, 622, 631, 639, 671, 672, 677, 735, 738, 757, 758, 767, 769, 774, 775, 776, 777, 783, 784, 787, 788, 836, 840, 864, 870, 877, 889, 914, 920, 921, 945, 961, 968, 989, 1006, 1020, 1030, 1032, 1033, 1034, 1038, 1051, 1059], "method": [2, 3, 4, 5, 8, 27, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 74, 91, 101, 102, 105, 121, 130, 133, 142, 146, 157, 165, 170, 183, 189, 195, 196, 197, 223, 231, 236, 246, 254, 261, 264, 265, 268, 292, 348, 351, 358, 359, 366, 373, 376, 377, 382, 395, 398, 437, 455, 457, 461, 462, 464, 468, 469, 472, 474, 483, 484, 485, 486, 487, 489, 490, 491, 538, 546, 547, 556, 558, 563, 568, 580, 588, 614, 616, 627, 632, 633, 635, 639, 640, 641, 644, 645, 646, 647, 650, 660, 675, 691, 708, 713, 719, 729, 735, 744, 745, 829, 832, 846, 847, 860, 939, 940, 953, 1009, 1034, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057], "attribut": [2, 3, 4, 5, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 59, 60, 61, 62, 63, 64, 65, 66, 121, 254, 632, 633, 635, 640, 641, 645, 646, 647, 735, 1043, 1045, 1047, 1052, 1055, 1056, 1057], "A": [5, 28, 31, 52, 55, 73, 97, 101, 102, 103, 110, 112, 128, 152, 158, 173, 187, 213, 225, 226, 227, 231, 254, 260, 310, 318, 358, 359, 366, 376, 377, 385, 388, 389, 391, 398, 431, 437, 438, 449, 461, 462, 479, 482, 483, 484, 485, 486, 487, 489, 490, 491, 510, 511, 516, 517, 519, 525, 526, 543, 578, 592, 596, 639, 671, 677, 693, 710, 713, 735, 738, 786, 795, 847, 948, 949, 950, 952, 955, 956, 957, 981, 982, 987, 988, 990, 996, 997, 1014, 1051, 1058], "encod": [5, 66, 101, 102, 112, 215, 254, 286, 287, 289, 290, 375, 512, 639, 762, 983], "set": [5, 6, 7, 8, 9, 10, 12, 14, 15, 18, 23, 24, 26, 28, 30, 31, 32, 33, 34, 47, 48, 67, 68, 91, 93, 96, 101, 102, 105, 106, 110, 112, 114, 115, 116, 119, 120, 122, 123, 124, 125, 126, 127, 128, 130, 132, 137, 142, 151, 157, 158, 159, 160, 170, 173, 179, 198, 200, 206, 215, 217, 222, 223, 225, 231, 254, 268, 292, 355, 378, 387, 421, 422, 423, 424, 426, 430, 431, 440, 467, 471, 482, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 503, 504, 522, 528, 529, 534, 535, 536, 537, 540, 570, 574, 588, 589, 601, 602, 613, 616, 617, 618, 622, 627, 628, 630, 631, 639, 650, 660, 662, 670, 671, 672, 676, 677, 681, 685, 696, 701, 708, 713, 716, 719, 735, 744, 745, 836, 855, 868, 905, 906, 907, 908, 910, 914, 915, 921, 948, 949, 950, 951, 952, 953, 955, 956, 957, 959, 963, 970, 974, 975, 993, 999, 1000, 1005, 1006, 1007, 1008, 1011, 1032, 1051, 1059], "string": [5, 7, 9, 12, 13, 14, 18, 28, 29, 31, 33, 34, 38, 52, 58, 66, 75, 97, 101, 102, 103, 112, 121, 122, 123, 126, 129, 134, 152, 156, 157, 158, 159, 173, 187, 197, 200, 207, 216, 221, 222, 225, 227, 231, 234, 254, 261, 289, 295, 341, 345, 348, 351, 352, 365, 371, 375, 383, 414, 440, 451, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 509, 510, 513, 514, 515, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 543, 564, 565, 566, 567, 573, 576, 578, 579, 585, 586, 588, 589, 596, 606, 607, 610, 611, 619, 622, 623, 624, 627, 628, 630, 639, 653, 662, 669, 670, 671, 672, 677, 696, 702, 707, 713, 716, 717, 735, 738, 764, 770, 822, 826, 829, 832, 833, 845, 861, 898, 979, 980, 981, 985, 986, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1010, 1012, 1013, 1014, 1030, 1051, 1059], "classmethod": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 375, 639, 669, 692, 735], "activ": [6, 10, 16, 17, 19, 20, 21, 22, 25, 409, 517, 893, 988], "bool": [6, 10, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 30, 31, 33, 35, 47, 48, 67, 73, 74, 75, 90, 91, 95, 97, 101, 102, 106, 109, 110, 112, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, 129, 132, 134, 135, 136, 148, 149, 151, 152, 153, 155, 156, 157, 158, 159, 163, 164, 166, 167, 168, 173, 185, 187, 197, 198, 201, 205, 207, 213, 215, 217, 218, 221, 222, 223, 224, 227, 229, 231, 238, 240, 241, 244, 254, 263, 264, 265, 266, 267, 268, 278, 284, 286, 287, 289, 290, 293, 304, 305, 306, 307, 308, 309, 310, 328, 344, 346, 352, 357, 358, 359, 360, 361, 362, 376, 377, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 398, 401, 402, 406, 409, 427, 430, 432, 437, 438, 444, 445, 446, 447, 451, 461, 462, 464, 471, 474, 476, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 493, 495, 498, 499, 503, 505, 506, 510, 512, 514, 524, 525, 526, 530, 531, 533, 535, 536, 537, 540, 545, 557, 560, 563, 564, 565, 566, 567, 568, 570, 573, 574, 580, 583, 588, 589, 601, 602, 604, 613, 616, 622, 627, 628, 631, 639, 653, 655, 657, 662, 664, 666, 670, 671, 672, 676, 677, 681, 685, 690, 697, 700, 702, 707, 708, 709, 713, 721, 723, 724, 727, 735, 738, 742, 743, 744, 745, 754, 760, 762, 767, 774, 781, 782, 783, 784, 785, 786, 805, 825, 827, 833, 839, 841, 842, 843, 850, 854, 861, 862, 863, 864, 865, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 881, 885, 886, 890, 891, 893, 911, 914, 916, 935, 936, 938, 940, 941, 942, 943, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 959, 961, 963, 964, 969, 974, 976, 981, 983, 985, 995, 996, 997, 1001, 1002, 1004, 1006, 1007, 1008, 1011, 1031, 1032, 1033, 1036, 1039, 1041, 1051, 1058], "decim": [6, 28, 31, 254, 492, 538, 639, 958, 1009, 1051], "temporari": 6, "remov": [6, 8, 140, 215, 226, 254, 268, 363, 439, 522, 528, 534, 535, 537, 593, 639, 659, 710, 735, 744, 993, 999, 1005, 1006, 1008, 1051], "later": [6, 588], "onc": [6, 55, 101, 102, 105, 128, 132, 133, 196, 198, 234, 254, 268, 292, 493, 639, 654, 717, 735, 745, 959, 1051], "stabil": 6, "happen": [6, 471, 639, 938, 1051], "being": [6, 101, 102, 112, 117, 215, 225, 226, 254, 268, 309, 345, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 592, 639, 710, 735, 785, 826, 857, 938, 1051, 1059], "consid": [6, 101, 102, 112, 117, 133, 142, 153, 179, 196, 223, 225, 226, 254, 268, 298, 299, 300, 309, 345, 438, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 639, 660, 673, 680, 685, 708, 710, 719, 735, 745, 775, 776, 777, 785, 826, 857, 870, 938, 961, 962, 963, 1051], "break": [6, 117, 225, 226, 254, 268, 309, 310, 345, 471, 483, 484, 485, 486, 487, 489, 490, 491, 639, 710, 735, 785, 786, 826, 857, 938, 1051], "chang": [6, 26, 67, 101, 102, 117, 225, 226, 227, 254, 268, 309, 312, 345, 466, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 639, 710, 735, 744, 785, 826, 857, 934, 938, 969, 1051], "current": [6, 9, 26, 54, 91, 97, 103, 129, 132, 135, 136, 172, 254, 324, 345, 431, 466, 639, 650, 655, 656, 676, 735, 738, 774, 778, 793, 801, 826, 934, 1051, 1058], "alpha": [6, 73, 268, 360, 361, 362, 639, 657, 662, 664, 690, 700, 735, 841, 842, 843, 1051], "state": [6, 8, 73, 83, 129, 583, 650, 657, 662, 664, 690, 700, 735], "cfg": [7, 8, 9, 15, 130], "path": [7, 9, 27, 28, 29, 30, 31, 32, 33, 34, 35, 47, 48, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 254, 453, 495, 519, 639, 692, 700, 716, 735, 964, 990, 1051], "previous": 7, "save": [7, 158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 650, 671, 672, 677, 735, 801, 822, 826, 833], "share": [7, 58, 144, 254, 840, 1051], "option": [7, 8, 9, 26, 30, 31, 39, 67, 101, 104, 105, 106, 110, 113, 114, 116, 118, 121, 122, 123, 124, 125, 126, 127, 169, 173, 179, 213, 217, 222, 254, 396, 482, 483, 484, 485, 486, 487, 489, 490, 491, 503, 529, 604, 622, 630, 639, 650, 676, 677, 685, 735, 738, 877, 881, 948, 949, 950, 951, 952, 953, 955, 956, 957, 974, 1000, 1029, 1051], "from": [7, 8, 28, 31, 52, 53, 54, 67, 74, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 112, 113, 114, 115, 116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 139, 140, 144, 146, 151, 156, 158, 159, 170, 171, 173, 174, 179, 187, 191, 195, 197, 198, 200, 215, 222, 223, 225, 227, 254, 304, 311, 316, 317, 318, 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 359, 363, 375, 382, 396, 431, 459, 460, 462, 466, 467, 473, 483, 484, 486, 489, 490, 491, 493, 506, 516, 517, 518, 524, 535, 536, 537, 540, 558, 560, 571, 572, 576, 587, 588, 589, 590, 591, 616, 621, 626, 627, 628, 638, 639, 659, 664, 669, 671, 672, 677, 685, 692, 693, 696, 708, 714, 735, 738, 744, 791, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 840, 846, 877, 881, 915, 930, 931, 932, 934, 959, 987, 988, 989, 995, 1006, 1007, 1008, 1011, 1039, 1042, 1051, 1058, 1059], "json": [7, 9, 33, 34, 108, 109, 115, 254, 375, 453, 518, 519, 639, 650, 669, 692, 716, 735, 989, 990], "file": [7, 9, 27, 28, 32, 33, 34, 35, 47, 48, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 146, 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254, 465, 475, 498, 639, 735, 744, 745, 846, 941, 968, 969, 1051], "error": [13, 30, 76, 101, 102, 112, 158, 159, 173, 187, 195, 227, 254, 263, 287, 293, 341, 345, 352, 395, 414, 430, 483, 484, 485, 486, 487, 489, 490, 491, 510, 512, 516, 518, 519, 535, 536, 537, 540, 639, 671, 672, 677, 719, 735, 762, 767, 822, 826, 833, 898, 914, 981, 983, 987, 989, 990, 1006, 1007, 1008, 1011, 1051], "row": [13, 18, 23, 28, 31, 33, 35, 48, 67, 68, 70, 74, 82, 84, 89, 93, 94, 96, 97, 101, 102, 105, 106, 110, 112, 114, 115, 116, 119, 122, 124, 133, 134, 135, 142, 146, 149, 152, 156, 157, 158, 160, 161, 166, 168, 169, 170, 171, 173, 174, 175, 179, 183, 197, 198, 206, 210, 211, 214, 216, 221, 223, 225, 226, 232, 236, 239, 248, 254, 268, 279, 365, 379, 400, 410, 431, 465, 466, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 504, 506, 515, 518, 549, 570, 573, 583, 584, 595, 599, 601, 602, 615, 617, 618, 625, 630, 639, 653, 655, 657, 660, 664, 667, 668, 671, 673, 677, 678, 680, 681, 685, 701, 705, 706, 707, 708, 710, 715, 719, 722, 731, 735, 744, 845, 846, 856, 880, 894, 934, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 975, 986, 989, 1021, 1051, 1059], "per": [13, 28, 31, 122, 124, 134, 156, 183, 184, 207, 221, 243, 245, 246, 254, 268, 292, 409, 412, 430, 506, 573, 639, 653, 702, 707, 726, 728, 729, 735, 893, 896, 914], "everi": [13, 101, 102, 112, 158, 211, 214, 227, 254, 304, 305, 306, 307, 308, 309, 345, 352, 365, 403, 404, 408, 410, 412, 413, 421, 426, 429, 515, 551, 583, 584, 639, 664, 671, 706, 735, 781, 782, 783, 784, 785, 826, 833, 845, 887, 888, 892, 894, 896, 897, 905, 910, 913, 986, 1023, 1051], "process": [13, 28, 47, 48, 128, 254, 735], "liter": [14, 18, 30, 52, 105, 124, 125, 126, 127, 156, 170, 176, 177, 181, 182, 185, 195, 196, 200, 209, 213, 226, 231, 254, 263, 286, 318, 358, 359, 366, 373, 376, 377, 383, 398, 437, 440, 455, 457, 461, 462, 468, 510, 525, 526, 546, 556, 564, 565, 566, 567, 570, 574, 576, 578, 579, 585, 586, 587, 588, 589, 590, 601, 602, 604, 606, 607, 610, 611, 613, 616, 619, 622, 623, 624, 626, 627, 628, 631, 639, 666, 696, 710, 713, 735, 738, 761, 795, 847, 861, 962, 963, 981, 996, 997, 1051, 1058], "left": [14, 31, 54, 67, 119, 120, 158, 159, 172, 173, 226, 254, 360, 361, 362, 382, 383, 483, 484, 485, 486, 487, 489, 490, 491, 494, 521, 543, 554, 576, 583, 584, 588, 589, 595, 615, 627, 628, 630, 639, 671, 672, 676, 677, 710, 735, 841, 842, 843, 861, 960, 992, 1014, 1034, 1051], "center": [14, 31, 254, 360, 361, 362, 482, 483, 484, 485, 486, 487, 489, 490, 491, 639, 841, 842, 843, 948, 949, 950, 951, 952, 953, 955, 956, 957, 1051], "right": [14, 16, 31, 101, 102, 119, 120, 158, 159, 172, 173, 254, 310, 360, 361, 362, 383, 421, 422, 423, 424, 471, 483, 484, 485, 486, 487, 489, 490, 491, 494, 503, 527, 576, 588, 589, 627, 628, 639, 671, 672, 676, 677, 735, 786, 841, 842, 843, 861, 905, 906, 907, 908, 938, 960, 974, 998, 1051], "cell": [14, 31, 254], "align": [14, 31, 67, 74, 254, 543, 1014], "keyerror": [14, 18], "recognis": [14, 18, 121], "column_abc": 14, "column_xyz": 14, "visibl": [15, 144, 254, 840, 1051], "eg": [15, 23, 31, 103, 254, 345, 535, 537, 556, 639, 1006, 1008], "low": [15, 128], "rang": [15, 31, 103, 139, 144, 158, 171, 254, 310, 311, 322, 336, 342, 343, 345, 352, 353, 382, 471, 570, 578, 587, 588, 589, 590, 601, 602, 626, 627, 628, 639, 671, 735, 786, 787, 799, 817, 823, 824, 826, 833, 834, 840, 934, 938, 1051], "100": [15, 31, 93, 96, 101, 102, 112, 115, 254, 518, 538, 543, 735, 949, 950, 952, 989, 1009, 1051, 1059], "98": [15, 164, 254, 291, 505, 538, 550, 555, 639, 1009], "99": [15, 31, 147, 148, 164, 167, 254, 262, 291, 366, 368, 505, 550, 555, 639, 665, 666, 735, 839, 847, 1051], "tbl_col": 15, "10": [15, 27, 28, 30, 31, 32, 35, 48, 52, 67, 74, 97, 103, 112, 124, 126, 136, 146, 155, 158, 159, 161, 163, 164, 165, 182, 186, 188, 192, 193, 200, 210, 231, 234, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 263, 267, 276, 277, 278, 280, 298, 303, 305, 308, 310, 312, 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[18, 67, 97], "utf8_full_condens": [18, 97], "utf8_no_bord": 18, "utf8_borders_onli": 18, "utf8_horizontal_onli": 18, "noth": [18, 292, 511, 516, 519, 639, 982, 987, 990], "rounded_corn": 18, "style": [18, 31, 187, 254], "border": 18, "line": [18, 31, 101, 102, 105, 112, 156, 166, 168, 254, 516, 987], "includ": [18, 26, 28, 30, 31, 72, 104, 113, 124, 134, 139, 144, 158, 185, 197, 221, 222, 225, 254, 310, 346, 383, 471, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 528, 530, 531, 534, 583, 617, 618, 639, 653, 671, 707, 735, 786, 787, 827, 840, 861, 938, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1001, 1002, 1005, 1041, 1051], "divid": [18, 345, 352, 360, 361, 362, 396, 639, 826, 833, 841, 842, 843, 881, 1051], "dens": [18, 156, 254, 474, 639, 940, 1051], "space": [18, 158, 254, 471, 639, 671, 735, 938, 1051], "horizont": [18, 74, 152, 163, 225, 254, 564, 565, 566, 567, 578, 579, 583, 584, 585, 586, 592, 595, 606, 607, 610, 611, 615, 623, 624], "markdown": 18, "compat": [18, 31, 35, 48, 254, 510, 511, 516, 517, 525, 526, 735, 738, 981, 982, 987, 988, 996, 997], "No": [18, 541, 1012], "appli": [18, 28, 31, 52, 74, 112, 121, 124, 126, 152, 186, 254, 320, 321, 322, 326, 328, 329, 330, 332, 334, 336, 339, 342, 343, 346, 349, 353, 354, 356, 361, 362, 438, 467, 482, 483, 484, 486, 490, 525, 535, 536, 537, 540, 563, 583, 584, 595, 605, 615, 630, 639, 673, 680, 681, 689, 735, 797, 798, 799, 803, 805, 806, 810, 812, 815, 817, 820, 823, 824, 827, 830, 834, 835, 837, 842, 843, 948, 949, 950, 952, 956, 996, 1006, 1007, 1008, 1011, 1051], "round": [18, 31, 69, 97, 254, 297, 372, 552, 639, 772, 852, 1051], "corner": [18, 31, 97, 254], "op": [18, 126, 254, 477, 535, 537, 639, 735, 1006, 1008, 1051], "one": [18, 29, 31, 57, 67, 82, 90, 92, 93, 94, 95, 96, 108, 109, 125, 126, 127, 130, 148, 149, 156, 157, 158, 159, 170, 179, 187, 195, 196, 201, 217, 220, 235, 254, 262, 368, 431, 435, 482, 505, 517, 550, 588, 620, 623, 630, 639, 666, 667, 670, 671, 672, 685, 697, 714, 718, 735, 744, 849, 877, 915, 919, 948, 988, 1015, 1051], "more": [18, 31, 33, 35, 48, 57, 67, 74, 89, 90, 91, 92, 93, 94, 95, 96, 103, 104, 108, 109, 110, 113, 124, 126, 130, 133, 138, 139, 170, 174, 179, 183, 195, 196, 198, 217, 223, 236, 254, 265, 268, 284, 298, 299, 300, 396, 432, 435, 438, 464, 493, 503, 517, 535, 536, 537, 557, 620, 623, 630, 639, 685, 708, 735, 738, 745, 760, 775, 776, 777, 787, 877, 881, 916, 959, 974, 988, 1006, 1007, 1008, 1036, 1051, 1059], "semigraph": 18, "box": [18, 133, 254], "draw": [18, 23, 24, 123, 493, 499, 639, 1059], "found": [18, 28, 54, 77, 86, 88, 93, 97, 143, 226, 254, 494, 519, 535, 537, 639, 710, 735, 960, 990, 1006, 1008, 1051, 1058], "unicod": 18, "block": [18, 157, 223, 254, 670, 693, 708, 715, 719, 735, 962, 963, 1051], "http": [18, 31, 91, 103, 132, 138, 254, 516, 987], "en": [18, 31, 254], "wikipedia": 18, "org": [18, 91, 103, 132, 138, 254], "wiki": 18, "drawing_charact": 18, "box_draw": 18, "mno": 18, "tbl_format": 18, "tbl_hide_column_data_typ": 18, "tbl_hide_dataframe_shap": 18, "hide": [19, 20, 21, 22, 31, 254], "etc": [19, 30, 31, 101, 104, 106, 110, 113, 114, 116, 254, 738, 962, 963, 1051], "inform": [21, 72, 104, 113, 138, 254, 298, 299, 300, 396, 503, 510, 516, 517, 525, 588, 589, 639, 690, 735, 775, 776, 777, 881, 974, 981, 987, 988, 996, 1051], "separ": [22, 28, 99, 101, 102, 112, 185, 187, 215, 222, 224, 254, 268, 410, 414, 515, 579, 583, 584, 639, 709, 735, 894, 898, 986, 1019, 1028, 1051], "between": [22, 74, 121, 122, 124, 126, 138, 189, 246, 254, 293, 313, 383, 414, 421, 422, 423, 424, 466, 471, 472, 487, 493, 499, 509, 571, 572, 580, 582, 614, 617, 618, 639, 691, 729, 735, 767, 789, 861, 898, 905, 906, 907, 908, 934, 938, 939, 953, 980, 1051], "set_tbl_column_data_type_inlin": 22, "max": [23, 31, 35, 48, 52, 128, 139, 148, 157, 158, 159, 187, 254, 298, 299, 305, 368, 431, 465, 474, 483, 495, 532, 607, 620, 639, 666, 670, 671, 672, 735, 775, 776, 781, 787, 849, 915, 940, 949, 964, 1003, 1051], "both": [23, 28, 58, 158, 159, 172, 173, 180, 195, 254, 267, 383, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 649, 671, 672, 676, 677, 686, 735, 861, 870, 1051], "tbl_row": 23, "char": [24, 58, 75, 517, 523, 988, 994], "enabl": [25, 75, 129, 200, 231, 254, 495, 639, 696, 713, 735, 964, 1051], "addit": [25, 30, 31, 93, 104, 113, 122, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 261, 324, 363, 366, 465, 506, 510, 516, 517, 525, 564, 566, 573, 576, 577, 578, 579, 585, 593, 606, 610, 619, 622, 623, 639, 659, 663, 670, 696, 697, 702, 709, 713, 717, 735, 793, 801, 847, 981, 987, 988, 996, 1051], "verbos": [25, 130, 517, 988], "debug": [25, 657, 664, 681, 735, 1059], "log": [25, 69, 291, 357, 435, 457, 468, 555, 639, 766, 839, 1035, 1051], "if_set": 26, "env_onli": 26, "dict": [26, 30, 31, 35, 55, 68, 70, 72, 90, 92, 93, 94, 95, 96, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 124, 151, 170, 185, 191, 195, 196, 197, 199, 213, 214, 222, 254, 264, 266, 310, 440, 471, 481, 639, 693, 695, 735, 921, 1051], "show": [26, 31, 56, 142, 156, 174, 184, 254, 660, 690, 700, 735], "variabl": [26, 49, 54, 125, 128, 179, 215, 254, 685, 735, 1028, 1051, 1058], "restrict": [26, 532, 588, 589, 1003], "dictionari": [26, 31, 90, 92, 93, 94, 96, 101, 102, 107, 108, 109, 111, 112, 170, 185, 195, 196, 197, 213, 214, 254, 440, 639, 735, 921, 1051], "those": [26, 31, 101, 197, 254, 474, 516, 639, 738, 940, 987, 1051], "been": [26, 31, 254, 292, 474, 483, 484, 485, 486, 487, 489, 490, 491, 570, 639, 940, 1051], "set_fmt_float": 26, "directli": [26, 54, 124, 126, 130, 197, 254, 360, 361, 362, 616, 639, 735, 841, 842, 843, 1051, 1059], "via": [26, 101, 102, 105, 112, 114, 115, 116, 170, 196, 254, 268, 639], "set_stat": 26, "all_stat": 26, "binaryio": [27, 32, 100, 101, 105, 106, 107, 110, 111, 254], "bytesio": [27, 28, 31, 32, 35, 100, 101, 102, 105, 106, 110, 254], "compress": [27, 32, 35, 47, 48, 254, 735], "avrocompress": [27, 254], "uncompress": [27, 32, 35, 48, 106, 114, 254, 735], "write": [27, 28, 29, 30, 31, 32, 33, 35, 48, 102, 106, 130, 254, 298, 299, 300, 453, 639, 679, 700, 716, 735, 775, 776, 777, 1051], "apach": [27, 35, 100, 103, 254], "avro": [27, 100, 254, 650], "should": [27, 28, 29, 31, 32, 33, 34, 35, 47, 48, 74, 90, 92, 94, 96, 104, 108, 109, 112, 121, 122, 126, 132, 133, 134, 140, 158, 159, 170, 173, 195, 196, 197, 214, 215, 221, 224, 227, 236, 254, 262, 268, 295, 309, 310, 341, 345, 352, 355, 363, 389, 391, 409, 471, 482, 483, 484, 485, 486, 487, 489, 490, 491, 494, 503, 577, 593, 600, 604, 617, 618, 630, 639, 653, 659, 671, 672, 676, 677, 681, 700, 702, 707, 709, 716, 735, 738, 745, 770, 785, 786, 822, 826, 833, 836, 893, 938, 948, 949, 950, 951, 952, 953, 955, 956, 957, 960, 974, 1051], "snappi": [27, 35, 48, 254, 735], "deflat": [27, 254], "import": [27, 28, 31, 32, 35, 38, 67, 90, 94, 95, 112, 117, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 139, 156, 158, 171, 173, 217, 218, 225, 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149, 150, 153, 160, 161, 163, 172, 176, 177, 178, 181, 184, 189, 191, 193, 195, 198, 199, 200, 203, 204, 206, 208, 209, 210, 215, 216, 217, 218, 219, 223, 228, 229, 254, 294, 577, 658, 659, 660, 661, 667, 676, 688, 693, 695, 696, 708, 714, 735, 769], "d": [27, 28, 30, 31, 32, 35, 58, 75, 93, 117, 139, 156, 158, 160, 161, 164, 172, 210, 212, 225, 229, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 261, 263, 325, 348, 351, 383, 467, 498, 511, 517, 531, 535, 536, 537, 576, 597, 639, 671, 676, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 802, 829, 832, 861, 982, 988, 1002, 1006, 1007, 1008, 1051], "e": [27, 28, 30, 32, 35, 58, 75, 91, 101, 102, 105, 106, 110, 114, 116, 139, 156, 158, 159, 161, 173, 210, 212, 217, 225, 227, 254, 261, 268, 341, 345, 352, 357, 363, 383, 433, 440, 483, 484, 485, 486, 487, 489, 490, 491, 498, 503, 593, 630, 639, 671, 672, 677, 679, 693, 735, 738, 822, 826, 833, 839, 861, 961, 974, 1032, 1051], "dirpath": [27, 28, 32, 35, 112, 254], "new_fil": [27, 28, 32, 35, 254], "has_head": [28, 31, 101, 102, 105, 112, 254], "quot": [28, 29, 101, 102, 112, 254], "batch_siz": [28, 101, 102, 115, 254], "1024": [28, 48, 101, 102, 115, 254, 735], "datetime_format": [28, 254], "date_format": [28, 254], "time_format": [28, 254], "float_precis": [28, 31, 254], "null_valu": [28, 101, 102, 112, 254], "textiowrapp": [28, 254], "comma": [28, 254], "csv": [28, 47, 48, 101, 102, 105, 112, 254, 650, 735], "result": [28, 33, 34, 47, 48, 67, 74, 94, 96, 103, 104, 112, 114, 116, 117, 126, 146, 158, 159, 172, 183, 197, 204, 218, 227, 234, 254, 268, 310, 348, 351, 360, 361, 362, 366, 396, 431, 438, 465, 471, 478, 482, 483, 484, 485, 486, 487, 489, 490, 491, 495, 497, 530, 531, 537, 556, 570, 583, 584, 588, 589, 605, 613, 616, 617, 618, 631, 639, 654, 671, 672, 676, 681, 690, 699, 716, 717, 735, 744, 745, 829, 832, 841, 842, 843, 846, 881, 938, 944, 948, 949, 950, 951, 952, 953, 955, 956, 957, 964, 967, 1001, 1002, 1008, 1032, 1051, 1058, 1059], "If": [28, 29, 30, 31, 32, 33, 34, 35, 48, 52, 58, 72, 74, 90, 91, 92, 93, 94, 95, 96, 101, 102, 104, 105, 106, 108, 109, 110, 112, 113, 114, 115, 116, 125, 132, 133, 134, 142, 146, 156, 158, 159, 161, 169, 170, 173, 175, 179, 183, 196, 197, 198, 206, 210, 214, 215, 217, 221, 222, 223, 225, 226, 254, 264, 266, 268, 298, 299, 300, 310, 318, 337, 338, 352, 369, 396, 426, 431, 438, 440, 451, 465, 471, 474, 476, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 493, 494, 499, 503, 504, 518, 520, 522, 523, 528, 529, 530, 531, 532, 534, 535, 536, 537, 540, 564, 566, 568, 570, 574, 580, 581, 583, 585, 588, 589, 595, 601, 602, 604, 606, 610, 613, 616, 617, 618, 622, 623, 627, 628, 630, 631, 639, 653, 660, 662, 671, 672, 676, 677, 681, 685, 693, 701, 707, 708, 710, 716, 719, 735, 738, 744, 745, 775, 776, 777, 786, 795, 818, 819, 833, 846, 854, 856, 857, 880, 881, 883, 910, 915, 938, 940, 942, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 959, 960, 964, 970, 974, 975, 989, 991, 993, 994, 999, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1011, 1021, 1027, 1032, 1041, 1051, 1058], "instead": [28, 33, 34, 52, 56, 67, 74, 101, 110, 124, 133, 158, 159, 170, 173, 183, 185, 195, 196, 197, 200, 218, 227, 231, 254, 278, 310, 341, 345, 352, 471, 483, 484, 485, 486, 487, 489, 490, 491, 520, 522, 528, 534, 564, 566, 570, 574, 585, 588, 589, 601, 602, 606, 610, 613, 616, 622, 623, 627, 628, 631, 639, 665, 671, 672, 677, 696, 713, 716, 735, 738, 754, 786, 822, 826, 833, 938, 962, 963, 991, 993, 999, 1005, 1033, 1051, 1058], "whether": [28, 94, 96, 126, 134, 201, 221, 254, 310, 328, 346, 401, 402, 445, 446, 471, 495, 639, 653, 681, 697, 702, 707, 735, 738, 786, 805, 827, 885, 886, 938, 1051, 1058], "header": [28, 31, 35, 48, 97, 101, 102, 105, 112, 143, 187, 222, 254, 735], "field": [28, 59, 86, 88, 93, 217, 224, 254, 431, 440, 480, 517, 518, 531, 532, 545, 583, 584, 605, 622, 639, 709, 735, 786, 915, 938, 946, 1000, 1002, 1003, 1017, 1019, 1051], "symbol": [28, 254], "byte": [28, 48, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 144, 254, 286, 289, 290, 520, 523, 735, 761, 764, 765, 840, 991, 994, 1051], "specifi": [28, 31, 67, 77, 86, 88, 105, 124, 134, 140, 144, 145, 148, 157, 158, 159, 172, 185, 195, 197, 200, 201, 207, 221, 224, 231, 234, 254, 360, 361, 362, 363, 368, 440, 465, 477, 483, 484, 485, 486, 487, 489, 490, 491, 506, 521, 527, 564, 566, 573, 576, 577, 578, 579, 585, 588, 589, 593, 606, 610, 619, 622, 623, 627, 628, 639, 653, 659, 663, 666, 670, 671, 672, 676, 696, 697, 702, 707, 709, 713, 717, 735, 840, 841, 842, 843, 849, 992, 998, 1051], "defin": [28, 31, 38, 121, 122, 124, 133, 158, 159, 183, 186, 236, 254, 268, 383, 430, 467, 481, 483, 484, 485, 486, 487, 489, 490, 491, 568, 588, 589, 604, 622, 627, 628, 639, 671, 672, 689, 719, 735, 738, 745, 861, 914, 947, 1051], "chrono": [28, 254, 348, 351, 535, 536, 537, 540, 829, 832, 1006, 1007, 1008, 1011], "rust": [28, 35, 83, 106, 110, 133, 236, 254, 745, 1051], "crate": [28, 254, 510, 511, 516, 517, 525, 526, 535, 536, 537, 540, 738, 981, 982, 987, 988, 996, 997, 1006, 1007, 1008, 1011], "fraction": [28, 119, 120, 198, 254, 346, 466, 493, 535, 537, 639, 827, 934, 959, 1006, 1008, 1051], "second": [28, 123, 158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 535, 537, 590, 591, 626, 630, 639, 671, 672, 677, 735, 738, 822, 826, 833, 1006, 1008, 1059], "precis": [28, 31, 38, 39, 170, 196, 197, 214, 254, 317, 538, 738, 794, 1009], "infer": [28, 90, 92, 93, 94, 95, 96, 101, 102, 105, 108, 109, 112, 115, 133, 254, 478, 518, 535, 536, 537, 538, 540, 616, 639, 735, 944, 989, 1006, 1007, 1008, 1009, 1011, 1051], "maximum": [28, 101, 102, 112, 122, 123, 124, 126, 176, 254, 403, 441, 459, 474, 606, 607, 639, 682, 735, 775, 807, 887, 922, 930, 935, 940, 1051], "timeunit": [28, 38, 40, 254, 317, 318, 350, 355, 537, 588, 589, 738, 794, 795, 831, 836, 1008], "frame": [28, 29, 31, 52, 53, 54, 55, 56, 57, 67, 74, 93, 119, 124, 133, 135, 170, 171, 180, 183, 186, 195, 196, 197, 218, 225, 254, 655, 686, 689, 735, 738, 774, 1051, 1058, 1059], "datetim": [28, 31, 67, 97, 124, 139, 156, 158, 159, 171, 173, 227, 254, 316, 317, 318, 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 406, 483, 484, 485, 486, 487, 489, 490, 491, 535, 537, 554, 588, 589, 591, 597, 604, 627, 628, 639, 671, 672, 677, 735, 738, 792, 793, 794, 795, 796, 797, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 877, 890, 891, 930, 931, 963, 1006, 1008, 1034, 1051], "place": [28, 134, 141, 146, 163, 164, 187, 192, 197, 203, 204, 207, 221, 229, 254, 278, 414, 425, 496, 497, 505, 639, 653, 698, 699, 702, 707, 735, 744, 754, 846, 898, 909, 941, 943, 966, 967, 976, 1051], "float64": [28, 31, 93, 101, 124, 143, 144, 152, 199, 218, 231, 254, 270, 271, 272, 273, 274, 275, 293, 301, 302, 311, 346, 363, 389, 391, 473, 501, 502, 552, 553, 577, 593, 613, 616, 631, 639, 661, 695, 713, 735, 738, 827, 1033, 1038, 1051], "repres": [28, 50, 65, 90, 92, 94, 95, 96, 208, 228, 233, 254, 389, 391, 508, 561, 564, 577, 580, 593, 604, 617, 618, 621, 629, 639, 703, 711, 735, 963, 978, 1040, 1051], "empti": [28, 81, 93, 101, 102, 105, 112, 135, 136, 158, 167, 179, 254, 604, 619, 655, 656, 671, 685, 735, 738, 774, 778, 864, 1051], "table_nam": [29, 31, 254], "connect": [29, 101, 103, 106, 110, 114, 116, 117, 254, 651], "if_exist": [29, 254], "dbwritemod": [29, 254], "fail": [29, 30, 91, 104, 106, 109, 113, 132, 223, 254, 279, 349, 431, 535, 536, 537, 540, 639, 708, 735, 745, 1006, 1007, 1008, 1011, 1051], "dbwriteengin": [29, 254], "sqlalchemi": [29, 254], "databas": [29, 103, 254, 650], "creat": [29, 31, 90, 94, 96, 113, 122, 123, 124, 125, 126, 127, 135, 136, 158, 159, 184, 187, 227, 231, 254, 318, 345, 352, 431, 475, 483, 484, 486, 489, 490, 491, 529, 560, 578, 587, 588, 589, 590, 591, 626, 627, 628, 639, 655, 656, 662, 671, 672, 713, 735, 774, 778, 791, 795, 826, 833, 932, 941, 1000, 1032, 1051, 1058, 1059], "append": [29, 30, 124, 146, 172, 173, 254, 310, 471, 475, 588, 589, 630, 639, 676, 677, 735, 846, 1051], "your": [29, 31, 67, 101, 102, 119, 120, 133, 170, 196, 197, 200, 214, 231, 234, 236, 254, 268, 535, 536, 537, 568, 639, 657, 673, 680, 681, 696, 713, 717, 719, 735, 745, 1006, 1007, 1008, 1051, 1059], "special": [29, 101, 102, 112, 254, 517, 745, 988, 1051], "uri": [29, 30, 103, 104, 113, 254], "postgresql": [29, 103, 254, 465, 639], "user": [29, 103, 133, 186, 236, 254, 268, 438, 467, 495, 568, 588, 639, 689, 719, 735, 745, 964, 1051], "pass": [29, 31, 35, 55, 67, 92, 103, 105, 112, 122, 124, 126, 134, 138, 140, 157, 158, 159, 161, 175, 185, 186, 200, 207, 210, 221, 227, 231, 234, 254, 268, 325, 344, 366, 381, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 522, 528, 534, 564, 566, 573, 577, 585, 588, 597, 606, 610, 622, 623, 639, 653, 659, 670, 671, 672, 674, 681, 689, 696, 700, 702, 707, 713, 717, 719, 735, 745, 802, 825, 847, 856, 883, 993, 999, 1005, 1021, 1051], "server": [29, 103, 254], "port": [29, 101, 103, 106, 110, 114, 116, 254], "sqlite": [29, 254], "db": [29, 103, 254], "replac": [29, 30, 101, 102, 112, 147, 148, 193, 222, 231, 254, 318, 344, 440, 526, 570, 639, 665, 713, 735, 795, 825, 921, 962, 963, 997, 1051], "insert": [29, 101, 102, 106, 110, 112, 114, 115, 116, 164, 192, 222, 224, 254, 494, 509, 543, 639, 709, 735, 960, 980, 1014, 1051], "mode": [29, 30, 52, 254, 517, 613, 616, 631, 639, 735, 988, 1051, 1058], "new": [29, 30, 31, 112, 130, 133, 142, 163, 164, 183, 184, 191, 192, 211, 222, 224, 225, 231, 254, 263, 318, 365, 382, 439, 525, 526, 531, 532, 544, 545, 551, 570, 639, 650, 660, 693, 706, 709, 713, 719, 735, 741, 791, 795, 845, 932, 943, 996, 997, 1002, 1003, 1015, 1017, 1023, 1029, 1051, 1058], "alreadi": [29, 30, 254, 309, 409, 639, 785, 893, 1051], "adbc": [29, 103, 254], "deltalak": [30, 104, 113, 118, 254], "deltat": [30, 254], "ignor": [30, 123, 124, 126, 177, 209, 254, 312, 344, 360, 361, 362, 408, 459, 460, 639, 788, 825, 841, 842, 843, 892, 930, 931, 1051], "overwrite_schema": [30, 254], "storage_opt": [30, 101, 104, 106, 110, 113, 114, 116, 254], "delta_write_opt": [30, 254], "delta": [30, 40, 104, 113, 208, 228, 254, 489, 491, 508, 561, 580, 617, 618, 621, 629, 639, 650, 703, 711, 735, 955, 957, 978, 1040, 1051], "like": [30, 91, 100, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 166, 168, 172, 217, 254, 316, 409, 452, 474, 516, 588, 589, 623, 627, 628, 639, 664, 692, 735, 744, 793, 893, 940, 987, 1051], "categor": [30, 58, 75, 172, 215, 216, 254, 294, 295, 440, 554, 639, 738, 768, 769, 770, 786, 938, 1034, 1051], "protocol": [30, 91, 103, 132, 254], "object": [30, 31, 32, 35, 57, 74, 91, 92, 97, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 113, 122, 124, 126, 132, 157, 158, 159, 174, 197, 217, 218, 254, 588, 589, 604, 627, 628, 650, 671, 672, 692, 735, 738, 768, 792, 884, 979, 1027, 1051], "handl": [30, 74, 97, 101, 102, 112, 117, 254, 312, 408, 543, 639, 788, 892, 1014, 1051], "throw": [30, 91, 254, 293, 518, 519, 639, 767, 989, 990, 1051], "add": [30, 31, 102, 133, 146, 158, 231, 232, 254, 469, 547, 591, 595, 630, 639, 671, 676, 713, 714, 715, 735, 744, 846, 1051], "anyth": [30, 195, 254, 517, 988], "updat": [30, 254, 735], "extra": [30, 35, 48, 101, 104, 105, 106, 110, 113, 114, 116, 146, 158, 254, 671, 735, 744, 846, 1051], "storag": [30, 101, 104, 106, 110, 113, 114, 116, 254], "backend": [30, 103, 104, 113, 254], "cloud": [30, 104, 113, 117, 254], "configur": [30, 104, 113, 254], "authent": [30, 104, 113, 254], "see": [30, 31, 38, 103, 104, 105, 113, 119, 120, 124, 126, 138, 172, 186, 254, 298, 299, 300, 368, 396, 438, 503, 510, 516, 517, 525, 588, 589, 615, 639, 738, 775, 776, 777, 881, 974, 981, 987, 988, 996, 1051, 1059], "here": [30, 31, 35, 90, 92, 93, 94, 96, 103, 104, 108, 109, 113, 122, 124, 126, 254, 519, 735, 990], "gc": [30, 104, 113, 254], "azur": [30, 104, 113, 254], "keyword": [30, 55, 104, 110, 113, 138, 186, 195, 200, 231, 234, 254, 467, 619, 622, 639, 689, 696, 713, 717, 735, 1051], "argument": [30, 35, 101, 104, 110, 113, 138, 140, 145, 157, 158, 159, 177, 185, 186, 187, 200, 201, 207, 209, 218, 224, 227, 231, 234, 254, 345, 352, 363, 383, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 522, 528, 534, 564, 566, 571, 572, 573, 576, 577, 578, 579, 585, 588, 593, 606, 610, 616, 619, 622, 623, 627, 639, 659, 663, 670, 671, 672, 689, 696, 697, 702, 709, 713, 717, 735, 744, 826, 833, 861, 993, 999, 1005, 1033, 1051], "while": [30, 102, 104, 105, 113, 124, 126, 170, 179, 222, 254, 685, 735], "lake": [30, 104, 113, 254, 650], "instanti": [30, 31, 200, 231, 254, 696, 713, 735], "basic": [30, 31, 254, 1059], "filesystem": [30, 104, 113, 254], "table_path": [30, 104, 113, 254], "doe": [30, 67, 74, 84, 90, 92, 93, 94, 96, 97, 101, 102, 104, 105, 108, 109, 112, 113, 117, 119, 120, 146, 171, 172, 195, 196, 197, 223, 231, 254, 292, 355, 431, 440, 558, 588, 594, 603, 639, 664, 676, 681, 708, 713, 735, 744, 836, 846, 854, 921, 969, 1041, 1051], "match": [30, 31, 38, 74, 84, 90, 92, 93, 94, 96, 108, 109, 119, 120, 148, 173, 195, 254, 446, 488, 510, 511, 514, 516, 517, 518, 519, 525, 526, 533, 535, 536, 537, 577, 639, 666, 677, 735, 738, 870, 877, 954, 981, 982, 985, 987, 988, 989, 990, 996, 997, 1004, 1006, 1007, 1008, 1051], "version": [30, 72, 104, 113, 118, 254, 292, 337, 338, 535, 537, 570, 588, 615, 616, 627, 639, 744, 818, 819, 1006, 1008, 1051], "old": [30, 191, 254, 693, 735], "existing_table_path": [30, 254], "store": [30, 101, 110, 146, 170, 196, 254, 294, 744, 769, 846, 1051], "bucket": [30, 104, 113, 254, 345, 352, 826, 833, 857, 1051], "prefix": [30, 130, 254, 263, 290, 439, 533, 543, 547, 639, 738, 765, 1004, 1014], "aws_region": [30, 113, 254], "the_aws_region": [30, 254], "aws_access_key_id": [30, 104, 113, 254], "the_aws_access_key_id": [30, 104, 113, 254], "aws_secret_access_kei": [30, 104, 113, 254], "the_aws_secret_access_kei": [30, 104, 113, 254], "workbook": [31, 254], "worksheet": [31, 254], "posit": [31, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 360, 361, 362, 363, 465, 471, 506, 524, 564, 566, 571, 572, 573, 576, 577, 578, 579, 585, 593, 606, 610, 619, 622, 623, 639, 659, 663, 670, 696, 697, 702, 709, 713, 717, 735, 841, 842, 843, 938, 995, 1051], "tupl": [31, 103, 133, 170, 195, 196, 197, 202, 233, 254, 478, 639, 690, 700, 735, 738, 944, 1051], "a1": [31, 68, 70, 254], "table_styl": [31, 254], "column_format": [31, 254], "dtype_format": [31, 254], "oneormoredatatyp": [31, 122, 254, 877, 1051], "conditional_format": [31, 254], "conditionalformatdict": [31, 254], "column_tot": [31, 254], "columntotalsdefinit": [31, 254], "column_width": [31, 254], "row_tot": [31, 254], "rowtotalsdefinit": [31, 254], "row_height": [31, 254], "sparklin": [31, 254], "sequenc": [31, 59, 67, 73, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 122, 123, 124, 134, 139, 145, 146, 172, 173, 179, 183, 186, 187, 197, 207, 215, 221, 223, 224, 225, 226, 227, 254, 387, 431, 438, 467, 506, 545, 568, 573, 583, 584, 595, 597, 605, 615, 620, 639, 653, 663, 676, 677, 689, 702, 707, 708, 709, 710, 735, 744, 787, 789, 846, 915, 963, 1017, 1051], "formula": [31, 254, 357, 639, 839, 1051], "autofilt": [31, 254], "autofit": [31, 254], "hidden_column": [31, 254], "hide_gridlin": [31, 254], "sheet_zoom": [31, 254], "freeze_pan": [31, 254], "excel": [31, 105, 254, 650], "open": [31, 101, 102, 105, 106, 110, 114, 116, 254], "xlsxwriter": [31, 118, 254], "ha": [31, 67, 112, 132, 158, 159, 227, 254, 268, 291, 292, 309, 449, 483, 484, 485, 486, 487, 489, 490, 491, 555, 568, 570, 639, 671, 672, 719, 735, 766, 774, 785, 787, 854, 867, 1035, 1051], "close": [31, 158, 159, 254, 383, 435, 483, 484, 485, 486, 487, 489, 490, 491, 503, 588, 589, 627, 628, 639, 671, 672, 735, 861, 974, 1051], "xlsx": [31, 105, 254], "work": [31, 39, 102, 105, 192, 254, 268, 284, 297, 298, 299, 300, 363, 372, 409, 432, 465, 481, 523, 557, 639, 760, 772, 775, 776, 777, 852, 893, 916, 994, 1036, 1051], "directori": [31, 35, 110, 254], "sheet1": [31, 254], "valid": [31, 38, 52, 106, 110, 126, 130, 144, 172, 254, 309, 510, 511, 516, 517, 519, 525, 526, 588, 589, 639, 676, 735, 738, 785, 840, 854, 981, 982, 987, 988, 990, 996, 997, 1051], "notat": [31, 254], "integ": [31, 43, 44, 45, 46, 61, 62, 63, 64, 122, 124, 125, 127, 158, 159, 254, 265, 297, 346, 372, 373, 464, 471, 476, 483, 484, 485, 486, 487, 488, 489, 490, 491, 524, 563, 570, 597, 601, 602, 616, 639, 671, 672, 735, 738, 772, 827, 852, 870, 938, 942, 954, 963, 995, 1051, 1058, 1059], "medium": [31, 254], "kei": [31, 67, 72, 74, 158, 170, 172, 173, 180, 185, 187, 191, 194, 196, 197, 254, 622, 671, 676, 677, 686, 693, 694, 735], "follow": [31, 35, 72, 101, 102, 104, 112, 113, 133, 158, 159, 173, 186, 227, 254, 268, 341, 345, 352, 467, 474, 483, 484, 485, 486, 487, 488, 489, 490, 491, 545, 556, 568, 588, 630, 632, 633, 635, 639, 640, 641, 645, 646, 647, 671, 672, 677, 689, 735, 822, 826, 833, 940, 962, 963, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057, 1059], "first_column": [31, 254], "last_column": [31, 254], "banded_column": [31, 254], "banded_row": [31, 254], "sheet": [31, 105, 254], "chart": [31, 254, 690, 735], "subsequ": [31, 57, 190, 218, 254, 431, 630, 662, 735], "colnam": [31, 112, 124, 143, 254, 661, 735], "given": [31, 52, 53, 67, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 121, 122, 124, 125, 126, 127, 133, 134, 144, 145, 147, 158, 159, 169, 185, 186, 195, 197, 203, 204, 207, 221, 226, 254, 268, 310, 316, 319, 348, 350, 351, 357, 383, 406, 425, 431, 433, 465, 467, 471, 474, 477, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 494, 496, 497, 517, 535, 537, 545, 568, 593, 605, 616, 617, 618, 639, 653, 663, 671, 672, 681, 689, 698, 699, 702, 707, 710, 719, 735, 738, 745, 786, 793, 796, 829, 831, 832, 839, 840, 857, 861, 880, 890, 909, 915, 917, 920, 932, 938, 940, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 960, 966, 967, 988, 1006, 1008, 1038, 1042, 1051, 1058, 1059], "dd": [31, 254], "mm": [31, 254], "yyyi": [31, 254], "00": [31, 124, 158, 173, 227, 254, 316, 317, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 535, 537, 540, 588, 591, 627, 628, 639, 671, 677, 735, 738, 793, 794, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 836, 837, 1006, 1008, 1011], "overridden": [31, 90, 92, 94, 96, 108, 109, 128, 254, 735], "basi": [31, 124, 254], "param": [31, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 123, 124, 126, 195, 254, 735], "It": [31, 180, 186, 236, 254, 268, 292, 448, 481, 588, 589, 639, 681, 686, 719, 735, 962, 963, 1051], "also": [31, 57, 93, 112, 122, 123, 124, 125, 126, 130, 143, 157, 158, 159, 180, 183, 196, 200, 207, 231, 254, 305, 308, 344, 383, 474, 483, 484, 485, 486, 487, 489, 490, 491, 506, 528, 534, 583, 584, 588, 595, 597, 615, 630, 638, 639, 644, 670, 671, 672, 686, 696, 702, 713, 714, 735, 738, 825, 861, 940, 1005, 1051], "group": [31, 35, 48, 52, 102, 157, 158, 159, 171, 183, 185, 187, 197, 225, 227, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 254, 262, 268, 365, 371, 409, 465, 471, 474, 481, 505, 506, 510, 516, 517, 525, 550, 568, 639, 670, 671, 672, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 735, 738, 893, 947, 981, 987, 988, 996, 1051], "float_dtyp": [31, 254], "simplifi": [31, 47, 48, 73, 254, 657, 662, 664, 690, 700, 735], "uniform": [31, 254], "condit": [31, 142, 149, 195, 254, 514, 533, 574, 595, 630, 660, 667, 735], "suppli": [31, 90, 92, 93, 94, 96, 108, 109, 195, 254, 467, 639, 735], "typenam": [31, 254], "3_color_scal": [31, 254], "data_bar": [31, 254], "make": [31, 59, 74, 90, 95, 101, 102, 106, 110, 114, 116, 180, 183, 190, 227, 254, 361, 362, 440, 465, 477, 493, 499, 531, 543, 639, 686, 735, 842, 843, 857, 921, 1002, 1014, 1051, 1059], "icon": [31, 254], "multipl": [31, 55, 73, 74, 101, 102, 112, 114, 115, 116, 122, 126, 134, 140, 144, 146, 149, 157, 158, 163, 171, 185, 187, 197, 200, 201, 207, 221, 231, 234, 254, 430, 445, 449, 456, 457, 465, 478, 481, 483, 484, 485, 486, 487, 489, 490, 491, 506, 564, 566, 570, 573, 577, 583, 584, 585, 595, 605, 606, 610, 615, 620, 623, 630, 639, 653, 659, 667, 670, 671, 696, 697, 702, 707, 713, 717, 719, 735, 744, 840, 846, 914, 926, 944, 1051], "singl": [31, 53, 67, 74, 101, 102, 103, 110, 112, 122, 133, 140, 141, 142, 146, 154, 169, 172, 185, 195, 197, 207, 254, 268, 341, 369, 407, 430, 438, 475, 478, 506, 509, 562, 564, 566, 568, 570, 573, 577, 578, 579, 585, 602, 605, 606, 610, 620, 623, 628, 639, 659, 660, 676, 702, 735, 744, 822, 846, 891, 914, 941, 944, 980, 1027, 1051], "across": [31, 67, 254, 564, 565, 566, 567, 585, 586, 606, 607, 610, 611, 623, 624], "effect": [31, 132, 152, 158, 217, 254, 268, 324, 588, 589, 639, 671, 715, 735, 793, 801], "heatmap": [31, 254], "min": [31, 35, 48, 139, 148, 158, 159, 187, 254, 298, 300, 306, 368, 465, 474, 486, 611, 619, 639, 666, 671, 672, 735, 775, 777, 782, 787, 849, 940, 952, 1051, 1059], "entir": [31, 254], "final": [31, 67, 116, 254, 360, 361, 362, 639, 664, 735, 841, 842, 843, 1051], "made": [31, 254, 1032, 1051], "up": [31, 59, 103, 170, 173, 196, 197, 214, 254, 268, 297, 543, 639, 676, 677, 681, 735, 738, 772, 1014, 1051], "abov": [31, 254, 630], "order": [31, 47, 48, 52, 54, 67, 73, 93, 119, 133, 134, 157, 158, 159, 185, 186, 187, 198, 201, 207, 217, 221, 223, 227, 254, 268, 278, 284, 295, 427, 432, 474, 493, 494, 495, 499, 505, 506, 545, 557, 558, 573, 639, 649, 653, 670, 671, 672, 685, 697, 702, 707, 708, 735, 745, 754, 760, 770, 786, 876, 911, 916, 938, 940, 945, 959, 960, 964, 976, 1017, 1036, 1037, 1051], "than": [31, 47, 48, 89, 94, 96, 101, 102, 112, 117, 124, 126, 133, 135, 156, 157, 170, 173, 185, 195, 196, 198, 225, 236, 254, 268, 376, 377, 398, 437, 451, 481, 493, 503, 521, 527, 532, 543, 568, 580, 588, 623, 639, 670, 677, 719, 735, 745, 947, 959, 974, 992, 998, 1003, 1014, 1051, 1059], "total": [31, 144, 254, 840, 1051], "export": [31, 170, 171, 196, 197, 214, 217, 254], "numer": [31, 173, 254, 261, 298, 299, 300, 373, 383, 435, 455, 457, 468, 477, 498, 546, 556, 639, 650, 677, 735, 738, 775, 776, 777, 787, 861, 875, 961, 968, 1032, 1051, 1059], "associ": [31, 53, 55, 67, 103, 123, 197, 254], "sum": [31, 52, 144, 152, 157, 158, 159, 169, 187, 234, 254, 268, 307, 308, 357, 369, 431, 483, 486, 490, 562, 564, 583, 585, 586, 595, 615, 624, 639, 657, 662, 664, 670, 671, 672, 688, 690, 700, 717, 735, 738, 783, 784, 839, 840, 949, 950, 952, 955, 956, 957, 1051], "must": [31, 91, 92, 104, 113, 139, 145, 158, 159, 173, 180, 195, 254, 310, 431, 438, 471, 483, 484, 485, 486, 487, 489, 490, 491, 639, 663, 671, 672, 677, 681, 686, 735, 786, 787, 938, 1051], "funcnam": [31, 254], "averag": [31, 254, 360, 361, 362, 474, 639, 841, 842, 843, 940, 1051], "count_num": [31, 254], "count": [31, 52, 101, 102, 106, 110, 112, 114, 115, 116, 139, 158, 159, 173, 183, 184, 187, 222, 226, 227, 232, 236, 245, 254, 269, 304, 341, 399, 407, 458, 463, 483, 484, 485, 486, 487, 489, 490, 491, 511, 558, 560, 569, 612, 639, 671, 672, 677, 688, 710, 715, 719, 728, 735, 787, 822, 857, 891, 928, 933, 982, 1037, 1039, 1051], "std_dev": [31, 254], "var": [31, 127, 254, 491, 639, 735, 1051], "pixel": [31, 254], "unit": [31, 38, 40, 124, 126, 144, 254, 317, 318, 325, 350, 355, 436, 449, 535, 537, 559, 588, 589, 597, 639, 690, 735, 738, 794, 795, 802, 831, 836, 840, 920, 1006, 1008, 1038, 1051, 1059], "hand": [31, 101, 102, 112, 254, 421, 422, 423, 424, 905, 906, 907, 908], "side": [31, 158, 159, 254, 383, 421, 422, 423, 424, 483, 484, 485, 486, 487, 489, 490, 491, 494, 588, 589, 627, 628, 639, 671, 672, 735, 861, 905, 906, 907, 908, 960, 1051], "call": [31, 56, 102, 124, 126, 130, 133, 157, 158, 159, 174, 253, 254, 268, 305, 308, 395, 469, 547, 606, 610, 639, 651, 670, 671, 672, 734, 735, 738, 745, 1051], "ad": [31, 93, 132, 158, 222, 231, 254, 267, 366, 583, 584, 639, 671, 713, 735, 847, 1051], "end": [31, 101, 102, 110, 112, 158, 254, 286, 289, 290, 316, 341, 345, 346, 363, 383, 426, 510, 514, 517, 529, 533, 570, 577, 588, 589, 593, 601, 602, 627, 628, 630, 639, 671, 673, 680, 690, 735, 738, 764, 793, 822, 826, 861, 910, 985, 988, 1000, 1004, 1051], "wise": [31, 67, 152, 254, 270, 271, 272, 273, 274, 275, 301, 302, 364, 434, 500, 501, 502, 552, 553, 583, 584, 595, 606, 610, 615, 639, 746, 747, 748, 749, 750, 751, 779, 780, 844, 918, 919, 971, 972, 973, 1024, 1025, 1051], "particip": [31, 254], "distinct": [31, 126, 185, 254, 284, 432, 474, 591, 639, 760, 916, 940, 1051, 1059], "referenc": [31, 254, 545], "differ": [31, 101, 117, 119, 146, 158, 159, 170, 196, 197, 214, 222, 254, 312, 322, 341, 342, 344, 353, 359, 408, 421, 423, 440, 459, 460, 462, 493, 499, 535, 558, 588, 594, 597, 603, 639, 662, 671, 672, 735, 738, 744, 745, 788, 799, 822, 823, 825, 834, 846, 892, 905, 907, 930, 931, 961, 1006, 1051], "row_index": [31, 254], "height": [31, 142, 254], "provid": [31, 47, 48, 55, 101, 102, 103, 104, 112, 113, 124, 126, 169, 254, 268, 287, 288, 431, 438, 512, 513, 516, 518, 519, 597, 622, 639, 650, 735, 745, 762, 763, 857, 880, 983, 984, 987, 989, 990, 1051, 1058, 1059], "intersect": [31, 254, 422, 738, 906], "bodi": [31, 254], "start": [31, 100, 101, 102, 103, 106, 110, 112, 114, 115, 116, 128, 157, 158, 174, 206, 227, 232, 254, 286, 289, 290, 322, 325, 326, 328, 329, 334, 336, 342, 343, 345, 346, 350, 352, 353, 354, 356, 363, 383, 426, 483, 484, 486, 489, 490, 491, 504, 510, 514, 517, 529, 533, 570, 577, 583, 588, 589, 593, 595, 601, 602, 627, 628, 630, 639, 670, 671, 690, 701, 715, 735, 738, 765, 794, 796, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 836, 837, 861, 910, 975, 985, 988, 1000, 1004, 1051, 1059], "zero": [31, 90, 91, 100, 101, 102, 106, 110, 123, 132, 148, 170, 195, 212, 217, 218, 254, 368, 431, 435, 494, 503, 543, 556, 639, 655, 666, 735, 774, 849, 915, 974, 1014, 1027, 1032, 1033, 1051], "unless": [31, 67, 92, 218, 254, 528, 534, 616, 735, 1005, 1033, 1041, 1051], "marker": [31, 254], "compliant": [31, 254], "case": [31, 67, 90, 92, 93, 94, 96, 108, 109, 116, 128, 133, 146, 158, 159, 170, 196, 197, 214, 254, 316, 438, 439, 448, 449, 510, 517, 525, 639, 671, 672, 735, 738, 744, 793, 846, 981, 988, 996, 1051], "three": [31, 220, 254, 431, 494, 639, 915], "avail": [31, 99, 103, 104, 113, 122, 130, 253, 254, 474, 570, 632, 633, 635, 638, 639, 640, 641, 644, 645, 646, 647, 650, 651, 664, 734, 735, 738, 940, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057], "insert_befor": [31, 254], "insert_aft": [31, 254], "respect": [31, 254], "direct": [31, 103, 110, 116, 225, 254], "far": [31, 254], "thei": [31, 93, 112, 119, 130, 174, 197, 254, 482, 639, 738, 948, 1051], "strongli": [31, 128, 133, 195, 236, 254, 268, 639, 745, 1051], "advis": [31, 174, 254], "structur": [31, 81, 85, 87, 146, 186, 197, 217, 254, 467, 639, 689, 735, 744, 846, 1051], "wherev": [31, 133, 236, 254, 268, 639, 745, 1051], "possibl": [31, 101, 133, 134, 157, 170, 196, 221, 223, 236, 254, 268, 436, 448, 532, 559, 639, 653, 670, 702, 707, 708, 735, 745, 1003, 1051], "simpl": [31, 126, 183, 254], "colx": [31, 57, 254, 738, 1059], "coli": [31, 254, 738, 1059], "after": [31, 57, 74, 93, 100, 101, 102, 106, 110, 112, 114, 115, 116, 146, 224, 253, 254, 363, 440, 465, 474, 543, 639, 709, 735, 744, 846, 921, 940, 1014, 1051], "befor": [31, 101, 112, 128, 130, 146, 158, 173, 224, 254, 307, 308, 309, 440, 465, 466, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 543, 548, 617, 618, 639, 671, 674, 677, 709, 735, 744, 783, 784, 785, 846, 921, 934, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1014, 1020, 1051], "most": [31, 54, 90, 101, 102, 103, 112, 254, 449, 456, 466, 532, 560, 639, 926, 934, 1003, 1039, 1051, 1058], "mandatori": [31, 254], "return_dtyp": [31, 133, 254, 268, 438, 440, 568, 605, 639, 745, 921, 1051], "latter": [31, 146, 254, 744, 846, 1051], "appropri": [31, 217, 254, 474, 639, 940, 1051], "pure": [31, 254, 1032, 1051], "actual": [31, 93, 105, 124, 126, 197, 254, 969, 1051], "indic": [31, 100, 101, 102, 106, 110, 112, 121, 122, 126, 158, 159, 173, 197, 201, 215, 227, 254, 279, 341, 345, 352, 385, 388, 389, 391, 392, 393, 430, 444, 447, 483, 484, 485, 486, 487, 489, 490, 491, 494, 500, 550, 573, 574, 639, 671, 672, 677, 697, 735, 738, 822, 826, 833, 865, 869, 871, 872, 873, 874, 914, 960, 971, 1022, 1028, 1051, 1059], "calcul": [31, 67, 158, 208, 228, 254, 312, 360, 361, 362, 396, 408, 421, 436, 488, 503, 508, 559, 561, 568, 580, 617, 618, 621, 629, 639, 671, 703, 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108, 109, 158, 169, 183, 254, 261, 265, 358, 359, 373, 376, 377, 398, 437, 455, 457, 461, 462, 464, 468, 523, 546, 556, 563, 639, 671, 735, 880, 994, 1010, 1012, 1013, 1051], "top_row": [31, 254], "top_col": [31, 254], "base": [31, 36, 142, 149, 158, 159, 254, 316, 324, 357, 360, 361, 362, 433, 434, 471, 524, 639, 660, 667, 671, 672, 735, 738, 793, 801, 839, 841, 842, 843, 857, 917, 918, 938, 995, 1042, 1051, 1059], "scroll": [31, 254], "region": [31, 254], "initit": [31, 254], "5th": [31, 254], "definit": [31, 122, 254, 396, 639, 881, 1051], "take": [31, 124, 130, 152, 158, 180, 186, 187, 211, 217, 254, 341, 506, 551, 588, 589, 592, 594, 603, 639, 671, 686, 706, 735, 822, 1023, 1042, 1051], "care": [31, 254, 268, 495, 639, 964, 1051], "rel": [31, 103, 104, 113, 119, 120, 254, 341, 360, 361, 362, 485, 487, 489, 491, 639, 822, 841, 842, 843, 1051], "readthedoc": [31, 254], "io": [31, 254], "working_with_conditional_format": [31, 254], "html": [31, 91, 132, 138, 254], "similarli": [31, 93, 158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "well": [31, 101, 102, 112, 145, 187, 254, 383, 588, 639, 663, 735, 861, 1051], "adjac": [31, 254], "two": [31, 57, 92, 94, 96, 103, 152, 179, 180, 187, 220, 235, 236, 254, 262, 313, 318, 431, 505, 550, 570, 571, 572, 580, 582, 617, 618, 639, 685, 686, 718, 719, 735, 789, 795, 915, 1051], "help": [31, 254, 664, 735], "appear": [31, 93, 119, 254, 558, 639, 1037, 1051], "working_with_sparklin": [31, 254], "inject": [31, 67, 254], "locat": [31, 146, 193, 219, 224, 254, 494, 639, 709, 735, 744, 846, 960, 963, 1022, 1051], "syntax": [31, 133, 183, 254, 510, 516, 517, 525, 700, 735, 981, 987, 988, 996, 1051], "ensur": [31, 75, 103, 123, 124, 126, 157, 185, 195, 254, 383, 560, 639, 670, 681, 735, 738, 1032, 1039, 1051], "correctli": [31, 254], "microsoft": [31, 118, 254], "com": [31, 103, 254, 360, 361, 362, 516, 517, 639, 841, 842, 843, 987, 988, 1051], "u": [31, 38, 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318, 795], "2023": [31, 97, 118, 171, 254, 318, 588, 604, 738, 795], "num": [31, 220, 254, 383, 386, 543, 639, 861, 1051], "500": [31, 170, 226, 254, 664, 710, 735, 810, 812, 820, 827, 949, 950, 952, 1051], "val": [31, 194, 254, 295, 467, 554, 630, 639, 694, 735, 770], "10_000": [31, 254], "20_000": [31, 254], "30_000": [31, 254], "increas": [31, 67, 101, 110, 254, 309, 639, 785, 1051], "b4": [31, 254], "light": [31, 254], "twice": [31, 105, 254], "each": [31, 67, 102, 115, 119, 120, 122, 133, 156, 157, 158, 159, 171, 173, 185, 218, 224, 227, 233, 234, 236, 237, 239, 248, 254, 268, 310, 341, 345, 352, 413, 429, 435, 471, 474, 481, 485, 487, 489, 491, 506, 517, 531, 532, 568, 570, 573, 579, 588, 601, 602, 622, 639, 671, 672, 677, 690, 709, 717, 719, 720, 722, 731, 735, 773, 786, 822, 826, 833, 897, 913, 938, 940, 947, 988, 1002, 1003, 1019, 1051, 1059], "titl": [31, 52, 254], "explicit": [31, 112, 122, 254, 615], "integr": [31, 254, 1059], "multi_fram": [31, 254], "wb": [31, 254], "coordin": [31, 254], "advanc": [31, 254, 431, 915, 1059], "min_color": [31, 254], "76933c": [31, 254], "mid_color": [31, 254], "c4d79b": [31, 254], "max_color": [31, 254], "ebf1d": [31, 254], "data_bar_2010": [31, 254], "bar_color": [31, 254], "9bbb59": [31, 254], "bar_negative_color_sam": [31, 254], "bar_negative_border_color_sam": [31, 254], "000": [31, 254, 1059], "white": [31, 254], "w": [31, 54, 55, 158, 197, 254, 516, 565, 567, 671, 735, 738, 987, 999], "get_worksheet_by_nam": [31, 254], "fmt_titl": [31, 254], "add_format": [31, 254], "font_color": [31, 254], "4f6228": [31, 254], "font_siz": [31, 254], "12": [31, 67, 97, 123, 124, 158, 159, 173, 227, 254, 263, 307, 316, 318, 322, 326, 329, 336, 337, 338, 342, 343, 345, 352, 354, 356, 466, 473, 490, 498, 538, 584, 587, 588, 590, 605, 639, 671, 672, 673, 677, 680, 700, 705, 735, 738, 745, 786, 793, 795, 817, 826, 833, 958, 1009, 1051, 1059], "ital": [31, 254], "bold": [31, 254], "customis": [31, 254], "trend": [31, 254], "win_loss": [31, 254], "subtl": [31, 254], "tone": [31, 254], "hidden": [31, 254], "id": [31, 74, 222, 236, 254, 481, 525, 526, 558, 560, 639, 719, 947, 1037, 1051, 1059], "q1": [31, 254], "55": [31, 69, 254], "20": [31, 124, 146, 163, 164, 180, 186, 188, 192, 193, 254, 276, 277, 278, 312, 345, 352, 378, 382, 467, 483, 484, 486, 597, 604, 627, 639, 686, 689, 735, 738, 788, 833, 1051], "35": [31, 118, 254, 312, 639, 788, 1051], "q2": [31, 254], "30": [31, 146, 158, 163, 186, 192, 193, 213, 231, 254, 276, 277, 278, 312, 318, 323, 329, 337, 343, 344, 345, 352, 356, 378, 498, 588, 604, 627, 639, 671, 689, 713, 735, 738, 786, 788, 795, 800, 818, 825, 826, 833, 1051, 1059], "15": [31, 118, 123, 133, 158, 159, 164, 254, 309, 312, 338, 345, 352, 467, 490, 584, 616, 627, 639, 671, 672, 735, 738, 785, 788, 826, 833, 1051], "60": [31, 146, 254, 346, 347, 490, 535, 639, 823, 827, 828, 1006], "q3": [31, 254], "40": [31, 146, 186, 254, 345, 352, 378, 538, 639, 689, 735, 806, 833, 1009], "80": [31, 254], "q4": [31, 254], "75": [31, 139, 254, 265, 464, 483, 484, 485, 486, 489, 490, 491, 639, 787, 857, 938, 1051, 1059], "account": [31, 97, 103, 254, 341, 360, 361, 362, 639, 822, 841, 842, 843, 1051], "flavour": [31, 254], "integer_dtyp": [31, 200, 254, 696, 735, 738], "0_": [31, 254], "just": [31, 112, 179, 254, 685, 735], "unifi": [31, 254, 738], "multi": [31, 101, 102, 254, 363, 516, 606, 610, 639, 987], "2_color_scal": [31, 254], "95b3d7": [31, 254], "ffffff": [31, 254], "standardis": [31, 254], "z": [31, 54, 74, 97, 122, 124, 144, 166, 168, 172, 179, 196, 197, 254, 261, 263, 265, 295, 378, 405, 439, 464, 469, 477, 517, 535, 537, 547, 565, 567, 586, 607, 611, 616, 624, 639, 676, 685, 701, 735, 738, 770, 849, 988, 1006, 1008, 1051, 1058], "score": [31, 254], "conjunct": [31, 105, 254], "a123": [31, 254], "b345": [31, 254], "c567": [31, 254], "d789": [31, 254], "e101": [31, 254], "45": [31, 159, 254, 318, 345, 352, 490, 511, 517, 571, 572, 604, 627, 639, 672, 735, 738, 795, 826, 833, 982, 988, 1059], "85": [31, 254, 1059], "font": [31, 254], "consola": [31, 254], "standard": [31, 118, 208, 217, 254, 329, 361, 489, 503, 508, 519, 621, 639, 703, 735, 806, 842, 974, 978, 990, 1051, 1059], "stdev": [31, 254], "ipccompress": [32, 254], "arrow": [32, 47, 76, 90, 103, 106, 114, 170, 196, 197, 212, 214, 254, 735, 1027, 1032, 1051], "ipc": [32, 106, 107, 114, 117, 254, 650], "binari": [32, 254, 286, 288, 289, 290, 761, 764, 765], "feather": [32, 106, 114, 254, 650], "lz4": [32, 35, 47, 48, 254, 735], "zstd": [32, 35, 47, 48, 254, 735], "pretti": [33, 254], "row_ori": [33, 254], "iobas": [33, 34, 108, 109, 254, 453, 692, 716, 735], "serial": [33, 34, 254], "represent": [33, 34, 216, 254, 295, 322, 326, 329, 330, 332, 334, 336, 339, 342, 343, 346, 353, 354, 356, 554, 639, 662, 679, 735, 770, 799, 803, 805, 806, 810, 812, 815, 817, 820, 823, 824, 827, 834, 835, 837, 1030, 1034, 1051], "orient": [33, 68, 70, 94, 96, 254, 735], "slower": [33, 94, 96, 133, 157, 185, 227, 236, 254, 268, 568, 639, 670, 719, 735, 745, 1051], "common": [33, 67, 73, 74, 254, 439, 588, 589, 639, 644, 657, 662, 664, 690, 700, 735], "write_ndjson": [33, 254], "newlin": [34, 109, 115, 254], "delimit": [34, 101, 102, 109, 112, 115, 187, 215, 254, 509, 980, 1028, 1051], "parquetcompress": [35, 254], "compression_level": [35, 48, 254, 735], "statist": [35, 48, 101, 102, 110, 116, 139, 254, 361, 362, 396, 483, 484, 485, 486, 487, 488, 489, 490, 491, 503, 639, 735, 787, 842, 843, 881, 954, 974, 1051], "row_group_s": [35, 48, 254, 735], "use_pyarrow": [35, 48, 101, 106, 110, 254, 735, 1031, 1032, 1051], "pyarrow_opt": [35, 104, 110, 113, 254], "parquet": [35, 48, 110, 111, 116, 254, 650, 735], "gzip": [35, 48, 254, 735], "lzo": [35, 48, 254, 735], "brotli": [35, 48, 254, 735], "choos": [35, 47, 48, 187, 254, 735], "good": [35, 47, 48, 170, 254, 735], "perform": [35, 47, 48, 67, 81, 91, 101, 102, 106, 110, 112, 114, 115, 116, 132, 133, 134, 157, 158, 159, 173, 190, 197, 221, 236, 254, 268, 465, 535, 536, 537, 639, 653, 671, 672, 677, 702, 707, 715, 735, 745, 1006, 1007, 1008, 1051], "fast": [35, 47, 48, 125, 127, 254, 366, 495, 639, 735, 847, 964, 1051, 1059], "decompress": [35, 47, 48, 254, 735], "backward": [35, 48, 148, 173, 254, 285, 338, 368, 639, 666, 677, 735, 819, 849, 1051], "guarante": [35, 48, 91, 101, 102, 223, 254, 664, 708, 735], "deal": [35, 48, 170, 254, 344, 352, 474, 535, 639, 735, 825, 833, 940, 1006, 1051], "older": [35, 48, 254, 735], "reader": [35, 48, 99, 101, 102, 106, 110, 254, 651, 735], "higher": [35, 48, 189, 246, 254, 472, 487, 614, 639, 691, 729, 735, 939, 953, 1051], "mean": [35, 48, 101, 102, 106, 110, 112, 139, 148, 157, 158, 159, 173, 187, 227, 234, 254, 341, 345, 352, 365, 368, 483, 484, 485, 486, 487, 489, 490, 491, 503, 516, 570, 575, 588, 639, 666, 670, 671, 672, 677, 681, 717, 735, 787, 822, 826, 833, 845, 849, 854, 950, 974, 987, 1051], "smaller": [35, 48, 144, 254, 664, 735, 840, 1051], "disk": [35, 47, 48, 106, 254, 700, 735], "11": [35, 48, 118, 124, 159, 254, 263, 314, 315, 329, 337, 338, 341, 345, 352, 382, 466, 474, 490, 504, 543, 563, 577, 623, 628, 639, 657, 672, 673, 680, 690, 705, 735, 738, 745, 822, 826, 833, 948, 1051], "22": [35, 48, 123, 254, 322, 342, 345, 352, 354, 355, 483, 484, 486, 489, 490, 491, 535, 577, 639, 735, 738, 826, 833, 836, 1006, 1059], "comput": [35, 48, 73, 74, 78, 144, 157, 173, 218, 223, 234, 246, 254, 260, 270, 271, 272, 273, 274, 275, 281, 282, 283, 296, 301, 302, 304, 305, 306, 307, 308, 313, 357, 364, 396, 417, 418, 419, 421, 422, 423, 424, 433, 434, 435, 456, 465, 466, 470, 471, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 500, 501, 502, 503, 507, 552, 553, 564, 565, 566, 567, 571, 572, 573, 580, 582, 585, 592, 606, 610, 617, 618, 623, 639, 670, 674, 676, 677, 708, 714, 717, 729, 735, 740, 746, 747, 748, 749, 750, 751, 757, 758, 759, 771, 779, 780, 781, 782, 783, 784, 789, 839, 840, 844, 881, 901, 902, 903, 905, 906, 907, 908, 917, 918, 919, 926, 934, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 971, 972, 973, 974, 977, 1024, 1025, 1033, 1051], "512": [35, 254, 468, 639, 934, 1051], "implement": [35, 91, 132, 133, 236, 254, 268, 395, 469, 547, 568, 639, 719, 745, 962, 963, 1051], "v": [35, 54, 55, 144, 254, 494, 639, 786, 960, 1051], "At": [35, 254], "moment": [35, 138, 254, 396, 503, 639, 881, 974, 1051], "pyarrow": [35, 90, 95, 101, 103, 104, 106, 110, 113, 117, 118, 171, 212, 217, 218, 254, 651, 1027, 1031, 1032, 1033, 1051], "write_t": [35, 254], "partition_col": [35, 103, 254], "write_to_dataset": [35, 254], "similar": [35, 128, 152, 173, 254, 348, 351, 465, 481, 630, 639, 677, 735, 829, 832, 947, 1051], "spark": [35, 254], "partit": [35, 103, 104, 110, 113, 117, 171, 185, 254], "we": [35, 101, 102, 105, 112, 158, 159, 173, 227, 254, 268, 341, 345, 352, 409, 438, 483, 484, 485, 486, 487, 489, 490, 491, 524, 604, 630, 639, 671, 672, 677, 681, 735, 745, 822, 826, 833, 857, 893, 995, 1051], "use_pyarrow_write_to_dataset": [35, 254], "first": [35, 67, 101, 102, 105, 112, 115, 123, 133, 156, 158, 161, 173, 175, 182, 186, 187, 210, 215, 216, 222, 223, 227, 239, 254, 278, 280, 309, 338, 341, 345, 379, 386, 400, 409, 412, 413, 431, 440, 467, 494, 506, 516, 518, 519, 525, 526, 576, 583, 588, 595, 599, 630, 639, 657, 671, 673, 677, 680, 689, 708, 714, 722, 735, 738, 754, 785, 787, 819, 822, 826, 856, 866, 883, 893, 896, 897, 915, 921, 960, 987, 989, 990, 996, 997, 1021, 1030, 1051, 1059], "watermark": [35, 254], "partitioned_object": [35, 254], "calendar": [37, 38, 158, 159, 173, 227, 254, 329, 341, 345, 352, 356, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 806, 822, 826, 833, 837], "time_unit": [38, 40, 97, 317, 318, 325, 350, 355, 535, 537, 588, 589, 597, 738, 794, 795, 802, 831, 836, 1006, 1008], "time_zon": [38, 97, 319, 344, 352, 537, 588, 589, 738, 793, 796, 801, 825, 833, 1008], "timezon": [38, 738], "m": [38, 40, 55, 172, 254, 316, 317, 318, 324, 325, 348, 350, 351, 355, 516, 535, 536, 537, 540, 588, 589, 597, 676, 735, 738, 793, 794, 795, 801, 802, 829, 831, 832, 836, 987, 1006, 1007, 1008, 1011], "zone": [38, 316, 319, 344, 535, 537, 588, 589, 738, 793, 796, 825, 1006, 1008], "zoneinfo": [38, 738], "run": [38, 47, 48, 73, 125, 127, 133, 157, 174, 187, 223, 236, 254, 268, 309, 409, 480, 481, 619, 639, 657, 662, 664, 670, 681, 685, 690, 700, 708, 735, 738, 745, 785, 893, 946, 947, 1051, 1058, 1059], "available_timezon": [38, 738], "check": [38, 101, 102, 112, 119, 120, 153, 158, 159, 167, 169, 172, 254, 264, 266, 286, 289, 290, 383, 387, 406, 510, 514, 533, 639, 671, 672, 676, 681, 735, 742, 743, 761, 764, 765, 862, 864, 867, 868, 870, 875, 876, 877, 879, 880, 890, 961, 981, 985, 1004, 1051], "128": [39, 69, 934, 1051], "bit": [39, 41, 42, 43, 44, 45, 46, 61, 62, 63, 64, 476, 510, 639, 942, 981, 1051], "neg": [39, 158, 159, 161, 175, 203, 204, 206, 210, 254, 425, 426, 467, 496, 497, 504, 529, 639, 671, 672, 698, 699, 701, 715, 735, 856, 883, 909, 910, 966, 967, 975, 1000, 1021, 1051], "scale": [39, 144, 254, 467, 538, 639, 840, 1009, 1051], "experiment": [39, 117, 200, 225, 226, 231, 254, 309, 345, 483, 484, 485, 486, 487, 489, 490, 491, 639, 696, 710, 713, 735, 785, 826, 857, 938, 1051], "progress": 39, "expect": [39, 82, 84, 89, 268, 568, 604, 639, 679, 681, 735], "32": [41, 44, 62, 69, 159, 169, 254, 457, 498, 639, 672, 735, 789, 823, 934, 953, 1051], "sign": [43, 44, 45, 46, 341, 476, 543, 639, 822, 870, 942, 1014, 1051], "maintain_ord": [47, 48, 134, 157, 185, 187, 221, 223, 227, 235, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 262, 268, 284, 432, 550, 557, 581, 639, 653, 657, 662, 664, 670, 690, 700, 702, 707, 708, 718, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 760, 916, 1036, 1051], "type_coercion": [47, 48, 73, 657, 662, 664, 690, 700, 735], "predicate_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "projection_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "simplify_express": [47, 48, 73, 657, 662, 664, 690, 700, 735], "no_optim": [47, 48, 73, 657, 664, 681, 690, 735], "slice_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "persist": [47, 48, 57, 735], "larger": [47, 48, 580, 735], "ram": [47, 48, 735], "maintain": [47, 48, 134, 221, 254, 284, 432, 494, 557, 639, 653, 702, 707, 735, 760, 916, 960, 1036, 1051], "slightli": [47, 48, 735], "faster": [47, 48, 146, 217, 225, 254, 268, 482, 523, 558, 639, 735, 744, 745, 846, 948, 994, 1051], "coercion": [47, 48, 73, 477, 639, 657, 662, 664, 690, 700, 735], "optim": [47, 48, 73, 110, 112, 114, 115, 116, 170, 174, 186, 190, 196, 223, 254, 657, 662, 664, 681, 690, 700, 708, 715, 719, 735, 771, 1051], "predic": [47, 48, 73, 112, 114, 115, 116, 117, 149, 169, 195, 254, 369, 562, 595, 639, 657, 662, 664, 667, 681, 690, 693, 700, 715, 735, 850, 962, 963, 1051], "pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 693, 700, 715, 735, 962, 963, 1051], "project": [47, 48, 73, 112, 114, 115, 116, 268, 505, 506, 639, 657, 662, 664, 681, 690, 693, 700, 716, 735], "turn": [47, 48, 73, 101, 102, 112, 541, 560, 639, 657, 662, 664, 681, 690, 735, 1012], "off": [47, 48, 73, 101, 102, 112, 560, 639, 657, 662, 664, 681, 690, 735], "certain": [47, 48, 80, 104, 113, 164, 227, 254, 577, 657, 690, 735, 1051], "slice": [47, 48, 68, 73, 144, 161, 171, 210, 254, 413, 429, 482, 483, 484, 485, 486, 487, 489, 490, 491, 639, 657, 662, 664, 681, 690, 700, 735, 840, 856, 897, 913, 948, 949, 950, 951, 952, 953, 955, 956, 957, 1021, 1051], "lf": [47, 48, 653, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 685, 687, 688, 689, 690, 691, 693, 694, 695, 696, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 711, 712, 713, 714, 715, 716, 735, 1058, 1059], "scan_csv": [47, 48, 101, 102, 735], "my_larger_than_ram_fil": [47, 48, 735], "data_pagesize_limit": [48, 735], "reduc": [48, 101, 102, 110, 112, 114, 115, 116, 241, 242, 244, 247, 498, 595, 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721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 738, 868, 1051, 1058, 1059], "global": [54, 58, 75, 124, 129, 217, 254, 440, 639, 1058], "scope": [54, 57, 130, 649, 738, 1058], "automat": [54, 57, 90, 92, 93, 94, 96, 97, 101, 102, 103, 108, 109, 112, 124, 126, 128, 200, 231, 254, 292, 440, 522, 528, 534, 639, 696, 713, 735, 921, 993, 999, 1005, 1051, 1058], "map": [54, 55, 92, 93, 101, 102, 106, 107, 110, 111, 112, 114, 133, 143, 151, 170, 191, 195, 196, 197, 213, 254, 345, 352, 439, 440, 465, 481, 560, 568, 639, 661, 693, 735, 787, 826, 833, 921, 947, 1051, 1058], "recent": [54, 466, 639, 934, 1051, 1058], "df1": [54, 56, 57, 58, 67, 74, 75, 119, 146, 153, 180, 218, 229, 254, 686, 735], "x": [54, 55, 67, 74, 97, 101, 102, 103, 112, 122, 124, 144, 163, 166, 168, 172, 179, 187, 196, 197, 254, 261, 263, 264, 265, 266, 268, 310, 311, 358, 359, 373, 376, 377, 378, 398, 405, 414, 435, 437, 438, 439, 455, 457, 461, 462, 464, 468, 469, 471, 477, 481, 500, 503, 531, 546, 547, 554, 556, 563, 565, 567, 568, 571, 572, 583, 584, 586, 595, 604, 605, 607, 611, 615, 624, 639, 676, 681, 685, 701, 735, 738, 745, 849, 879, 971, 974, 1002, 1034, 1051, 1058], "df2": [54, 56, 57, 58, 67, 74, 75, 119, 146, 153, 218, 229, 254], "t": [54, 55, 74, 105, 112, 133, 158, 186, 223, 225, 254, 268, 309, 316, 324, 344, 357, 360, 361, 362, 409, 465, 467, 471, 516, 522, 535, 537, 568, 630, 639, 671, 689, 708, 735, 738, 745, 785, 825, 839, 841, 842, 843, 893, 938, 961, 987, 993, 999, 1006, 1008, 1032, 1041, 1051], "join": [54, 58, 67, 73, 74, 75, 173, 226, 254, 382, 465, 639, 657, 662, 664, 677, 690, 700, 710, 714, 735], "named_fram": [55, 1058], "lf1": [55, 57], "o": [55, 106, 114, 291, 309, 515, 555, 639, 766, 785, 986, 1005, 1035, 1051], "lf2": [55, 57, 735], "p": [55, 69, 186, 254, 467, 622, 639, 689, 735], "q": [55, 197, 254, 310, 471, 622, 639, 938, 1051], "r": [55, 467, 511, 515, 516, 517, 525, 639, 738, 982, 986, 987, 988, 996], "lf3": [55, 735], "lf4": [55, 735], "either": [55, 124, 159, 169, 174, 177, 185, 195, 209, 217, 254, 471, 535, 564, 573, 622, 639, 672, 679, 735, 738, 938, 1006, 1051], "tbl1": [55, 57], "tbl2": [55, 57], "tbl3": 55, "tbl4": 55, "statement": [56, 630], "hello_world": 56, "baz": [56, 164, 165, 187, 224, 254, 530, 532, 675, 709, 735, 738, 1003], "hello_data": 56, "foo_bar": [56, 604], "registr": [57, 650], "lifetim": [57, 130, 649], "context": [57, 58, 128, 183, 237, 254, 262, 268, 292, 303, 369, 409, 448, 505, 506, 560, 568, 581, 594, 597, 603, 616, 619, 638, 639, 649, 650, 714, 720, 735, 893, 1058], "manag": [57, 58, 649, 650, 1058], "often": [57, 130, 158, 159, 254, 407, 477, 639, 671, 672, 735, 891], "want": [57, 93, 133, 146, 183, 254, 268, 298, 299, 300, 352, 369, 438, 440, 481, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 613, 616, 631, 639, 657, 673, 680, 735, 738, 744, 745, 775, 776, 777, 833, 846, 947, 1032, 1051], "df0": [57, 180, 254, 686, 735], "exit": [57, 58, 130, 1058], "construct": [57, 90, 92, 93, 94, 95, 96, 254, 375, 440, 613, 616, 631, 639, 669, 692, 735, 1051], "through": [57, 738, 1051], "tbl0": 57, "remain": [57, 101, 102, 112, 144, 254, 531, 532, 570, 681, 735, 840, 1002, 1003, 1051], "text": [57, 523, 525, 526, 620, 994, 1059], "misc": 57, "testing1234": 57, "test1": 57, "test2": 57, "test3": 57, "temporarili": [58, 128, 130, 158, 159, 254, 671, 672, 735], "cach": [58, 73, 75, 106, 112, 114, 116, 129, 440, 483, 484, 485, 486, 487, 489, 490, 491, 535, 536, 537, 540, 639, 649, 657, 662, 664, 690, 700, 735, 1006, 1007, 1008, 1011], "categori": [58, 75, 215, 254, 294, 295, 310, 471, 639, 769, 770, 786, 857, 938, 1051], "until": [58, 174, 254, 588], "finish": [58, 78, 146, 254, 744, 846, 1051], "invalid": [58, 101, 102, 112, 518, 519, 524, 556, 588, 589, 639, 989, 990, 995], "outermost": 58, "color": [58, 75, 236, 286, 288, 289, 290, 719], "red": [58, 75, 236, 719], "green": [58, 75, 236, 719], "blue": [58, 75, 286, 288, 289, 290], "orang": [58, 75, 137, 237, 238, 240, 241, 242, 244, 246, 247, 254, 720, 721, 723, 724, 725, 727, 729, 730], "uint8": [58, 75, 121, 123, 216, 217, 254, 307, 308, 440, 548, 563, 639, 738, 783, 784, 1020, 1051, 1059], "yellow": [58, 75, 286, 288, 289, 290], "black": [58, 75, 133, 254, 286, 288, 289, 290], "succe": [58, 101, 102, 112], "df_join": [58, 75], "cat": [58, 75, 216, 254, 310, 471, 510, 539, 542, 554, 579, 635, 639, 738, 786, 857, 938, 981, 1010, 1013, 1047, 1051], "u8": [58, 75, 215, 216, 254, 440, 563, 639, 738, 1028, 1051, 1059], "composit": [59, 123, 1059], "schemadict": [59, 90, 92, 93, 94, 95, 96, 112, 199, 254, 622, 681, 695, 719, 735], "struct_seri": [59, 719], "dai": [60, 158, 159, 171, 173, 227, 254, 325, 329, 336, 337, 338, 341, 342, 343, 345, 350, 352, 353, 354, 356, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 590, 591, 639, 671, 672, 677, 735, 738, 818, 819, 822, 823, 826, 833, 835], "unsign": [61, 62, 63, 64, 476, 639, 870, 942, 1051], "could": [65, 78, 142, 158, 254, 293, 583, 595, 639, 660, 671, 735, 767, 1051], "static": [65, 719], "utf": 66, "frametyp": [67, 1058], "joinstrategi": [67, 172, 254, 676, 735], "outer": [67, 74, 172, 254, 676, 735], "descend": [67, 134, 201, 207, 221, 254, 278, 427, 474, 495, 505, 506, 573, 639, 653, 697, 702, 707, 735, 754, 876, 911, 940, 964, 976, 1051], "fill": [67, 74, 135, 147, 148, 204, 225, 254, 285, 305, 308, 367, 368, 374, 382, 483, 484, 486, 490, 497, 521, 527, 543, 596, 613, 616, 631, 639, 665, 666, 699, 714, 735, 848, 849, 860, 932, 949, 950, 952, 955, 956, 957, 967, 992, 998, 1014, 1051], "sort": [67, 68, 119, 123, 134, 158, 159, 173, 180, 186, 187, 201, 221, 227, 239, 248, 254, 278, 295, 369, 465, 495, 506, 560, 562, 573, 639, 653, 662, 671, 672, 677, 686, 689, 690, 697, 700, 707, 722, 731, 735, 738, 754, 770, 876, 964, 1036, 1039, 1051, 1059], "origin": [67, 101, 102, 223, 254, 344, 395, 440, 465, 476, 477, 478, 511, 516, 517, 519, 521, 527, 543, 571, 572, 639, 708, 735, 786, 825, 921, 938, 944, 982, 987, 988, 990, 992, 998, 1014, 1051], "In": [67, 104, 113, 116, 124, 126, 130, 133, 144, 146, 158, 159, 183, 217, 254, 268, 588, 639, 671, 672, 735, 744, 840, 846, 941, 1051], "duplic": [67, 79, 166, 172, 173, 223, 254, 263, 384, 395, 471, 639, 676, 677, 708, 735, 863, 938, 1051], "behaviour": [67, 74, 510, 516, 517, 525, 556, 639, 981, 987, 988, 996], "strategi": [67, 74, 101, 121, 122, 123, 124, 126, 148, 158, 172, 173, 182, 254, 268, 368, 431, 639, 666, 671, 676, 677, 735, 849, 915, 1051], "suitabl": [67, 74, 122, 133, 254, 268, 494, 639, 745, 960, 1051, 1059], "get": [67, 98, 107, 111, 128, 134, 137, 143, 151, 154, 155, 158, 159, 161, 162, 166, 168, 169, 175, 182, 195, 199, 202, 206, 210, 221, 230, 239, 248, 254, 262, 276, 277, 278, 280, 284, 294, 304, 305, 306, 307, 308, 325, 341, 370, 379, 384, 386, 394, 397, 400, 411, 415, 416, 432, 441, 442, 443, 448, 450, 454, 459, 460, 472, 480, 504, 508, 520, 523, 548, 549, 557, 561, 588, 594, 599, 603, 606, 607, 608, 609, 610, 611, 621, 625, 629, 639, 653, 658, 661, 668, 671, 672, 673, 678, 680, 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1047, 1052, 1055, 1056, 1057], "access": [68, 69, 70, 71, 170, 195, 196, 254, 440, 639, 714, 735, 1059], "by_first_letter_of_column_nam": 68, "f": [68, 102, 139, 156, 171, 212, 222, 225, 254, 261, 268, 431, 498, 515, 535, 537, 568, 578, 605, 639, 915, 986, 1006, 1008], "fromkei": [68, 70], "by_first_letter_of_column_valu": 68, "starts_with": [68, 286, 289, 510, 514, 738, 985], "to_seri": [68, 154, 254, 535, 574, 616, 1006], "xx": [68, 70, 123, 126, 738], "xy": [68, 70], "yy": [68, 70, 123, 126, 738], "yz": [68, 70], "b1": [68, 70], "b2": [68, 70], "pow_n": 69, "powersofn": 69, "next": [69, 158, 159, 173, 227, 254, 285, 341, 345, 352, 474, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 822, 826, 833, 940, 1051], "ceil": [69, 639, 1051], "previou": [69, 130, 466, 469, 545, 547, 639, 744, 934, 1051], "floor": [69, 639, 1051], "nearest": [69, 173, 189, 246, 254, 297, 372, 382, 472, 487, 614, 639, 677, 691, 729, 735, 772, 852, 860, 939, 953, 1051], "24": [69, 118, 133, 144, 158, 159, 173, 227, 254, 307, 309, 322, 327, 341, 342, 345, 352, 354, 457, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 785, 804, 822, 826, 833, 880, 1051], "001": [69, 330, 331, 333, 340, 591, 811, 813, 821], "next_pow2": 69, "prev_pow2": 69, "nearest_pow2": 69, "split_by_column_dtyp": 70, "collect_al": 70, "31": [71, 124, 318, 322, 323, 336, 337, 341, 353, 535, 587, 588, 590, 597, 604, 738, 793, 795, 799, 800, 818, 822, 1006, 1059], "42": [71, 159, 160, 180, 254, 672, 686, 735, 855, 1051], "961": 71, "1764": 71, "4160": 71, "build": [72, 91, 738, 1059], "wa": 72, "compil": [72, 403, 404, 887, 888], "gate": 72, "info": [72, 104, 113, 118], "otherwis": [72, 123, 128, 133, 180, 197, 200, 236, 254, 268, 287, 298, 299, 300, 344, 352, 360, 361, 362, 431, 467, 476, 510, 512, 564, 566, 568, 585, 606, 610, 623, 630, 639, 686, 696, 719, 735, 745, 762, 775, 776, 777, 825, 833, 841, 842, 843, 915, 942, 962, 963, 981, 983, 1051], "depend": [72, 118, 268, 403, 404, 448, 471, 474, 568, 594, 597, 603, 639, 887, 888, 938, 940, 1051, 1059], "host": [72, 101, 106, 110, 114, 116], "git": 72, "lazy_fram": 73, "comm_subplan_elim": [73, 657, 662, 664, 690, 700, 735], "comm_subexpr_elim": [73, 292, 639, 657, 662, 664, 690, 700, 735], "graph": [73, 174, 254, 674, 714, 735], "parallel": [73, 74, 99, 103, 110, 116, 158, 173, 174, 186, 254, 309, 409, 639, 650, 671, 676, 677, 719, 735, 785, 893, 1051], "threadpool": [73, 128], "Will": [73, 657, 662, 664, 690, 700, 735, 1051], "try": [73, 85, 87, 101, 102, 105, 106, 110, 112, 114, 116, 657, 662, 664, 690, 700, 735], "branch": [73, 657, 662, 664, 690, 700, 735], "subplan": [73, 657, 662, 664, 690, 700, 735], "union": [73, 74, 424, 657, 662, 664, 690, 700, 735, 738, 908], "subexpress": [73, 657, 662, 664, 690, 700, 735], "reus": [73, 657, 662, 664, 690, 700, 735], "part": [73, 90, 124, 517, 531, 532, 657, 662, 664, 690, 700, 714, 735, 988, 1002, 1003], "fashion": [73, 172, 254, 657, 662, 664, 690, 700, 735], "item": [74, 102, 195, 198, 254, 365, 406, 412, 414, 493, 532, 639, 845, 890, 896, 898, 959, 1003, 1051], "iter": [74, 125, 127, 134, 157, 158, 159, 170, 171, 185, 195, 196, 197, 200, 201, 207, 221, 222, 231, 233, 234, 254, 309, 363, 465, 506, 564, 565, 566, 567, 573, 576, 577, 578, 579, 585, 586, 593, 606, 607, 610, 611, 619, 622, 623, 624, 639, 653, 670, 671, 672, 696, 697, 702, 707, 713, 717, 735, 785, 1051, 1059], "polarstyp": 74, "concatmethod": 74, "vertic": [74, 146, 225, 229, 254, 509, 980], "rechunk": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 146, 254, 639, 744, 773, 846, 927, 1051], "combin": [74, 85, 87, 158, 159, 160, 173, 227, 254, 265, 279, 341, 352, 464, 522, 528, 534, 588, 639, 671, 672, 677, 700, 735, 822, 826, 833, 993, 999, 1005], "concaten": [74, 152, 186, 254, 467, 578, 579, 639, 689, 735, 773, 927, 1051], "diagon": [74, 222, 254], "vstack": [74, 146, 254], "vertical_relax": 74, "coerc": [74, 477, 639], "equal": [74, 75, 101, 102, 112, 119, 120, 134, 153, 158, 173, 180, 221, 254, 292, 358, 359, 376, 398, 431, 462, 477, 482, 483, 484, 485, 486, 487, 489, 490, 491, 520, 521, 527, 543, 617, 618, 639, 653, 671, 677, 686, 702, 707, 735, 915, 948, 949, 950, 951, 952, 953, 955, 956, 957, 961, 991, 992, 998, 1014, 1051, 1059], "supertyp": [74, 148, 254, 267, 639, 666, 735], "find": [74, 150, 254, 494, 639, 960, 1051], "miss": [74, 101, 102, 112, 147, 254, 285, 360, 361, 362, 374, 389, 391, 639, 665, 735, 841, 842, 843, 1041, 1051], "stack": [74, 163, 229, 254], "don": [74, 133, 223, 225, 254, 268, 309, 409, 465, 471, 568, 639, 708, 735, 738, 745, 785, 893, 938, 961, 1041, 1051], "auto": [74, 90, 92, 93, 94, 96, 108, 109, 110, 116, 222, 254, 735, 1059], "logic": [74, 133, 236, 254, 268, 554, 568, 639, 669, 676, 692, 716, 719, 735, 745, 1034, 1051], "align_fram": 74, "pattern": [74, 101, 102, 112, 114, 115, 116, 170, 254, 446, 510, 511, 516, 517, 525, 526, 719, 738, 962, 963, 981, 982, 987, 988, 996, 997, 1051], "collis": 74, "need": [74, 97, 101, 102, 103, 105, 119, 120, 158, 159, 197, 205, 217, 254, 431, 440, 498, 520, 538, 613, 616, 631, 639, 671, 672, 735, 968, 991, 1009, 1051], "sure": [74, 90, 95, 101, 102, 106, 110, 158, 159, 190, 254, 671, 672, 735], "contigu": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 190, 254], "relev": 74, "df_h1": 74, "l1": 74, "l2": 74, "df_h2": 74, "r1": 74, "r2": 74, "r3": 74, "df_d1": 74, "df_d2": 74, "df_a1": 74, "df_a2": 74, "df_a3": 74, "disabl": [75, 129, 170, 254], "encount": [76, 158, 254, 459, 460, 518, 519, 580, 583, 595, 639, 671, 735, 930, 931, 989, 990, 1051], "least": [82, 124, 466, 560, 639, 934, 1039, 1051], "unexpect": [83, 254, 268, 438, 639, 745, 1051], "caus": [83, 91, 101, 102, 112, 132, 146, 254, 744, 846, 1051], "panic": 83, "mismatch": [85, 109], "incompat": 87, "pa": [90, 117], "chunkedarrai": [90, 182, 254, 789, 1051], "recordbatch": [90, 171, 254], "schemadefinit": [90, 92, 93, 94, 96, 108, 109, 254, 735], "schema_overrid": [90, 92, 93, 94, 95, 96, 108, 109, 171, 217, 254, 284, 735, 738, 760], "copi": [90, 91, 132, 135, 136, 171, 212, 217, 218, 231, 254, 366, 543, 639, 655, 656, 713, 735, 774, 778, 791, 847, 1014, 1027, 1032, 1033, 1051], "closest": 90, "pair": [90, 92, 93, 94, 96, 108, 109, 123, 191, 254, 693, 735, 1059], "sever": [90, 92, 93, 94, 96, 108, 109, 254, 735, 1059], "wai": [90, 92, 93, 94, 96, 108, 109, 140, 157, 171, 186, 207, 234, 254, 465, 467, 506, 516, 577, 639, 659, 670, 689, 702, 717, 719, 735, 987], "form": [90, 92, 93, 94, 96, 108, 109, 170, 196, 225, 254, 466, 639, 735, 934, 1051], "them": [90, 92, 93, 94, 96, 108, 109, 112, 146, 158, 159, 173, 180, 227, 254, 383, 414, 459, 460, 465, 578, 639, 671, 672, 677, 686, 735, 738, 744, 846, 898, 930, 931, 1051], "dimens": [90, 92, 94, 96, 108, 109, 254, 478, 639, 735, 944, 1051], "allow_copi": [91, 132], "interchang": [91, 132], "__dataframe__": 91, "convers": [91, 132, 170, 171, 196, 197, 214, 218, 254, 535, 536, 537, 540, 588, 650, 1006, 1007, 1008, 1011, 1031, 1032, 1033, 1051], "detail": [91, 103, 119, 120, 132, 254, 735, 1059], "latest": [91, 104, 113, 132, 344, 352, 374, 449, 639, 825, 833], "runtimeerror": 91, "from_panda": [91, 105], "from_arrow": 91, "effici": [91, 171, 254], "clone": [92, 93, 94, 95, 96, 135, 217, 218, 254, 655, 735, 774, 1031, 1032, 1033, 1041, 1051], "dimension": [92, 94, 96, 217, 254, 735, 1051], "infer_schema_length": [93, 96, 101, 102, 105, 112, 115, 254, 518, 735, 989], "NOT": [93, 119, 120, 447, 1058], "typic": [93, 133, 254, 324, 738, 745, 801, 1051], "clearer": 93, "load": [93, 95, 104, 113, 125, 127, 254, 650, 673, 680, 735, 1059], "_partial_": [93, 254, 735], "omit": [93, 97, 122, 124, 126, 130, 183, 197, 254, 627, 628, 738], "mani": [93, 96, 103, 146, 254, 518, 744, 846, 989, 1051], "scan": [93, 96, 101, 102, 110, 112, 113, 114, 115, 116, 117, 158, 159, 254, 664, 671, 672, 673, 680, 735], "slow": [93, 96, 101, 102, 112, 268, 309, 639, 719, 785, 1051], "partial": 93, "present": [93, 119, 124, 387, 639, 1041, 1051], "np": [94, 149, 217, 254, 550, 639, 735, 865, 869, 871, 872, 948, 1022, 1051], "ndarrai": [94, 149, 217, 254, 550, 639, 735, 789, 960, 963, 1022, 1032, 1051], "numpi": [94, 118, 138, 170, 196, 197, 214, 217, 218, 254, 459, 460, 639, 735, 865, 869, 871, 872, 930, 931, 948, 1032, 1033, 1041, 1051], "columnar": [94, 96, 170, 196, 254], "interpret": [94, 96, 101, 102, 112, 254, 735], "yield": [94, 96, 101, 102, 112, 144, 146, 222, 254, 465, 639, 735, 744, 840, 846, 1051], "conclus": [94, 96, 254, 735], "nan_to_nul": [95, 254, 735, 1051], "include_index": 95, "pd": [95, 105, 554, 639, 1033, 1034, 1051], "panda": [95, 105, 118, 158, 218, 254, 337, 338, 554, 639, 671, 735, 818, 819, 1033, 1034, 1051], "instal": [95, 101, 102, 103, 106, 110, 118, 138, 217, 218, 254, 700, 735, 1033, 1051], "nan": [95, 119, 120, 124, 132, 147, 218, 254, 314, 315, 358, 359, 367, 376, 377, 382, 389, 391, 392, 393, 398, 437, 441, 454, 459, 460, 461, 462, 556, 580, 639, 665, 735, 747, 751, 790, 848, 871, 872, 930, 931, 948, 1032, 1033, 1051, 1059], "convert": [95, 104, 105, 113, 132, 213, 214, 215, 216, 217, 220, 254, 311, 319, 348, 351, 431, 439, 473, 524, 535, 536, 537, 538, 540, 639, 735, 796, 829, 832, 915, 995, 1006, 1007, 1008, 1009, 1011, 1019, 1030, 1031, 1032, 1033, 1041, 1051], "pd_df": 95, "pd_seri": 95, "tbl": [97, 99, 102], "reconstruct": 97, "repr": [97, 124, 126], "trim": 97, "whitespac": [97, 522, 528, 534, 993, 999, 1005], "prompt": 97, "extract": [97, 320, 322, 323, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 339, 340, 342, 343, 346, 347, 349, 353, 354, 356, 467, 517, 518, 519, 639, 797, 798, 799, 800, 803, 804, 806, 810, 811, 812, 813, 815, 816, 817, 820, 821, 823, 824, 827, 828, 830, 834, 835, 837, 988, 989, 990], "to_init_repr": [97, 254, 1051], "truncat": [97, 158, 170, 196, 197, 214, 254, 341, 671, 690, 735, 822], "identifi": [97, 179, 185, 223, 254, 685, 708, 735], "compound": [97, 197, 254, 738], "struct": [97, 183, 200, 220, 224, 231, 254, 310, 431, 440, 471, 480, 481, 518, 531, 532, 560, 583, 584, 586, 605, 639, 696, 709, 713, 719, 735, 786, 915, 938, 946, 989, 1000, 1002, 1003, 1051], "neither": [97, 105, 198, 254, 431, 915], "source_ac": 97, "source_cha": 97, "ident": [97, 135, 136, 254, 348, 480, 481, 639, 655, 656, 735, 774, 778, 829, 946, 947, 1051], "timestamp": [97, 344, 597, 825], "tor_id": 97, "nnel_id": 97, "\u03bc": [97, 124, 158, 159, 170, 173, 196, 197, 214, 227, 254, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 535, 537, 588, 591, 597, 639, 671, 672, 677, 735, 738, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 836, 837, 1006, 1008], "asia": [97, 738, 797, 798, 830], "tokyo": [97, 738], "123456780": 97, 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702, 707, 713, 717, 735, 861, 1051], "stop": [100, 101, 102, 106, 110, 112, 114, 115, 116, 322, 325, 326, 328, 329, 334, 336, 342, 343, 345, 346, 350, 352, 353, 354, 356, 483, 484, 486, 489, 490, 491, 639, 794, 796, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 836, 837], "textio": 101, "new_column": [101, 102, 105, 112, 192, 254], "comment_char": [101, 102, 112], "quote_char": [101, 102, 112], "skip_row": [101, 102, 112], "missing_utf8_is_empty_str": [101, 102, 112], "ignore_error": [101, 102, 109, 112], "n_thread": [101, 102], "8192": 101, "csvencod": [101, 102, 112], "low_memori": [101, 102, 110, 112, 115, 116], "skip_rows_after_head": [101, 102, 112], "row_count_nam": [101, 102, 106, 110, 112, 114, 115, 116], "row_count_offset": [101, 102, 106, 110, 112, 114, 115, 116], "sample_s": [101, 102], "eol_char": [101, 102, 112], "handler": [101, 102, 105], "g": [101, 102, 104, 105, 106, 110, 113, 114, 116, 158, 159, 173, 217, 225, 227, 254, 261, 268, 310, 341, 345, 352, 363, 471, 483, 484, 485, 486, 487, 489, 490, 491, 498, 593, 639, 671, 672, 677, 693, 735, 822, 826, 833, 961, 1032, 1051], "builtin": [101, 102, 105], "stringio": [101, 102], "fsspec": [101, 102, 106, 110, 113, 114, 116, 118], "remot": [101, 102, 106, 110], "autogener": [101, 102, 112], "column_x": [101, 102, 112], "enumer": [101, 102, 112, 171, 254], "shorter": [101, 102], "comment": [101, 102, 112], "instanc": [101, 102, 112, 124, 126, 130, 146, 152, 254, 293, 449, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 639, 681, 715, 735, 744, 767, 846, 1051], "escap": [101, 102, 112], "dure": [101, 102, 112, 130, 146, 254, 744, 846, 1051], "would": [101, 102, 112, 278, 412, 448, 474, 573, 588, 639, 738, 754, 896, 940, 1032, 1051], "prefer": [101, 102, 104, 112, 127, 133, 146, 170, 195, 196, 236, 254, 268, 482, 639, 735, 744, 745, 846, 948, 1051, 1059], "treat": [101, 102, 112, 510, 525, 526, 981, 996, 997], "10000": [101, 171, 254, 543], "might": [101, 102, 112, 128, 134, 217, 221, 254, 268, 558, 639, 653, 702, 707, 735, 1033, 1051], "issu": [101, 102, 105, 112, 307, 308, 548, 639, 783, 784, 1020, 1051], "iso8601": [101, 102, 112], "physic": [101, 102, 173, 254, 295, 554, 639, 654, 676, 677, 735, 770, 1034, 1051], "cpu": [101, 102], "system": [101, 102], "wrongli": 101, "done": [101, 102, 112, 117, 156, 173, 254, 267, 269, 293, 465, 521, 527, 569, 639, 677, 735, 767, 992, 998, 1051], "buffer": [101, 102, 144, 170, 254, 840, 1051], "modifi": [101, 102, 112, 128, 130, 146, 163, 170, 229, 254, 279, 344, 355, 510, 516, 517, 525, 639, 744, 825, 836, 846, 943, 981, 987, 988, 996, 1010, 1012, 1013, 1051], "upper": [101, 102, 144, 158, 254, 298, 299, 383, 431, 559, 570, 588, 589, 601, 602, 627, 628, 639, 671, 735, 776, 840, 861, 920, 1038, 1051], "bound": [101, 102, 144, 158, 254, 298, 299, 300, 383, 412, 430, 431, 436, 559, 570, 588, 589, 601, 602, 627, 628, 639, 671, 735, 776, 777, 840, 861, 896, 914, 920, 1038, 1051], "lossi": [101, 102, 112], "decod": [101, 102], "usag": [101, 102, 112, 124, 126, 205, 254, 738, 969, 1051], "expens": [101, 102, 110, 112, 115, 116, 125, 127, 133, 158, 159, 170, 195, 196, 197, 222, 223, 254, 268, 639, 671, 672, 708, 735, 744, 745, 846, 1051, 1059], "aggreg": [101, 102, 110, 152, 157, 158, 159, 176, 177, 178, 181, 183, 187, 188, 189, 208, 209, 225, 228, 234, 235, 238, 240, 254, 262, 369, 380, 438, 465, 482, 483, 484, 485, 486, 487, 489, 490, 491, 560, 564, 565, 566, 567, 583, 584, 585, 586, 595, 600, 606, 607, 610, 611, 615, 623, 624, 639, 671, 672, 681, 682, 683, 684, 687, 688, 691, 703, 704, 711, 717, 718, 721, 723, 735, 858, 948, 949, 950, 952, 955, 956, 957, 1051], "nativ": [101, 106, 110, 122, 133, 170, 196, 197, 214, 236, 254, 268, 568, 639, 644, 719, 745, 1051, 1059], "parser": 101, "even": [101, 467, 471, 639, 938, 1051], "regard": [101, 580], "sens": [101, 106, 110, 114, 116, 180, 227, 254, 465, 639, 686, 735], "particular": [101, 106, 110, 114, 116, 144, 254, 840, 1051], "usernam": [101, 103, 106, 110, 114, 116], "password": [101, 103, 106, 110, 114, 116], "skip": [101, 102, 105, 110, 112, 116, 225, 254, 745, 1051], "offset": [101, 102, 106, 110, 112, 114, 115, 116, 158, 159, 206, 227, 232, 254, 316, 324, 337, 338, 341, 345, 352, 426, 504, 529, 535, 537, 588, 589, 639, 671, 672, 701, 715, 735, 793, 801, 818, 819, 822, 826, 833, 910, 975, 1000, 1006, 1008, 1051], "row_count": [101, 102, 106, 110, 112, 114, 115, 116], "sampl": [101, 102, 236, 254, 503, 639, 719, 974, 1051], "estim": [101, 102, 144, 254, 269, 361, 362, 396, 569, 639, 735, 840, 842, 843, 881, 1051], "alloc": [101, 102, 144, 190, 254, 840, 1051], "lazili": [101, 102, 112, 113, 114, 115, 116, 440, 639], "glob": [101, 102, 112, 114, 115, 116], "continu": [101, 110, 310, 471, 503, 639, 786, 938, 974, 1051], "benchmark": [101, 110], "50000": 102, "batchedcsvread": [102, 650], "upon": 102, "creation": 102, "gather": 102, "next_batch": 102, "big": 102, "interest": 102, "seen_group": 102, "big_fil": 102, "df_current_batch": 102, "concat": [102, 773, 927, 1051], "partition_df": 102, "partition_bi": [102, 171, 254], "as_dict": [102, 185, 254], "fh": 102, "write_csv": [102, 112, 254], "els": [102, 630], "partition_on": 103, "partition_rang": 103, "partition_num": 103, "dbreadengin": 103, "raw": 103, "connectorx": [103, 118], "driver": 103, "snowflak": 103, "warehous": 103, "role": 103, "transfer": 103, "document": [103, 105, 348, 351, 519, 535, 536, 537, 540, 829, 832, 990, 1006, 1007, 1008, 1011], "redshift": 103, "mysql": 103, "mariadb": 103, "clickhous": 103, "oracl": 103, "bigqueri": 103, "pleas": [103, 158, 254, 671, 735], "doc": [103, 138, 254], "github": 103, "sfu": 103, "connector": 103, "destin": 103, "small": [103, 123, 174, 254, 345, 664, 735, 1059], "still": 103, "develop": [103, 124, 126], "explicitli": [103, 122, 124, 130, 440, 622, 639, 649], "test_tabl": 103, "compani": 103, "testdb": 103, "public": [103, 254, 639, 650, 735, 1051], "myrol": 103, "delta_table_opt": [104, 113], "root": [104, 113, 296, 395, 439, 450, 469, 507, 547, 638, 639, 771, 977, 1051], "absolut": [104, 113, 119, 120, 260, 360, 361, 362, 639, 740, 841, 842, 843, 1051], "sinc": [104, 113, 134, 221, 254, 292, 325, 535, 537, 570, 588, 597, 616, 627, 639, 653, 702, 707, 735, 744, 802, 1006, 1008, 1051], "avoid": [104, 196, 254, 263, 639], "year": [104, 113, 158, 159, 173, 227, 254, 328, 329, 341, 342, 345, 352, 353, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 590, 639, 671, 672, 677, 735, 805, 806, 822, 823, 826, 833, 834], "2021": [104, 113, 139, 156, 158, 227, 254, 535, 604, 671, 735, 738, 797, 798, 806, 830, 877, 1006, 1051], "aw": [104, 113], "googl": [104, 113], "service_account": [104, 113], "service_account_json_absolute_path": [104, 113], "az": [104, 113], "adl": [104, 113], "abf": [104, 113], "azure_storage_account_nam": [104, 113], "azure_storage_account_kei": [104, 113], "without_fil": [104, 113], "track": [104, 113, 133, 254, 431, 1058], "sheet_id": 105, "sheet_nam": 105, "xlsx2csv_option": 105, "read_csv_opt": 105, "noreturn": 105, "xlsx2csv": [105, 118], "read_csv": [105, 112], "nor": [105, 198, 254], "skip_empty_lin": 105, "my": [105, 117, 541, 1012], "datasheet": 105, "correct": [105, 361, 362, 396, 488, 503, 639, 681, 735, 842, 843, 881, 954, 974, 1051], "look": [105, 286, 431, 761], "whole": [105, 505, 506, 516, 639, 719, 735, 987], "With": [105, 133, 169, 254, 268, 483, 484, 485, 486, 487, 489, 490, 491, 639, 745, 880, 1051], "1000": [105, 144, 216, 254, 330, 331, 543, 811, 1030, 1051, 1059], "spreadsheet": [105, 187, 254], "xl": 105, "xlsm": 105, "xlsb": 105, "odf": 105, "od": 105, "odt": 105, "memory_map": [106, 110, 114], "v2": [106, 114], "greatli": [106, 114], "repeat": [106, 114, 310, 471, 475, 477, 613, 631, 639], "give": [106, 110, 114, 115, 116, 179, 223, 254, 325, 396, 477, 556, 639, 650, 685, 708, 735, 802, 881, 1051], "That": [106, 681, 735], "filenam": 106, "my_fil": 106, "write_ipc": [106, 254], "read_ndjson": 108, "becaus": 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352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "hard": [112, 681, 735], "mydf": 112, "lambda": [112, 133, 152, 186, 236, 254, 268, 360, 361, 362, 431, 438, 439, 482, 568, 583, 584, 595, 605, 615, 639, 681, 689, 719, 735, 745, 841, 842, 843, 915, 1051], "lower": [112, 158, 189, 246, 254, 298, 300, 383, 436, 439, 472, 476, 487, 570, 588, 589, 601, 602, 614, 627, 628, 639, 664, 671, 691, 729, 735, 777, 861, 920, 939, 942, 953, 1038, 1051], "simpli": [112, 465, 583, 595, 639], "idx": [112, 158, 171, 254, 431, 671, 735, 915, 963, 1051], "uint16": [112, 307, 308, 548, 639, 738, 783, 784, 1020, 1051], "u16": [112, 124, 1059], "eu": 113, "central": [113, 396, 503, 639, 881, 974, 1051], "allow_pyarrow_filt": 117, "comparison": 117, "dset": 117, "folder": 117, "05": [117, 119, 120, 124, 173, 227, 254, 318, 319, 323, 344, 346, 348, 351, 588, 677, 735, 738, 795, 796, 797, 798, 799, 800, 825, 826, 829, 830, 832, 833, 835], "04": [117, 158, 227, 254, 318, 319, 323, 327, 334, 335, 336, 337, 338, 344, 345, 346, 347, 348, 351, 353, 355, 535, 588, 591, 671, 735, 738, 796, 800, 804, 810, 812, 815, 816, 817, 818, 819, 820, 824, 825, 827, 828, 829, 832, 834, 835, 836, 1006], "stdout": [118, 130, 156, 254, 451], "17": [118, 124, 234, 492, 597, 616, 627, 639, 717, 779, 826, 1051, 1059], "platform": 118, "linux": 118, "90": [118, 538, 1009], "wsl2": 118, "x86_64": 118, "glibc2": 118, "main": 118, "apr": 118, "14": [118, 124, 133, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 316, 329, 382, 474, 627, 639, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 738, 793, 826], "44": [118, 180, 254, 313, 639, 686, 735], "51": 118, "gcc": 118, "matplotlib": [118, 690, 700, 735], "check_dtyp": [119, 120], "check_exact": [119, 120], "rtol": [119, 120], "1e": [119, 120], "atol": [119, 120], "08": [119, 120, 124, 159, 254, 318, 345, 535, 588, 591, 672, 735, 738, 826, 833, 1006], "nans_compare_equ": [119, 120], "check_column_ord": 119, "check_row_ord": 119, "assertionerror": [119, 120], "compar": [119, 120, 153, 254, 358, 359, 376, 377, 398, 437, 461, 462, 639, 961, 1051], "exactli": [119, 120, 123, 124, 126, 531, 532, 969, 1002, 1003, 1051], "toler": [119, 120, 173, 254, 677, 735], "inexact": [119, 120], "assert": [119, 120, 122, 124, 126, 159, 254, 650, 672, 735, 738, 854, 1051], "irrespect": 119, "unsort": 119, "check_nam": 120, "s1": [120, 152, 254, 880, 915, 1033, 1042, 1051], "searchstrategi": [121, 123, 124, 126], "null_prob": [121, 124, 126], "percentag": [121, 124, 126, 466, 639, 934, 1051], "chanc": [121, 124, 126, 1059], "independ": [121, 122, 124, 126], "flag": [121, 254, 495, 510, 516, 517, 525, 639, 662, 681, 735, 964, 981, 987, 988, 996, 1051], "hypothesi": [121, 122, 123, 124, 125, 126, 1059], "sampled_from": [121, 1059], "unique_small_int": 121, "ccy": [121, 1059], "gbp": [121, 1059], "eur": [121, 139, 156, 254, 1059], "jpy": [121, 1059], "min_col": [122, 124], "max_col": [122, 124], "standalon": [122, 124], "mincol": 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"n_chunk": [124, 126, 254, 744, 846, 1051], "randomis": 124, "onto": 124, "pct": 124, "preced": [124, 738], "disallow": [124, 126], "inf": [124, 126, 275, 310, 385, 388, 471, 556, 639, 751, 786, 857, 865, 869, 920, 934, 938, 1038, 1051], "exclud": [124, 126, 305, 308, 383, 577, 639, 861, 877, 1051], "deploi": [124, 126], "characterist": [124, 126], "concret": [124, 126], "test_repr": 124, "isinst": [124, 126, 254], "0x11f561580": 124, "known": [124, 431, 719, 915], "0565": 124, "34715": 124, "5844": 124, "33": [124, 180, 254, 492, 577, 639, 686, 735, 953, 1051], "076854": 124, "3382": 124, "48662": 124, "7540": 124, "29": [124, 158, 159, 173, 227, 254, 316, 337, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 793, 818, 822, 826, 833], "836271": 124, "4063": 124, "06": [124, 227, 254, 318, 329, 343, 344, 356, 738, 825, 826, 835], "39092": 124, "1889": 124, "13": [124, 135, 136, 147, 148, 155, 159, 164, 174, 182, 231, 237, 238, 240, 241, 242, 243, 244, 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130, "cleaner": 130, "breviti": 130, "vein": 130, "durat": [130, 173, 227, 254, 316, 323, 324, 327, 331, 333, 335, 340, 347, 352, 355, 554, 588, 589, 627, 628, 639, 677, 735, 738, 793, 800, 801, 804, 811, 813, 816, 821, 828, 833, 836, 1034, 1051], "set_ascii_t": 130, "write_ascii_frame_to_stdout": 130, "sy": 130, "nan_as_nul": 132, "_pyarrowdatafram": 132, "nullabl": 132, "extens": [132, 218, 254, 1033, 1051], "propag": [132, 177, 209, 254, 359, 459, 460, 462, 639, 930, 931, 1051], "inference_s": [133, 254], "256": [133, 254, 934, 1051], "much": [133, 225, 236, 254, 268, 309, 430, 568, 639, 719, 745, 785, 914, 1051], "almost": [133, 236, 254, 535, 536, 537, 588, 745, 1006, 1007, 1008, 1051], "_significantly_": [133, 236, 254, 745, 1051], "intens": [133, 236, 254, 465, 639, 745, 1051], "forc": [133, 173, 236, 254, 676, 677, 719, 735, 745, 1051], "materi": [133, 236, 254, 690, 719, 735, 738, 745, 1051], "parallelis": [133, 236, 254, 745, 1051], "optimis": [133, 197, 236, 254, 735, 745, 962, 963, 1051], "achiev": [133, 236, 254, 268, 639, 745, 1051], "best": [133, 236, 254, 268, 639, 745, 1051], "tri": [133, 254], "arbitrarili": [133, 254], "rearrang": [133, 254], "transform": [133, 254, 438, 539, 541, 542, 639], "preserv": [133, 157, 217, 218, 254, 500, 639, 971, 1033, 1051], "lru_cach": [133, 254, 268, 639, 745, 1051], "magnitud": [133, 254, 268, 639, 745, 1051], "column_1": [133, 222, 254], "better": [133, 217, 236, 254, 268, 560, 639, 719, 962, 963, 1051], "scalar": [133, 169, 195, 254, 494, 568, 604, 639, 880, 960, 1051], "k": [134, 197, 221, 254, 291, 295, 396, 555, 639, 653, 707, 735, 766, 770, 881, 1035, 1051], "intoexpr": [134, 157, 158, 159, 200, 207, 221, 231, 234, 254, 267, 383, 407, 421, 422, 423, 424, 465, 497, 506, 564, 565, 566, 567, 570, 573, 576, 578, 579, 583, 585, 586, 588, 589, 595, 601, 602, 606, 607, 610, 611, 616, 619, 622, 623, 624, 627, 628, 630, 639, 653, 667, 670, 671, 672, 696, 702, 707, 713, 717, 735, 861, 1051], "nulls_last": [134, 207, 221, 254, 278, 505, 639, 653, 702, 707, 735, 754, 1051], "smallest": [134, 221, 254, 291, 639, 653, 707, 735, 766, 1051], "largest": [134, 158, 159, 173, 221, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 555, 639, 653, 671, 672, 677, 707, 735, 822, 826, 833, 1035, 1051], "last": [134, 161, 173, 175, 187, 197, 207, 210, 221, 223, 248, 254, 278, 309, 322, 337, 342, 353, 395, 412, 429, 469, 505, 532, 547, 549, 588, 625, 639, 653, 677, 702, 705, 707, 708, 731, 735, 738, 754, 785, 799, 818, 823, 834, 856, 883, 896, 913, 1003, 1021, 1051], "wors": [134, 221, 254, 653, 702, 707, 735], "search": [134, 173, 221, 254, 653, 677, 702, 707, 735], "top_k": [134, 254, 291, 639, 653, 735, 766, 1051], "greater": [135, 173, 254, 376, 377, 503, 532, 639, 677, 735, 974, 1003, 1051], "cheap": [135, 136, 254, 655, 656, 735, 744, 774, 778, 1051], "deepcopi": [135, 136, 254, 655, 656, 735, 774, 778, 1051], "clear": [136, 254, 656, 735, 778, 1051], "properti": [137, 143, 151, 162, 199, 202, 230, 254, 658, 661, 695, 712, 735, 738, 1059], "appl": [137, 163, 172, 191, 193, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 479, 514, 533, 639, 676, 693, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 985, 1004], "banana": [137, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 479, 639, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730], "pairwis": [138, 254], "pearson": [138, 254, 396, 503, 580, 639, 881, 974, 1051], "correl": [138, 254, 580, 617], "coeffici": [138, 254, 503, 639, 974, 1051], "corrcoef": [138, 254], "percentil": [139, 254, 787, 1051], "summari": [139, 254, 787, 1051], "glimps": [139, 161, 254], "usd": [139, 156, 254, 1059], "2020": [139, 156, 159, 254, 319, 323, 324, 327, 330, 331, 333, 335, 340, 344, 347, 348, 351, 352, 535, 536, 537, 672, 735, 738, 796, 800, 801, 804, 811, 813, 816, 821, 825, 828, 829, 832, 833, 1006, 1007, 1008], "null_count": [139, 142, 254, 309, 639, 735, 785, 787, 1051], "266667": [139, 254], "666667": [139, 177, 228, 242, 254, 360, 639, 711, 725, 735], "std": [139, 254, 482, 489, 639, 735, 787, 955, 1051], "101514": [139, 254], "707107": [139, 254, 361, 489, 639, 842, 1051], "57735": [139, 254], "median": [139, 187, 254, 368, 485, 639, 714, 735, 787, 951, 1051], "more_column": [140, 145, 201, 224, 254, 363, 593, 639, 659, 663, 697, 709, 735], "Or": [140, 157, 158, 159, 173, 207, 227, 234, 254, 465, 506, 577, 630, 639, 659, 670, 671, 672, 677, 702, 717, 735], "subset": [142, 183, 223, 254, 660, 708, 735], "snippet": [142, 254, 660, 735], "all_horizont": [142, 254, 564, 660, 735], "is_nul": [142, 254, 639, 660, 735, 1051], "sizeunit": [144, 254, 840, 1051], "heap": [144, 254, 840, 1051], "its": [144, 254, 318, 345, 352, 506, 639, 795, 826, 833, 840, 1051], "bitmap": [144, 254, 840, 1051], "therefor": [144, 254, 630, 840, 1051], "structarrai": [144, 254, 840, 1051], "constant": [144, 159, 254, 316, 366, 639, 672, 735, 793, 840, 847, 1051], "unchang": [144, 254, 554, 639, 681, 719, 735, 840, 1034, 1051], "capac": [144, 205, 254, 840, 969, 1051], "ffi": [144, 254, 840, 1051], "kb": [144, 254, 840, 1051], "mb": [144, 254, 840, 1051], "gb": [144, 254, 840, 1051], "tb": [144, 254, 840, 1051], "revers": [144, 254, 304, 305, 306, 307, 308, 439, 469, 547, 639, 735, 781, 782, 783, 784, 1051], "1_000_000": [144, 254, 840, 1051], "25888898": [144, 254], "689577102661133": [144, 254], "long": [145, 179, 225, 254, 663, 685, 735], "letter": [145, 239, 248, 254, 363, 517, 593, 639, 663, 722, 731, 735, 738, 988], "onlin": [146, 254, 744, 846, 1051], "rerun": [146, 254, 744, 846, 1051], "conveni": [146, 254, 744, 846, 1051], "evalu": [147, 149, 173, 254, 265, 279, 309, 381, 401, 402, 431, 440, 464, 564, 566, 570, 574, 588, 589, 592, 601, 602, 613, 616, 622, 627, 628, 630, 631, 639, 667, 674, 676, 677, 735, 755, 785, 885, 886, 1042, 1051], "Not": [147, 254, 389, 391, 440, 639, 665, 735], "To": [147, 254, 314, 315, 341, 368, 510, 516, 517, 525, 541, 623, 639, 665, 735, 822, 981, 987, 988, 996, 1012, 1032, 1051], "fillnullstrategi": [148, 254, 368, 639, 666, 735, 849, 1051], "matches_supertyp": [148, 254, 666, 735], "forward": [148, 173, 254, 337, 368, 374, 639, 666, 677, 735, 818, 849, 1051], "consecut": [148, 254, 285, 368, 374, 509, 639, 666, 735, 849, 980, 1051], "fill_nan": [148, 254, 639, 735, 1051], "OR": [149, 254, 566, 567, 667, 735, 738], "reduct": [152, 254], "supercast": [152, 254], "parent": [152, 254], "rule": [152, 254], "arithmet": [152, 254], "zip_with": [152, 254, 1051], "foo11": [152, 254], "bar22": [152, 254], "null_equ": [153, 254, 961, 1051], "retriev": [154, 254, 403, 404, 544, 887, 888, 1015], "return_as_str": [156, 254, 451], "preview": [156, 254], "wide": [156, 179, 225, 254, 685, 735], "nice": [156, 254], "few": [156, 254], "rather": [156, 173, 254, 451, 481, 543, 639, 677, 735, 947, 1014, 1051], "head": [156, 175, 210, 254, 267, 400, 639, 680, 735, 883, 1021, 1051], "tail": [156, 161, 254, 267, 503, 639, 735, 856, 974, 1051], "more_bi": [157, 185, 207, 254, 506, 639, 670, 702, 735], "consist": [157, 185, 254, 535, 670, 735, 744, 846, 1006, 1051], "regardless": [157, 254, 519, 990], "agg": [157, 158, 159, 254, 262, 268, 369, 371, 505, 506, 550, 562, 581, 639, 657, 662, 664, 670, 671, 672, 690, 700, 735, 738], "index_column": [158, 159, 254, 671, 672, 735], "timedelta": [158, 159, 227, 254, 322, 325, 326, 329, 334, 336, 341, 342, 343, 345, 346, 350, 352, 353, 354, 356, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 738, 807, 809, 814, 822, 826, 833, 930, 931, 1051], "period": [158, 159, 203, 204, 254, 345, 352, 360, 361, 362, 425, 466, 496, 497, 588, 589, 627, 628, 639, 671, 672, 698, 699, 735, 826, 833, 841, 842, 843, 909, 934, 966, 967, 1051], "include_boundari": [158, 254, 671, 735], "closedinterv": [158, 159, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 861, 1051], "start_bi": [158, 254, 671, 735], "startbi": [158, 254, 671, 735], "window": [158, 159, 254, 309, 345, 352, 360, 361, 362, 465, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 578, 617, 618, 639, 671, 672, 735, 785, 826, 833, 841, 842, 843, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1051], "check_sort": [158, 159, 254, 671, 672, 735], "dynamicgroupbi": [158, 254], "groupbi": [158, 159, 183, 254, 262, 268, 309, 369, 371, 409, 465, 505, 506, 550, 562, 568, 581, 639, 657, 662, 664, 671, 672, 690, 700, 735, 738, 785, 893, 1051], "member": [158, 254, 671, 735, 868, 1051], "seen": [158, 254, 285, 374, 639, 671, 735], "roll": [158, 159, 254, 337, 338, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 578, 617, 618, 639, 671, 672, 735, 818, 819, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1051], "slot": [158, 254, 309, 312, 408, 639, 671, 735, 785, 788, 892, 1051], "interv": [158, 159, 227, 254, 310, 328, 345, 346, 352, 383, 471, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 786, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 837, 861, 938, 1051], "1n": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "nanosecond": [158, 159, 173, 227, 254, 341, 345, 346, 352, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 822, 826, 827, 833], "1u": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "microsecond": [158, 159, 170, 173, 196, 197, 214, 227, 254, 341, 345, 346, 352, 483, 484, 485, 486, 487, 489, 490, 491, 590, 591, 626, 639, 671, 672, 677, 690, 735, 738, 822, 826, 833], "1m": [158, 159, 173, 227, 254, 330, 331, 333, 340, 341, 345, 347, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 811, 813, 821, 822, 826, 828, 833], "millisecond": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 738, 822, 826, 833], "minut": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 590, 591, 626, 627, 639, 671, 672, 677, 735, 738, 822, 826, 833], "1h": [158, 159, 173, 227, 254, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 627, 628, 639, 671, 672, 677, 735, 801, 803, 822, 826, 833], "hour": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 590, 591, 626, 627, 639, 671, 672, 677, 735, 738, 822, 826, 833], "1d": [158, 159, 173, 227, 254, 317, 327, 335, 341, 345, 352, 355, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 604, 639, 671, 672, 677, 735, 738, 794, 802, 804, 807, 808, 809, 814, 816, 822, 826, 831, 833, 835, 836], "1w": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "week": [158, 159, 173, 227, 254, 341, 345, 352, 354, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 738, 822, 826, 833, 835], "1mo": [158, 159, 173, 227, 254, 319, 323, 337, 338, 341, 344, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 639, 671, 672, 677, 735, 796, 800, 817, 818, 819, 822, 823, 824, 825, 826, 833, 834], "month": [158, 159, 173, 227, 254, 322, 337, 338, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 589, 590, 639, 671, 672, 677, 735, 799, 818, 819, 822, 826, 833], "1q": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "quarter": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "1y": [158, 159, 173, 227, 254, 328, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 805, 822, 826, 833, 837], "1i": [158, 159, 173, 227, 254, 341, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822], "3d12h4m25": [158, 159, 173, 227, 254, 345, 352, 671, 672, 677, 735, 826, 833], "suffix": [158, 159, 172, 173, 200, 227, 231, 234, 254, 263, 289, 341, 345, 352, 389, 391, 392, 393, 439, 465, 469, 479, 483, 484, 485, 486, 487, 489, 490, 491, 514, 639, 671, 672, 676, 677, 696, 713, 714, 717, 735, 738, 764, 822, 826, 833, 985], "_satur": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 639, 671, 672, 677, 735, 822, 826, 833], "satur": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "28": [158, 159, 173, 213, 227, 254, 261, 341, 344, 345, 352, 355, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 822, 825, 826, 833, 836, 1059], "correspond": [158, 159, 173, 217, 227, 254, 329, 341, 345, 352, 474, 481, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 806, 822, 826, 833, 940, 947, 1051], "due": [158, 159, 173, 197, 227, 254, 263, 293, 324, 341, 345, 352, 395, 469, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 547, 639, 671, 672, 677, 735, 767, 801, 822, 826, 833, 1051], "daylight": [158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 801, 822, 826, 833], "10i": [158, 159, 254, 671, 672, 735], "ascend": [158, 159, 254, 671, 672, 735], "dynam": [158, 254, 431, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 735, 915], "matter": [158, 159, 170, 196, 197, 214, 254, 671, 672, 735], "_lower_bound": [158, 254, 671, 735], "_upper_bound": [158, 254, 671, 735], "harder": [158, 254, 671, 735], "tempor": [158, 159, 170, 196, 197, 214, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 639, 650, 671, 672, 735, 738, 861, 877, 1051], "inclus": [158, 159, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 530, 531, 570, 588, 589, 601, 602, 627, 628, 639, 671, 672, 735, 861, 1001, 1002, 1051], "datapoint": [158, 254, 671, 735], "mondai": [158, 254, 352, 354, 671, 735, 833, 835], "tuesdai": [158, 254, 671, 735], "wednesdai": [158, 254, 671, 735], "thursdai": [158, 254, 671, 735], "fridai": [158, 254, 671, 735], "saturdai": [158, 254, 671, 735], "sundai": [158, 254, 354, 671, 735, 835], "weekli": [158, 254, 352, 671, 735, 833], "sorted": [158, 159, 254, 671, 672, 735], "metadata": [158, 159, 254, 671, 672, 735], "verifi": [158, 159, 254, 671, 672, 735], "incorrectli": [158, 159, 254, 431, 671, 672, 735], "incorrect": [158, 159, 254, 355, 495, 639, 671, 672, 719, 735, 836, 964, 1051], "re": [158, 217, 254, 337, 338, 671, 735, 818, 819, 1058], "come": [158, 254, 337, 338, 396, 639, 651, 671, 734, 735, 818, 819, 881, 1051], "set_index": [158, 254, 671, 735], "resampl": [158, 254, 671, 735], "reset_index": [158, 254, 671, 735], "though": [158, 254, 671, 735], "evenli": [158, 254, 471, 639, 671, 735, 938, 1051], "upsampl": [158, 254, 671, 735], "date_rang": [158, 227, 254, 317, 319, 322, 323, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 350, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 639, 671, 735, 793, 794, 796, 799, 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method)": [[49, "polars.List.__init__"]], "null (class in polars)": [[50, "polars.Null"]], "__init__() (polars.null method)": [[50, "polars.Null.__init__"]], "object (class in polars)": [[51, "polars.Object"]], "__init__() (polars.object method)": [[51, "polars.Object.__init__"]], "execute() (polars.sqlcontext method)": [[52, "polars.SQLContext.execute"]], "register() (polars.sqlcontext method)": [[53, "polars.SQLContext.register"]], "register_globals() (polars.sqlcontext method)": [[54, "polars.SQLContext.register_globals"]], "register_many() (polars.sqlcontext method)": [[55, "polars.SQLContext.register_many"]], "tables() (polars.sqlcontext method)": [[56, "polars.SQLContext.tables"]], "unregister() (polars.sqlcontext method)": [[57, "polars.SQLContext.unregister"]], "stringcache (class in polars)": [[58, "polars.StringCache"]], "__init__() (polars.stringcache method)": [[58, "polars.StringCache.__init__"]], "struct (class in polars)": [[59, "polars.Struct"]], "__init__() (polars.struct method)": [[59, "polars.Struct.__init__"]], "time (class in polars)": [[60, "polars.Time"]], "__init__() (polars.time method)": [[60, "polars.Time.__init__"]], "uint16 (class in polars)": [[61, "polars.UInt16"]], "__init__() (polars.uint16 method)": [[61, "polars.UInt16.__init__"]], "uint32 (class in polars)": [[62, "polars.UInt32"]], "__init__() (polars.uint32 method)": [[62, "polars.UInt32.__init__"]], "uint64 (class in polars)": [[63, "polars.UInt64"]], "__init__() (polars.uint64 method)": [[63, "polars.UInt64.__init__"]], "uint8 (class in polars)": [[64, "polars.UInt8"]], "__init__() (polars.uint8 method)": [[64, "polars.UInt8.__init__"]], "unknown (class in polars)": [[65, "polars.Unknown"]], "__init__() (polars.unknown method)": [[65, "polars.Unknown.__init__"]], "utf8 (class in polars)": [[66, "polars.Utf8"]], "__init__() (polars.utf8 method)": [[66, "polars.Utf8.__init__"]], "align_frames() (in module polars)": [[67, "polars.align_frames"]], "register_dataframe_namespace() (in module polars.api)": [[68, "polars.api.register_dataframe_namespace"]], "register_expr_namespace() (in module polars.api)": [[69, "polars.api.register_expr_namespace"]], "register_lazyframe_namespace() (in module polars.api)": [[70, "polars.api.register_lazyframe_namespace"]], "register_series_namespace() (in module polars.api)": [[71, "polars.api.register_series_namespace"]], "build_info() (in module polars)": [[72, "polars.build_info"]], "collect_all() (in module polars)": [[73, "polars.collect_all"]], "concat() (in module polars)": [[74, "polars.concat"]], "enable_string_cache() (in module polars)": [[75, "polars.enable_string_cache"]], "arrowerror": [[76, "polars.exceptions.ArrowError"]], "columnnotfounderror": [[77, "polars.exceptions.ColumnNotFoundError"]], "computeerror": [[78, "polars.exceptions.ComputeError"]], "duplicateerror": [[79, "polars.exceptions.DuplicateError"]], "invalidoperationerror": [[80, "polars.exceptions.InvalidOperationError"]], "nodataerror": [[81, "polars.exceptions.NoDataError"]], "norowsreturnederror": [[82, "polars.exceptions.NoRowsReturnedError"]], "polarspanicerror": [[83, "polars.exceptions.PolarsPanicError"]], "rowserror": [[84, "polars.exceptions.RowsError"]], "schemaerror": [[85, "polars.exceptions.SchemaError"]], "schemafieldnotfounderror": [[86, "polars.exceptions.SchemaFieldNotFoundError"]], "shapeerror": [[87, "polars.exceptions.ShapeError"]], "structfieldnotfounderror": [[88, "polars.exceptions.StructFieldNotFoundError"]], "toomanyrowsreturnederror": [[89, "polars.exceptions.TooManyRowsReturnedError"]], "from_arrow() (in module polars)": [[90, "polars.from_arrow"]], "from_dataframe() (in module polars)": [[91, "polars.from_dataframe"]], "from_dict() (in module polars)": [[92, "polars.from_dict"]], "from_dicts() (in module polars)": [[93, "polars.from_dicts"]], "from_numpy() (in module polars)": [[94, "polars.from_numpy"]], "from_pandas() (in module polars)": [[95, "polars.from_pandas"]], "from_records() (in module polars)": [[96, "polars.from_records"]], "from_repr() (in module polars)": [[97, "polars.from_repr"]], "get_index_type() (in module polars)": [[98, "polars.get_index_type"]], "next_batches() (polars.io.csv.batched_reader.batchedcsvreader method)": [[99, "polars.io.csv.batched_reader.BatchedCsvReader.next_batches"]], "read_avro() (in module polars)": [[100, "polars.read_avro"]], "read_csv() (in module polars)": [[101, "polars.read_csv"]], "read_csv_batched() (in module polars)": [[102, "polars.read_csv_batched"]], "read_database() (in module polars)": [[103, "polars.read_database"]], "read_delta() (in module polars)": [[104, "polars.read_delta"]], "read_excel() (in module polars)": [[105, "polars.read_excel"]], "read_ipc() (in module polars)": [[106, "polars.read_ipc"]], "read_ipc_schema() (in module polars)": [[107, "polars.read_ipc_schema"]], "read_json() (in module polars)": [[108, "polars.read_json"]], "read_ndjson() (in module polars)": [[109, "polars.read_ndjson"]], "read_parquet() (in module polars)": [[110, "polars.read_parquet"]], "read_parquet_schema() (in module polars)": [[111, "polars.read_parquet_schema"]], "scan_csv() (in module polars)": [[112, "polars.scan_csv"]], "scan_delta() (in module polars)": [[113, "polars.scan_delta"]], "scan_ipc() (in module polars)": [[114, "polars.scan_ipc"]], "scan_ndjson() (in module polars)": [[115, "polars.scan_ndjson"]], "scan_parquet() (in module polars)": [[116, "polars.scan_parquet"]], "scan_pyarrow_dataset() (in module polars)": [[117, "polars.scan_pyarrow_dataset"]], "show_versions() (in module polars)": [[118, "polars.show_versions"]], "assert_frame_equal() (in module polars.testing)": [[119, "polars.testing.assert_frame_equal"]], "assert_series_equal() (in module polars.testing)": [[120, "polars.testing.assert_series_equal"]], "__init__() (polars.testing.parametric.column method)": [[121, "polars.testing.parametric.column.__init__"]], "column (class in polars.testing.parametric)": [[121, "polars.testing.parametric.column"]], "columns() (in module polars.testing.parametric)": [[122, "polars.testing.parametric.columns"]], "create_list_strategy() (in module polars.testing.parametric)": [[123, "polars.testing.parametric.create_list_strategy"]], "dataframes() (in module polars.testing.parametric)": [[124, "polars.testing.parametric.dataframes"]], "load_profile() (in module polars.testing.parametric)": [[125, "polars.testing.parametric.load_profile"]], "series() (in module polars.testing.parametric)": [[126, "polars.testing.parametric.series"]], "set_profile() (in module polars.testing.parametric)": [[127, "polars.testing.parametric.set_profile"]], "threadpool_size() (in module polars)": [[128, "polars.threadpool_size"]], "using_string_cache() (in module polars)": [[129, "polars.using_string_cache"]], "__dataframe__() (polars.dataframe method)": [[132, "polars.DataFrame.__dataframe__"]], "apply() (polars.dataframe method)": 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"groupby_rolling() (polars.dataframe method)": [[159, "polars.DataFrame.groupby_rolling"]], "hash_rows() (polars.dataframe method)": [[160, "polars.DataFrame.hash_rows"]], "head() (polars.dataframe method)": [[161, "polars.DataFrame.head"]], "height (polars.dataframe property)": [[162, "polars.DataFrame.height"]], "hstack() (polars.dataframe method)": [[163, "polars.DataFrame.hstack"]], "insert_at_idx() (polars.dataframe method)": [[164, "polars.DataFrame.insert_at_idx"]], "interpolate() (polars.dataframe method)": [[165, "polars.DataFrame.interpolate"]], "is_duplicated() (polars.dataframe method)": [[166, "polars.DataFrame.is_duplicated"]], "is_empty() (polars.dataframe method)": [[167, "polars.DataFrame.is_empty"]], "is_unique() (polars.dataframe method)": [[168, "polars.DataFrame.is_unique"]], "item() (polars.dataframe method)": [[169, "polars.DataFrame.item"]], "iter_rows() (polars.dataframe method)": [[170, "polars.DataFrame.iter_rows"]], "iter_slices() (polars.dataframe method)": 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module polars)": [[565, "polars.all_horizontal"]], "any() (in module polars)": [[566, "polars.any"]], "any_horizontal() (in module polars)": [[567, "polars.any_horizontal"]], "apply() (in module polars)": [[568, "polars.apply"]], "approx_unique() (in module polars)": [[569, "polars.approx_unique"]], "arange() (in module polars)": [[570, "polars.arange"]], "arctan2() (in module polars)": [[571, "polars.arctan2"]], "arctan2d() (in module polars)": [[572, "polars.arctan2d"]], "arg_sort_by() (in module polars)": [[573, "polars.arg_sort_by"]], "arg_where() (in module polars)": [[574, "polars.arg_where"]], "avg() (in module polars)": [[575, "polars.avg"]], "coalesce() (in module polars)": [[576, "polars.coalesce"]], "col() (in module polars)": [[577, "polars.col"]], "concat_list() (in module polars)": [[578, "polars.concat_list"]], "concat_str() (in module polars)": [[579, "polars.concat_str"]], "corr() (in module polars)": [[580, "polars.corr"]], "count() (in module polars)": [[581, "polars.count"]], "cov() (in module polars)": [[582, "polars.cov"]], "cumfold() (in module polars)": [[583, "polars.cumfold"]], "cumreduce() (in module polars)": [[584, "polars.cumreduce"]], "cumsum() (in module polars)": [[585, "polars.cumsum"]], "cumsum_horizontal() (in module polars)": [[586, "polars.cumsum_horizontal"]], "date() (in module polars)": [[587, "polars.date"]], "date_range() (in module polars)": [[588, "polars.date_range"]], "date_ranges() (in module polars)": [[589, "polars.date_ranges"]], "datetime() (in module polars)": [[590, "polars.datetime"]], "duration() (in module polars)": [[591, "polars.duration"]], "element() (in module polars)": [[592, "polars.element"]], "exclude() (in module polars)": [[593, "polars.exclude"]], "first() (in module polars)": [[594, "polars.first"]], "fold() (in module polars)": [[595, "polars.fold"]], "format() (in module polars)": [[596, "polars.format"]], "from_epoch() (in module polars)": [[597, "polars.from_epoch"]], "groups() (in module polars)": [[598, "polars.groups"]], "head() (in module polars)": [[599, "polars.head"]], "implode() (in module polars)": [[600, "polars.implode"]], "int_range() (in module polars)": [[601, "polars.int_range"]], "int_ranges() (in module polars)": [[602, "polars.int_ranges"]], "last() (in module polars)": [[603, "polars.last"]], "lit() (in module polars)": [[604, "polars.lit"]], "map() (in module polars)": [[605, "polars.map"]], "max() (in module polars)": [[606, "polars.max"]], "max_horizontal() (in module polars)": [[607, "polars.max_horizontal"]], "mean() (in module polars)": [[608, "polars.mean"]], "median() (in module polars)": [[609, "polars.median"]], "min() (in module polars)": [[610, "polars.min"]], "min_horizontal() (in module polars)": [[611, "polars.min_horizontal"]], "n_unique() (in module polars)": [[612, "polars.n_unique"]], "ones() (in module polars)": [[613, "polars.ones"]], "quantile() (in module polars)": [[614, "polars.quantile"]], "reduce() (in module polars)": [[615, "polars.reduce"]], "repeat() (in module polars)": [[616, "polars.repeat"]], "rolling_corr() (in module polars)": [[617, "polars.rolling_corr"]], "rolling_cov() (in module polars)": [[618, "polars.rolling_cov"]], "select() (in module polars)": [[619, "polars.select"]], "sql_expr() (in module polars)": [[620, "polars.sql_expr"]], "std() (in module polars)": [[621, "polars.std"]], "struct() (in module polars)": [[622, "polars.struct"]], "sum() (in module polars)": [[623, "polars.sum"]], "sum_horizontal() (in module polars)": [[624, "polars.sum_horizontal"]], "tail() (in module polars)": [[625, "polars.tail"]], "time() (in module polars)": [[626, "polars.time"]], "time_range() (in module polars)": [[627, "polars.time_range"]], "time_ranges() (in module polars)": [[628, "polars.time_ranges"]], "var() (in module polars)": [[629, "polars.var"]], "when() (in module polars)": [[630, "polars.when"]], "zeros() (in module polars)": [[631, "polars.zeros"]], "bottom_k() (polars.lazyframe method)": [[653, "polars.LazyFrame.bottom_k"]], "cache() (polars.lazyframe method)": [[654, "polars.LazyFrame.cache"]], "clear() (polars.lazyframe method)": [[655, "polars.LazyFrame.clear"]], "clone() (polars.lazyframe method)": [[656, "polars.LazyFrame.clone"]], "collect() (polars.lazyframe method)": [[657, "polars.LazyFrame.collect"]], "columns (polars.lazyframe property)": [[658, "polars.LazyFrame.columns"]], "drop() (polars.lazyframe method)": [[659, "polars.LazyFrame.drop"]], "drop_nulls() (polars.lazyframe method)": [[660, "polars.LazyFrame.drop_nulls"]], "dtypes (polars.lazyframe property)": [[661, "polars.LazyFrame.dtypes"]], "explain() (polars.lazyframe method)": [[662, "polars.LazyFrame.explain"]], "explode() (polars.lazyframe method)": [[663, "polars.LazyFrame.explode"]], "fetch() (polars.lazyframe method)": [[664, "polars.LazyFrame.fetch"]], "fill_nan() (polars.lazyframe method)": [[665, "polars.LazyFrame.fill_nan"]], "fill_null() (polars.lazyframe method)": [[666, "polars.LazyFrame.fill_null"]], "filter() (polars.lazyframe method)": [[667, "polars.LazyFrame.filter"]], "first() (polars.lazyframe method)": [[668, "polars.LazyFrame.first"]], "from_json() (polars.lazyframe class method)": [[669, "polars.LazyFrame.from_json"]], "groupby() (polars.lazyframe method)": [[670, "polars.LazyFrame.groupby"]], "groupby_dynamic() (polars.lazyframe method)": [[671, "polars.LazyFrame.groupby_dynamic"]], "groupby_rolling() (polars.lazyframe method)": [[672, "polars.LazyFrame.groupby_rolling"]], "head() (polars.lazyframe method)": [[673, "polars.LazyFrame.head"]], "inspect() (polars.lazyframe method)": [[674, "polars.LazyFrame.inspect"]], "interpolate() (polars.lazyframe method)": [[675, "polars.LazyFrame.interpolate"]], "join() (polars.lazyframe method)": [[676, "polars.LazyFrame.join"]], "join_asof() (polars.lazyframe method)": [[677, "polars.LazyFrame.join_asof"]], "last() (polars.lazyframe method)": [[678, "polars.LazyFrame.last"]], "lazy() (polars.lazyframe method)": [[679, "polars.LazyFrame.lazy"]], "limit() (polars.lazyframe method)": [[680, "polars.LazyFrame.limit"]], "map() (polars.lazyframe method)": [[681, "polars.LazyFrame.map"]], "max() (polars.lazyframe method)": [[682, "polars.LazyFrame.max"]], "mean() (polars.lazyframe method)": [[683, "polars.LazyFrame.mean"]], "median() (polars.lazyframe method)": [[684, "polars.LazyFrame.median"]], "melt() (polars.lazyframe method)": [[685, "polars.LazyFrame.melt"]], "merge_sorted() (polars.lazyframe method)": [[686, "polars.LazyFrame.merge_sorted"]], "min() (polars.lazyframe method)": [[687, "polars.LazyFrame.min"]], "null_count() (polars.lazyframe method)": [[688, "polars.LazyFrame.null_count"]], "pipe() (polars.lazyframe method)": [[689, "polars.LazyFrame.pipe"]], "profile() (polars.lazyframe method)": [[690, "polars.LazyFrame.profile"]], "quantile() (polars.lazyframe method)": [[691, "polars.LazyFrame.quantile"]], "read_json() (polars.lazyframe class method)": [[692, "polars.LazyFrame.read_json"]], "rename() (polars.lazyframe method)": [[693, "polars.LazyFrame.rename"]], "reverse() (polars.lazyframe method)": [[694, "polars.LazyFrame.reverse"]], "schema (polars.lazyframe property)": [[695, "polars.LazyFrame.schema"]], "select() (polars.lazyframe method)": [[696, "polars.LazyFrame.select"]], "set_sorted() (polars.lazyframe method)": [[697, "polars.LazyFrame.set_sorted"]], "shift() (polars.lazyframe method)": [[698, "polars.LazyFrame.shift"]], "shift_and_fill() (polars.lazyframe method)": [[699, "polars.LazyFrame.shift_and_fill"]], "show_graph() (polars.lazyframe method)": [[700, "polars.LazyFrame.show_graph"]], "slice() (polars.lazyframe method)": [[701, "polars.LazyFrame.slice"]], "sort() (polars.lazyframe method)": [[702, "polars.LazyFrame.sort"]], "std() (polars.lazyframe method)": [[703, "polars.LazyFrame.std"]], "sum() (polars.lazyframe method)": [[704, "polars.LazyFrame.sum"]], "tail() (polars.lazyframe method)": [[705, "polars.LazyFrame.tail"]], "take_every() (polars.lazyframe method)": [[706, "polars.LazyFrame.take_every"]], "top_k() (polars.lazyframe method)": [[707, "polars.LazyFrame.top_k"]], "unique() (polars.lazyframe method)": [[708, "polars.LazyFrame.unique"]], "unnest() (polars.lazyframe method)": [[709, "polars.LazyFrame.unnest"]], "update() (polars.lazyframe method)": [[710, "polars.LazyFrame.update"]], "var() (polars.lazyframe method)": [[711, "polars.LazyFrame.var"]], "width (polars.lazyframe property)": [[712, "polars.LazyFrame.width"]], "with_columns() (polars.lazyframe method)": [[713, "polars.LazyFrame.with_columns"]], "with_context() (polars.lazyframe method)": [[714, "polars.LazyFrame.with_context"]], "with_row_count() (polars.lazyframe method)": [[715, "polars.LazyFrame.with_row_count"]], "write_json() (polars.lazyframe method)": [[716, "polars.LazyFrame.write_json"]], "agg() (polars.lazyframe.groupby.lazygroupby method)": [[717, "polars.lazyframe.groupby.LazyGroupBy.agg"]], "all() (polars.lazyframe.groupby.lazygroupby method)": [[718, "polars.lazyframe.groupby.LazyGroupBy.all"]], "apply() (polars.lazyframe.groupby.lazygroupby method)": [[719, "polars.lazyframe.groupby.LazyGroupBy.apply"]], "count() (polars.lazyframe.groupby.lazygroupby method)": [[720, "polars.lazyframe.groupby.LazyGroupBy.count"]], "first() (polars.lazyframe.groupby.lazygroupby method)": [[721, "polars.lazyframe.groupby.LazyGroupBy.first"]], "head() (polars.lazyframe.groupby.lazygroupby method)": [[722, "polars.lazyframe.groupby.LazyGroupBy.head"]], "last() (polars.lazyframe.groupby.lazygroupby method)": [[723, "polars.lazyframe.groupby.LazyGroupBy.last"]], "max() (polars.lazyframe.groupby.lazygroupby method)": [[724, "polars.lazyframe.groupby.LazyGroupBy.max"]], "mean() (polars.lazyframe.groupby.lazygroupby method)": [[725, "polars.lazyframe.groupby.LazyGroupBy.mean"]], "median() (polars.lazyframe.groupby.lazygroupby method)": [[726, "polars.lazyframe.groupby.LazyGroupBy.median"]], 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930, 931, 932, 933, 934, 937, 938, 939, 940, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 959, 960, 961, 962, 963, 966, 967, 971, 972, 973, 974, 980, 981, 982, 983, 984, 985, 987, 988, 989, 990, 991, 994, 996, 997, 1000, 1002, 1003, 1004, 1014, 1020, 1021, 1022, 1023, 1024, 1025, 1032, 1033, 1034, 1037, 1039, 1041, 1042, 1051, 1059], "within": [2, 49, 119, 120, 157, 158, 159, 173, 227, 254, 474, 506, 639, 671, 672, 677, 735, 1058], "exampl": [2, 8, 9, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 67, 68, 69, 70, 71, 74, 75, 90, 92, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 112, 113, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202, 203, 204, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 254, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 588, 589, 591, 592, 593, 594, 595, 596, 597, 599, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 615, 616, 619, 620, 621, 622, 623, 624, 625, 627, 628, 629, 630, 631, 639, 650, 653, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 693, 694, 695, 696, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 735, 741, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 766, 767, 769, 770, 772, 773, 774, 775, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 839, 840, 841, 842, 843, 846, 847, 848, 849, 850, 852, 855, 856, 857, 860, 861, 862, 863, 864, 865, 867, 868, 869, 870, 871, 872, 873, 874, 875, 877, 878, 879, 880, 882, 885, 886, 892, 893, 894, 897, 898, 900, 905, 906, 908, 909, 910, 911, 913, 915, 920, 921, 922, 923, 924, 925, 926, 927, 928, 934, 935, 936, 938, 939, 940, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 961, 962, 963, 964, 966, 970, 971, 972, 973, 975, 976, 978, 980, 981, 982, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1051, 1058], "dtype": [2, 31, 38, 70, 74, 75, 90, 92, 94, 96, 97, 101, 102, 108, 109, 112, 121, 122, 123, 124, 126, 132, 158, 159, 173, 216, 217, 218, 254, 268, 293, 294, 298, 299, 300, 307, 308, 355, 363, 405, 430, 436, 438, 440, 476, 477, 483, 484, 485, 486, 487, 489, 490, 491, 498, 518, 535, 548, 550, 554, 559, 568, 570, 581, 593, 597, 601, 602, 604, 605, 613, 616, 622, 631, 639, 671, 672, 677, 735, 738, 757, 758, 767, 769, 774, 775, 776, 777, 783, 784, 787, 788, 836, 840, 864, 870, 877, 889, 914, 920, 921, 945, 961, 968, 989, 1006, 1020, 1030, 1032, 1033, 1034, 1038, 1051, 1059], "method": [2, 3, 4, 5, 8, 27, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 74, 91, 101, 102, 105, 121, 130, 133, 142, 146, 157, 165, 170, 183, 189, 195, 196, 197, 223, 231, 236, 246, 254, 261, 264, 265, 268, 292, 348, 351, 358, 359, 366, 373, 376, 377, 382, 395, 398, 437, 455, 457, 461, 462, 464, 468, 469, 472, 474, 483, 484, 485, 486, 487, 489, 490, 491, 538, 546, 547, 556, 558, 563, 568, 580, 588, 614, 616, 627, 632, 633, 635, 639, 640, 641, 644, 645, 646, 647, 650, 660, 675, 691, 708, 713, 719, 729, 735, 744, 745, 829, 832, 846, 847, 860, 939, 940, 953, 1009, 1034, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057], "attribut": [2, 3, 4, 5, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 59, 60, 61, 62, 63, 64, 65, 66, 121, 254, 632, 633, 635, 640, 641, 645, 646, 647, 735, 1043, 1045, 1047, 1052, 1055, 1056, 1057], "A": [5, 28, 31, 52, 55, 73, 97, 101, 102, 103, 110, 112, 128, 152, 158, 173, 187, 213, 225, 226, 227, 231, 254, 260, 310, 318, 358, 359, 366, 376, 377, 385, 388, 389, 391, 398, 431, 437, 438, 449, 461, 462, 479, 482, 483, 484, 485, 486, 487, 489, 490, 491, 510, 511, 516, 517, 519, 525, 526, 543, 578, 592, 596, 639, 671, 677, 693, 710, 713, 735, 738, 786, 795, 847, 948, 949, 950, 952, 955, 956, 957, 981, 982, 987, 988, 990, 996, 997, 1014, 1051, 1058], "encod": [5, 66, 101, 102, 112, 215, 254, 286, 287, 289, 290, 375, 512, 639, 762, 983], "set": [5, 6, 7, 8, 9, 10, 12, 14, 15, 18, 23, 24, 26, 28, 30, 31, 32, 33, 34, 47, 48, 67, 68, 91, 93, 96, 101, 102, 105, 106, 110, 112, 114, 115, 116, 119, 120, 122, 123, 124, 125, 126, 127, 128, 130, 132, 137, 142, 151, 157, 158, 159, 160, 170, 173, 179, 198, 200, 206, 215, 217, 222, 223, 225, 231, 254, 268, 292, 355, 378, 387, 421, 422, 423, 424, 426, 430, 431, 440, 467, 471, 482, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 503, 504, 522, 528, 529, 534, 535, 536, 537, 540, 570, 574, 588, 589, 601, 602, 613, 616, 617, 618, 622, 627, 628, 630, 631, 639, 650, 660, 662, 670, 671, 672, 676, 677, 681, 685, 696, 701, 708, 713, 716, 719, 735, 744, 745, 836, 855, 868, 905, 906, 907, 908, 910, 914, 915, 921, 948, 949, 950, 951, 952, 953, 955, 956, 957, 959, 963, 970, 974, 975, 993, 999, 1000, 1005, 1006, 1007, 1008, 1011, 1032, 1051, 1059], "string": [5, 7, 9, 12, 13, 14, 18, 28, 29, 31, 33, 34, 38, 52, 58, 66, 75, 97, 101, 102, 103, 112, 121, 122, 123, 126, 129, 134, 152, 156, 157, 158, 159, 173, 187, 197, 200, 207, 216, 221, 222, 225, 227, 231, 234, 254, 261, 289, 295, 341, 345, 348, 351, 352, 365, 371, 375, 383, 414, 440, 451, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 509, 510, 513, 514, 515, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 543, 564, 565, 566, 567, 573, 576, 578, 579, 585, 586, 588, 589, 596, 606, 607, 610, 611, 619, 622, 623, 624, 627, 628, 630, 639, 653, 662, 669, 670, 671, 672, 677, 696, 702, 707, 713, 716, 717, 735, 738, 764, 770, 822, 826, 829, 832, 833, 845, 861, 898, 979, 980, 981, 985, 986, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1010, 1012, 1013, 1014, 1030, 1051, 1059], "classmethod": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 375, 639, 669, 692, 735], "activ": [6, 10, 16, 17, 19, 20, 21, 22, 25, 409, 517, 893, 988], "bool": [6, 10, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 30, 31, 33, 35, 47, 48, 67, 73, 74, 75, 90, 91, 95, 97, 101, 102, 106, 109, 110, 112, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, 129, 132, 134, 135, 136, 148, 149, 151, 152, 153, 155, 156, 157, 158, 159, 163, 164, 166, 167, 168, 173, 185, 187, 197, 198, 201, 205, 207, 213, 215, 217, 218, 221, 222, 223, 224, 227, 229, 231, 238, 240, 241, 244, 254, 263, 264, 265, 266, 267, 268, 278, 284, 286, 287, 289, 290, 293, 304, 305, 306, 307, 308, 309, 310, 328, 344, 346, 352, 357, 358, 359, 360, 361, 362, 376, 377, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 398, 401, 402, 406, 409, 427, 430, 432, 437, 438, 444, 445, 446, 447, 451, 461, 462, 464, 471, 474, 476, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 493, 495, 498, 499, 503, 505, 506, 510, 512, 514, 524, 525, 526, 530, 531, 533, 535, 536, 537, 540, 545, 557, 560, 563, 564, 565, 566, 567, 568, 570, 573, 574, 580, 583, 588, 589, 601, 602, 604, 613, 616, 622, 627, 628, 631, 639, 653, 655, 657, 662, 664, 666, 670, 671, 672, 676, 677, 681, 685, 690, 697, 700, 702, 707, 708, 709, 713, 721, 723, 724, 727, 735, 738, 742, 743, 744, 745, 754, 760, 762, 767, 774, 781, 782, 783, 784, 785, 786, 805, 825, 827, 833, 839, 841, 842, 843, 850, 854, 861, 862, 863, 864, 865, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 881, 885, 886, 890, 891, 893, 911, 914, 916, 935, 936, 938, 940, 941, 942, 943, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 959, 961, 963, 964, 969, 974, 976, 981, 983, 985, 995, 996, 997, 1001, 1002, 1004, 1006, 1007, 1008, 1011, 1031, 1032, 1033, 1036, 1039, 1041, 1051, 1058], "decim": [6, 28, 31, 254, 492, 538, 639, 958, 1009, 1051], "temporari": 6, "remov": [6, 8, 140, 215, 226, 254, 268, 363, 439, 522, 528, 534, 535, 537, 593, 639, 659, 710, 735, 744, 993, 999, 1005, 1006, 1008, 1051], "later": [6, 588], "onc": [6, 55, 101, 102, 105, 128, 132, 133, 196, 198, 234, 254, 268, 292, 493, 639, 654, 717, 735, 745, 959, 1051], "stabil": 6, "happen": [6, 471, 639, 938, 1051], "being": [6, 101, 102, 112, 117, 215, 225, 226, 254, 268, 309, 345, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 592, 639, 710, 735, 785, 826, 857, 938, 1051, 1059], "consid": [6, 101, 102, 112, 117, 133, 142, 153, 179, 196, 223, 225, 226, 254, 268, 298, 299, 300, 309, 345, 438, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 639, 660, 673, 680, 685, 708, 710, 719, 735, 745, 775, 776, 777, 785, 826, 857, 870, 938, 961, 962, 963, 1051], "break": [6, 117, 225, 226, 254, 268, 309, 310, 345, 471, 483, 484, 485, 486, 487, 489, 490, 491, 639, 710, 735, 785, 786, 826, 857, 938, 1051], "chang": [6, 26, 67, 101, 102, 117, 225, 226, 227, 254, 268, 309, 312, 345, 466, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 639, 710, 735, 744, 785, 826, 857, 934, 938, 969, 1051], "current": [6, 9, 26, 54, 91, 97, 103, 129, 132, 135, 136, 172, 254, 324, 345, 431, 466, 639, 650, 655, 656, 676, 735, 738, 774, 778, 793, 801, 826, 934, 1051, 1058], "alpha": [6, 73, 268, 360, 361, 362, 639, 657, 662, 664, 690, 700, 735, 841, 842, 843, 1051], "state": [6, 8, 73, 83, 129, 583, 650, 657, 662, 664, 690, 700, 735], "cfg": [7, 8, 9, 15, 130], "path": [7, 9, 27, 28, 29, 30, 31, 32, 33, 34, 35, 47, 48, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 254, 453, 495, 519, 639, 692, 700, 716, 735, 964, 990, 1051], "previous": 7, "save": [7, 158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 650, 671, 672, 677, 735, 801, 822, 826, 833], "share": [7, 58, 144, 254, 840, 1051], "option": [7, 8, 9, 26, 30, 31, 39, 67, 101, 104, 105, 106, 110, 113, 114, 116, 118, 121, 122, 123, 124, 125, 126, 127, 169, 173, 179, 213, 217, 222, 254, 396, 482, 483, 484, 485, 486, 487, 489, 490, 491, 503, 529, 604, 622, 630, 639, 650, 676, 677, 685, 735, 738, 877, 881, 948, 949, 950, 951, 952, 953, 955, 956, 957, 974, 1000, 1029, 1051], "from": [7, 8, 28, 31, 52, 53, 54, 67, 74, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 112, 113, 114, 115, 116, 119, 120, 121, 122, 123, 124, 125, 126, 127, 139, 140, 144, 146, 151, 156, 158, 159, 170, 171, 173, 174, 179, 187, 191, 195, 197, 198, 200, 215, 222, 223, 225, 227, 254, 304, 311, 316, 317, 318, 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 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487, 488, 489, 490, 491, 494, 500, 501, 502, 507, 508, 531, 532, 538, 552, 553, 555, 561, 580, 617, 618, 621, 629, 639, 653, 702, 703, 707, 711, 735, 745, 746, 747, 748, 749, 750, 751, 766, 771, 774, 779, 780, 781, 782, 783, 784, 785, 844, 845, 850, 856, 868, 880, 883, 891, 893, 894, 905, 906, 907, 908, 918, 919, 934, 940, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 960, 971, 972, 973, 977, 978, 1002, 1003, 1009, 1021, 1024, 1025, 1030, 1035, 1036, 1040, 1051], "too": [13, 158, 159, 173, 227, 254, 341, 345, 352, 481, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "optimist": 13, "lead": [13, 35, 97, 117, 158, 159, 254, 268, 438, 495, 522, 534, 543, 550, 560, 580, 639, 671, 672, 681, 719, 735, 745, 964, 993, 1005, 1014, 1051], "out": [13, 47, 48, 97, 118, 159, 197, 254, 287, 412, 430, 510, 512, 630, 672, 735, 762, 896, 914, 981, 983], "memori": [13, 48, 74, 90, 91, 94, 95, 96, 101, 102, 106, 110, 112, 114, 115, 116, 132, 133, 146, 197, 205, 236, 254, 465, 475, 498, 639, 735, 744, 745, 846, 941, 968, 969, 1051], "error": [13, 30, 76, 101, 102, 112, 158, 159, 173, 187, 195, 227, 254, 263, 287, 293, 341, 345, 352, 395, 414, 430, 483, 484, 485, 486, 487, 489, 490, 491, 510, 512, 516, 518, 519, 535, 536, 537, 540, 639, 671, 672, 677, 719, 735, 762, 767, 822, 826, 833, 898, 914, 981, 983, 987, 989, 990, 1006, 1007, 1008, 1011, 1051], "row": [13, 18, 23, 28, 31, 33, 35, 48, 67, 68, 70, 74, 82, 84, 89, 93, 94, 96, 97, 101, 102, 105, 106, 110, 112, 114, 115, 116, 119, 122, 124, 133, 134, 135, 142, 146, 149, 152, 156, 157, 158, 160, 161, 166, 168, 169, 170, 171, 173, 174, 175, 179, 183, 197, 198, 206, 210, 211, 214, 216, 221, 223, 225, 226, 232, 236, 239, 248, 254, 268, 279, 365, 379, 400, 410, 431, 465, 466, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 504, 506, 515, 518, 549, 570, 573, 583, 584, 595, 599, 601, 602, 615, 617, 618, 625, 630, 639, 653, 655, 657, 660, 664, 667, 668, 671, 673, 677, 678, 680, 681, 685, 701, 705, 706, 707, 708, 710, 715, 719, 722, 731, 735, 744, 845, 846, 856, 880, 894, 934, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 975, 986, 989, 1021, 1051, 1059], "per": [13, 28, 31, 122, 124, 134, 156, 183, 184, 207, 221, 243, 245, 246, 254, 268, 292, 409, 412, 430, 506, 573, 639, 653, 702, 707, 726, 728, 729, 735, 893, 896, 914], "everi": [13, 101, 102, 112, 158, 211, 214, 227, 254, 304, 305, 306, 307, 308, 309, 345, 352, 365, 403, 404, 408, 410, 412, 413, 421, 426, 429, 515, 551, 583, 584, 639, 664, 671, 706, 735, 781, 782, 783, 784, 785, 826, 833, 845, 887, 888, 892, 894, 896, 897, 905, 910, 913, 986, 1023, 1051], "process": [13, 28, 47, 48, 128, 254, 735], "liter": [14, 18, 30, 52, 105, 124, 125, 126, 127, 156, 170, 176, 177, 181, 182, 185, 195, 196, 200, 209, 213, 226, 231, 254, 263, 286, 318, 358, 359, 366, 373, 376, 377, 383, 398, 437, 440, 455, 457, 461, 462, 468, 510, 525, 526, 546, 556, 564, 565, 566, 567, 570, 574, 576, 578, 579, 585, 586, 587, 588, 589, 590, 601, 602, 604, 606, 607, 610, 611, 613, 616, 619, 622, 623, 624, 626, 627, 628, 631, 639, 666, 696, 710, 713, 735, 738, 761, 795, 847, 861, 962, 963, 981, 996, 997, 1051, 1058], "left": [14, 31, 54, 67, 119, 120, 158, 159, 172, 173, 226, 254, 360, 361, 362, 382, 383, 483, 484, 485, 486, 487, 489, 490, 491, 494, 521, 543, 554, 576, 583, 584, 588, 589, 595, 615, 627, 628, 630, 639, 671, 672, 676, 677, 710, 735, 841, 842, 843, 861, 960, 992, 1014, 1034, 1051], "center": [14, 31, 254, 360, 361, 362, 482, 483, 484, 485, 486, 487, 489, 490, 491, 639, 841, 842, 843, 948, 949, 950, 951, 952, 953, 955, 956, 957, 1051], "right": [14, 16, 31, 101, 102, 119, 120, 158, 159, 172, 173, 254, 310, 360, 361, 362, 383, 421, 422, 423, 424, 471, 483, 484, 485, 486, 487, 489, 490, 491, 494, 503, 527, 576, 588, 589, 627, 628, 639, 671, 672, 676, 677, 735, 786, 841, 842, 843, 861, 905, 906, 907, 908, 938, 960, 974, 998, 1051], "cell": [14, 31, 254], "align": [14, 31, 67, 74, 254, 543, 1014], "keyerror": [14, 18], "recognis": [14, 18, 121], "column_abc": 14, "column_xyz": 14, "visibl": [15, 144, 254, 840, 1051], "eg": [15, 23, 31, 103, 254, 345, 535, 537, 556, 639, 1006, 1008], "low": [15, 128], "rang": [15, 31, 103, 139, 144, 158, 171, 254, 310, 311, 322, 336, 342, 343, 345, 352, 353, 382, 471, 570, 578, 587, 588, 589, 590, 601, 602, 626, 627, 628, 639, 671, 735, 786, 787, 799, 817, 823, 824, 826, 833, 834, 840, 934, 938, 1051], "100": [15, 31, 93, 96, 101, 102, 112, 115, 254, 518, 538, 543, 735, 949, 950, 952, 989, 1009, 1051, 1059], "98": [15, 164, 254, 291, 505, 538, 550, 555, 639, 1009], "99": [15, 31, 147, 148, 164, 167, 254, 262, 291, 366, 368, 505, 550, 555, 639, 665, 666, 735, 839, 847, 1051], "tbl_col": 15, "10": [15, 27, 28, 30, 31, 32, 35, 48, 52, 67, 74, 97, 103, 112, 124, 126, 136, 146, 155, 158, 159, 161, 163, 164, 165, 182, 186, 188, 192, 193, 200, 210, 231, 234, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 263, 267, 276, 277, 278, 280, 298, 303, 305, 308, 310, 312, 314, 315, 316, 318, 324, 337, 338, 344, 345, 352, 355, 378, 379, 382, 387, 395, 399, 400, 408, 413, 425, 426, 429, 434, 466, 467, 471, 504, 543, 549, 563, 576, 583, 588, 592, 595, 597, 599, 604, 605, 625, 628, 639, 657, 671, 672, 673, 675, 680, 689, 690, 696, 704, 705, 713, 717, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 738, 745, 775, 786, 788, 793, 795, 801, 825, 833, 836, 839, 856, 868, 883, 892, 897, 909, 910, 913, 918, 934, 962, 963, 1021, 1051, 1059], "95": [15, 262, 639], "96": [15, 262, 639], "97": [15, 164, 254, 262, 639], "move": [16, 197, 254, 360, 361, 362, 483, 484, 486, 490, 639, 841, 842, 843, 949, 950, 952, 956, 1051], "inlin": [16, 197, 254, 510, 516, 517, 525, 981, 987, 988, 996], "parenthes": 16, "below": [17, 31, 104, 113, 142, 254, 368, 588, 589, 630, 639, 660, 735], "ascii_ful": 18, "ascii_full_condens": 18, "ascii_no_bord": 18, "ascii_borders_onli": 18, "ascii_borders_only_condens": 18, "ascii_horizontal_onli": 18, "ascii_markdown": 18, "utf8_ful": [18, 67, 97], "utf8_full_condens": [18, 97], "utf8_no_bord": 18, "utf8_borders_onli": 18, "utf8_horizontal_onli": 18, "noth": [18, 292, 511, 516, 519, 639, 982, 987, 990], "rounded_corn": 18, "style": [18, 31, 187, 254], "border": 18, "line": [18, 31, 101, 102, 105, 112, 156, 166, 168, 254, 516, 987], "includ": [18, 26, 28, 30, 31, 72, 104, 113, 124, 134, 139, 144, 158, 185, 197, 221, 222, 225, 254, 310, 346, 383, 471, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 528, 530, 531, 534, 583, 617, 618, 639, 653, 671, 707, 735, 786, 787, 827, 840, 861, 938, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1001, 1002, 1005, 1041, 1051], "divid": [18, 345, 352, 360, 361, 362, 396, 639, 826, 833, 841, 842, 843, 881, 1051], "dens": [18, 156, 254, 474, 639, 940, 1051], "space": [18, 158, 254, 471, 639, 671, 735, 938, 1051], "horizont": [18, 74, 152, 163, 225, 254, 564, 565, 566, 567, 578, 579, 583, 584, 585, 586, 592, 595, 606, 607, 610, 611, 615, 623, 624], "markdown": 18, "compat": [18, 31, 35, 48, 254, 510, 511, 516, 517, 525, 526, 735, 738, 981, 982, 987, 988, 996, 997], "No": [18, 541, 1012], "appli": [18, 28, 31, 52, 74, 112, 121, 124, 126, 152, 186, 254, 320, 321, 322, 326, 328, 329, 330, 332, 334, 336, 339, 342, 343, 346, 349, 353, 354, 356, 361, 362, 438, 467, 482, 483, 484, 486, 490, 525, 535, 536, 537, 540, 563, 583, 584, 595, 605, 615, 630, 639, 673, 680, 681, 689, 735, 797, 798, 799, 803, 805, 806, 810, 812, 815, 817, 820, 823, 824, 827, 830, 834, 835, 837, 842, 843, 948, 949, 950, 952, 956, 996, 1006, 1007, 1008, 1011, 1051], "round": [18, 31, 69, 97, 254, 297, 372, 552, 639, 772, 852, 1051], "corner": [18, 31, 97, 254], "op": [18, 126, 254, 477, 535, 537, 639, 735, 1006, 1008, 1051], "one": [18, 29, 31, 57, 67, 82, 90, 92, 93, 94, 95, 96, 108, 109, 125, 126, 127, 130, 148, 149, 156, 157, 158, 159, 170, 179, 187, 195, 196, 201, 217, 220, 235, 254, 262, 368, 431, 435, 482, 505, 517, 550, 588, 620, 623, 630, 639, 666, 667, 670, 671, 672, 685, 697, 714, 718, 735, 744, 849, 877, 915, 919, 948, 988, 1015, 1051], "more": [18, 31, 33, 35, 48, 57, 67, 74, 89, 90, 91, 92, 93, 94, 95, 96, 103, 104, 108, 109, 110, 113, 124, 126, 130, 133, 138, 139, 170, 174, 179, 183, 195, 196, 198, 217, 223, 236, 254, 265, 268, 284, 298, 299, 300, 396, 432, 435, 438, 464, 493, 503, 517, 535, 536, 537, 557, 620, 623, 630, 639, 685, 708, 735, 738, 745, 760, 775, 776, 777, 787, 877, 881, 916, 959, 974, 988, 1006, 1007, 1008, 1036, 1051, 1059], "semigraph": 18, "box": [18, 133, 254], "draw": [18, 23, 24, 123, 493, 499, 639, 1059], "found": [18, 28, 54, 77, 86, 88, 93, 97, 143, 226, 254, 494, 519, 535, 537, 639, 710, 735, 960, 990, 1006, 1008, 1051, 1058], "unicod": 18, "block": [18, 157, 223, 254, 670, 693, 708, 715, 719, 735, 962, 963, 1051], "http": [18, 31, 91, 103, 132, 138, 254, 516, 987], "en": [18, 31, 254], "wikipedia": 18, "org": [18, 91, 103, 132, 138, 254], "wiki": 18, "drawing_charact": 18, "box_draw": 18, "mno": 18, "tbl_format": 18, "tbl_hide_column_data_typ": 18, "tbl_hide_dataframe_shap": 18, "hide": [19, 20, 21, 22, 31, 254], "etc": [19, 30, 31, 101, 104, 106, 110, 113, 114, 116, 254, 738, 962, 963, 1051], "inform": [21, 72, 104, 113, 138, 254, 298, 299, 300, 396, 503, 510, 516, 517, 525, 588, 589, 639, 690, 735, 775, 776, 777, 881, 974, 981, 987, 988, 996, 1051], "separ": [22, 28, 99, 101, 102, 112, 185, 187, 215, 222, 224, 254, 268, 410, 414, 515, 579, 583, 584, 639, 709, 735, 894, 898, 986, 1019, 1028, 1051], "between": [22, 74, 121, 122, 124, 126, 138, 189, 246, 254, 293, 313, 383, 414, 421, 422, 423, 424, 466, 471, 472, 487, 493, 499, 509, 571, 572, 580, 582, 614, 617, 618, 639, 691, 729, 735, 767, 789, 861, 898, 905, 906, 907, 908, 934, 938, 939, 953, 980, 1051], "set_tbl_column_data_type_inlin": 22, "max": [23, 31, 35, 48, 52, 128, 139, 148, 157, 158, 159, 187, 254, 298, 299, 305, 368, 431, 465, 474, 483, 495, 532, 607, 620, 639, 666, 670, 671, 672, 735, 775, 776, 781, 787, 849, 915, 940, 949, 964, 1003, 1051], "both": [23, 28, 58, 158, 159, 172, 173, 180, 195, 254, 267, 383, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 649, 671, 672, 676, 677, 686, 735, 861, 870, 1051], "tbl_row": 23, "char": [24, 58, 75, 517, 523, 988, 994], "enabl": [25, 75, 129, 200, 231, 254, 495, 639, 696, 713, 735, 964, 1051], "addit": [25, 30, 31, 93, 104, 113, 122, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 261, 324, 363, 366, 465, 506, 510, 516, 517, 525, 564, 566, 573, 576, 577, 578, 579, 585, 593, 606, 610, 619, 622, 623, 639, 659, 663, 670, 696, 697, 702, 709, 713, 717, 735, 793, 801, 847, 981, 987, 988, 996, 1051], "verbos": [25, 130, 517, 988], "debug": [25, 657, 664, 681, 735, 1059], "log": [25, 69, 291, 357, 435, 457, 468, 555, 639, 766, 839, 1035, 1051], "if_set": 26, "env_onli": 26, "dict": [26, 30, 31, 35, 55, 68, 70, 72, 90, 92, 93, 94, 95, 96, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 124, 151, 170, 185, 191, 195, 196, 197, 199, 213, 214, 222, 254, 264, 266, 310, 440, 471, 481, 639, 693, 695, 735, 921, 1051], "show": [26, 31, 56, 142, 156, 174, 184, 254, 660, 690, 700, 735], "variabl": [26, 49, 54, 125, 128, 179, 215, 254, 685, 735, 1028, 1051, 1058], "restrict": [26, 532, 588, 589, 1003], "dictionari": [26, 31, 90, 92, 93, 94, 96, 101, 102, 107, 108, 109, 111, 112, 170, 185, 195, 196, 197, 213, 214, 254, 440, 639, 735, 921, 1051], "those": [26, 31, 101, 197, 254, 474, 516, 639, 738, 940, 987, 1051], "been": [26, 31, 254, 292, 474, 483, 484, 485, 486, 487, 489, 490, 491, 570, 639, 940, 1051], "set_fmt_float": 26, "directli": [26, 54, 124, 126, 130, 197, 254, 360, 361, 362, 616, 639, 735, 841, 842, 843, 1051, 1059], "via": [26, 101, 102, 105, 112, 114, 115, 116, 170, 196, 254, 268, 639], "set_stat": 26, "all_stat": 26, "binaryio": [27, 32, 100, 101, 105, 106, 107, 110, 111, 254], "bytesio": [27, 28, 31, 32, 35, 100, 101, 102, 105, 106, 110, 254], "compress": [27, 32, 35, 47, 48, 254, 735], "avrocompress": [27, 254], "uncompress": [27, 32, 35, 48, 106, 114, 254, 735], "write": [27, 28, 29, 30, 31, 32, 33, 35, 48, 102, 106, 130, 254, 298, 299, 300, 453, 639, 679, 700, 716, 735, 775, 776, 777, 1051], "apach": [27, 35, 100, 103, 254], "avro": [27, 100, 254, 650], "should": [27, 28, 29, 31, 32, 33, 34, 35, 47, 48, 74, 90, 92, 94, 96, 104, 108, 109, 112, 121, 122, 126, 132, 133, 134, 140, 158, 159, 170, 173, 195, 196, 197, 214, 215, 221, 224, 227, 236, 254, 262, 268, 295, 309, 310, 341, 345, 352, 355, 363, 389, 391, 409, 471, 482, 483, 484, 485, 486, 487, 489, 490, 491, 494, 503, 577, 593, 600, 604, 617, 618, 630, 639, 653, 659, 671, 672, 676, 677, 681, 700, 702, 707, 709, 716, 735, 738, 745, 770, 785, 786, 822, 826, 833, 836, 893, 938, 948, 949, 950, 951, 952, 953, 955, 956, 957, 960, 974, 1051], "snappi": [27, 35, 48, 254, 735], "deflat": [27, 254], "import": [27, 28, 31, 32, 35, 38, 67, 90, 94, 95, 112, 117, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 139, 156, 158, 171, 173, 217, 218, 225, 227, 254, 311, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 571, 572, 588, 589, 591, 627, 628, 639, 650, 671, 677, 681, 735, 779, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 865, 869, 871, 872, 877, 948, 972, 1024, 1051, 1059], "pathlib": [27, 28, 32, 35, 112, 254], "foo": [27, 28, 30, 32, 33, 34, 35, 56, 133, 137, 138, 140, 141, 142, 143, 146, 149, 150, 152, 153, 154, 155, 160, 161, 162, 163, 164, 165, 167, 172, 176, 177, 178, 181, 184, 187, 189, 191, 192, 193, 195, 198, 199, 200, 202, 203, 204, 206, 208, 209, 210, 212, 214, 215, 216, 217, 218, 219, 222, 223, 224, 228, 229, 230, 233, 254, 294, 298, 299, 300, 341, 379, 381, 400, 406, 411, 412, 415, 416, 478, 496, 497, 509, 511, 513, 515, 517, 522, 528, 530, 532, 534, 539, 542, 549, 551, 569, 575, 577, 580, 581, 582, 594, 598, 599, 603, 606, 608, 609, 610, 612, 614, 619, 621, 625, 629, 630, 639, 658, 659, 660, 661, 667, 674, 675, 676, 688, 693, 695, 696, 708, 709, 712, 714, 716, 735, 738, 769, 775, 822, 898, 944, 982, 984, 986, 988, 1003, 1010, 1013, 1051], "bar": [27, 28, 30, 32, 33, 34, 35, 56, 133, 137, 138, 140, 141, 142, 143, 146, 149, 150, 152, 153, 154, 155, 161, 163, 164, 165, 167, 172, 176, 177, 178, 181, 184, 187, 189, 191, 192, 193, 195, 198, 199, 200, 203, 204, 206, 208, 209, 210, 212, 214, 215, 216, 217, 218, 219, 223, 224, 228, 229, 233, 254, 294, 381, 416, 503, 513, 515, 530, 532, 569, 575, 577, 580, 581, 582, 594, 599, 603, 606, 608, 609, 610, 612, 619, 621, 625, 629, 630, 639, 658, 659, 660, 661, 667, 674, 675, 676, 688, 693, 695, 696, 708, 709, 712, 716, 735, 738, 769, 898, 974, 984, 986, 1003, 1051], "ham": [27, 28, 30, 32, 35, 137, 138, 140, 141, 142, 143, 149, 150, 153, 160, 161, 163, 172, 176, 177, 178, 181, 184, 189, 191, 193, 195, 198, 199, 200, 203, 204, 206, 208, 209, 210, 215, 216, 217, 218, 219, 223, 228, 229, 254, 294, 577, 658, 659, 660, 661, 667, 676, 688, 693, 695, 696, 708, 714, 735, 769], "d": [27, 28, 30, 31, 32, 35, 58, 75, 93, 117, 139, 156, 158, 160, 161, 164, 172, 210, 212, 225, 229, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 261, 263, 325, 348, 351, 383, 467, 498, 511, 517, 531, 535, 536, 537, 576, 597, 639, 671, 676, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 802, 829, 832, 861, 982, 988, 1002, 1006, 1007, 1008, 1051], "e": [27, 28, 30, 32, 35, 58, 75, 91, 101, 102, 105, 106, 110, 114, 116, 139, 156, 158, 159, 161, 173, 210, 212, 217, 225, 227, 254, 261, 268, 341, 345, 352, 357, 363, 383, 433, 440, 483, 484, 485, 486, 487, 489, 490, 491, 498, 503, 593, 630, 639, 671, 672, 677, 679, 693, 735, 738, 822, 826, 833, 839, 861, 961, 974, 1032, 1051], "dirpath": [27, 28, 32, 35, 112, 254], "new_fil": [27, 28, 32, 35, 254], "has_head": [28, 31, 101, 102, 105, 112, 254], "quot": [28, 29, 101, 102, 112, 254], "batch_siz": [28, 101, 102, 115, 254], "1024": [28, 48, 101, 102, 115, 254, 735], "datetime_format": [28, 254], "date_format": [28, 254], "time_format": [28, 254], "float_precis": [28, 31, 254], "null_valu": [28, 101, 102, 112, 254], "textiowrapp": [28, 254], "comma": [28, 254], "csv": [28, 47, 48, 101, 102, 105, 112, 254, 650, 735], "result": [28, 33, 34, 47, 48, 67, 74, 94, 96, 103, 104, 112, 114, 116, 117, 126, 146, 158, 159, 172, 183, 197, 204, 218, 227, 234, 254, 268, 310, 348, 351, 360, 361, 362, 366, 396, 431, 438, 465, 471, 478, 482, 483, 484, 485, 486, 487, 489, 490, 491, 495, 497, 530, 531, 537, 556, 570, 583, 584, 588, 589, 605, 613, 616, 617, 618, 631, 639, 654, 671, 672, 676, 681, 690, 699, 716, 717, 735, 744, 745, 829, 832, 841, 842, 843, 846, 881, 938, 944, 948, 949, 950, 951, 952, 953, 955, 956, 957, 964, 967, 1001, 1002, 1008, 1032, 1051, 1058, 1059], "If": [28, 29, 30, 31, 32, 33, 34, 35, 48, 52, 58, 72, 74, 90, 91, 92, 93, 94, 95, 96, 101, 102, 104, 105, 106, 108, 109, 110, 112, 113, 114, 115, 116, 125, 132, 133, 134, 142, 146, 156, 158, 159, 161, 169, 170, 173, 175, 179, 183, 196, 197, 198, 206, 210, 214, 215, 217, 221, 222, 223, 225, 226, 254, 264, 266, 268, 298, 299, 300, 310, 318, 337, 338, 352, 369, 396, 426, 431, 438, 440, 451, 465, 471, 474, 476, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 493, 494, 499, 503, 504, 518, 520, 522, 523, 528, 529, 530, 531, 532, 534, 535, 536, 537, 540, 564, 566, 568, 570, 574, 580, 581, 583, 585, 588, 589, 595, 601, 602, 604, 606, 610, 613, 616, 617, 618, 622, 623, 627, 628, 630, 631, 639, 653, 660, 662, 671, 672, 676, 677, 681, 685, 693, 701, 707, 708, 710, 716, 719, 735, 738, 744, 745, 775, 776, 777, 786, 795, 818, 819, 833, 846, 854, 856, 857, 880, 881, 883, 910, 915, 938, 940, 942, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 959, 960, 964, 970, 974, 975, 989, 991, 993, 994, 999, 1000, 1001, 1002, 1003, 1005, 1006, 1007, 1008, 1011, 1021, 1027, 1032, 1041, 1051, 1058], "instead": [28, 33, 34, 52, 56, 67, 74, 101, 110, 124, 133, 158, 159, 170, 173, 183, 185, 195, 196, 197, 200, 218, 227, 231, 254, 278, 310, 341, 345, 352, 471, 483, 484, 485, 486, 487, 489, 490, 491, 520, 522, 528, 534, 564, 566, 570, 574, 585, 588, 589, 601, 602, 606, 610, 613, 616, 622, 623, 627, 628, 631, 639, 665, 671, 672, 677, 696, 713, 716, 735, 738, 754, 786, 822, 826, 833, 938, 962, 963, 991, 993, 999, 1005, 1033, 1051, 1058], "whether": [28, 94, 96, 126, 134, 201, 221, 254, 310, 328, 346, 401, 402, 445, 446, 471, 495, 639, 653, 681, 697, 702, 707, 735, 738, 786, 805, 827, 885, 886, 938, 1051, 1058], "header": [28, 31, 35, 48, 97, 101, 102, 105, 112, 143, 187, 222, 254, 735], "field": [28, 59, 86, 88, 93, 217, 224, 254, 431, 440, 480, 517, 518, 531, 532, 545, 583, 584, 605, 622, 639, 709, 735, 786, 915, 938, 946, 1000, 1002, 1003, 1017, 1019, 1051], "symbol": [28, 254], "byte": [28, 48, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 144, 254, 286, 289, 290, 520, 523, 735, 761, 764, 765, 840, 991, 994, 1051], "specifi": [28, 31, 67, 77, 86, 88, 105, 124, 134, 140, 144, 145, 148, 157, 158, 159, 172, 185, 195, 197, 200, 201, 207, 221, 224, 231, 234, 254, 360, 361, 362, 363, 368, 440, 465, 477, 483, 484, 485, 486, 487, 489, 490, 491, 506, 521, 527, 564, 566, 573, 576, 577, 578, 579, 585, 588, 589, 593, 606, 610, 619, 622, 623, 627, 628, 639, 653, 659, 663, 666, 670, 671, 672, 676, 696, 697, 702, 707, 709, 713, 717, 735, 840, 841, 842, 843, 849, 992, 998, 1051], "defin": [28, 31, 38, 121, 122, 124, 133, 158, 159, 183, 186, 236, 254, 268, 383, 430, 467, 481, 483, 484, 485, 486, 487, 489, 490, 491, 568, 588, 589, 604, 622, 627, 628, 639, 671, 672, 689, 719, 735, 738, 745, 861, 914, 947, 1051], "chrono": [28, 254, 348, 351, 535, 536, 537, 540, 829, 832, 1006, 1007, 1008, 1011], "rust": [28, 35, 83, 106, 110, 133, 236, 254, 745, 1051], "crate": [28, 254, 510, 511, 516, 517, 525, 526, 535, 536, 537, 540, 738, 981, 982, 987, 988, 996, 997, 1006, 1007, 1008, 1011], "fraction": [28, 119, 120, 198, 254, 346, 466, 493, 535, 537, 639, 827, 934, 959, 1006, 1008, 1051], "second": [28, 123, 158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 535, 537, 590, 591, 626, 630, 639, 671, 672, 677, 735, 738, 822, 826, 833, 1006, 1008, 1059], "precis": [28, 31, 38, 39, 170, 196, 197, 214, 254, 317, 538, 738, 794, 1009], "infer": [28, 90, 92, 93, 94, 95, 96, 101, 102, 105, 108, 109, 112, 115, 133, 254, 478, 518, 535, 536, 537, 538, 540, 616, 639, 735, 944, 989, 1006, 1007, 1008, 1009, 1011, 1051], "maximum": [28, 101, 102, 112, 122, 123, 124, 126, 176, 254, 403, 441, 459, 474, 606, 607, 639, 682, 735, 775, 807, 887, 922, 930, 935, 940, 1051], "timeunit": [28, 38, 40, 254, 317, 318, 350, 355, 537, 588, 589, 738, 794, 795, 831, 836, 1008], "frame": [28, 29, 31, 52, 53, 54, 55, 56, 57, 67, 74, 93, 119, 124, 133, 135, 170, 171, 180, 183, 186, 195, 196, 197, 218, 225, 254, 655, 686, 689, 735, 738, 774, 1051, 1058, 1059], "datetim": [28, 31, 67, 97, 124, 139, 156, 158, 159, 171, 173, 227, 254, 316, 317, 318, 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 406, 483, 484, 485, 486, 487, 489, 490, 491, 535, 537, 554, 588, 589, 591, 597, 604, 627, 628, 639, 671, 672, 677, 735, 738, 792, 793, 794, 795, 796, 797, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 877, 890, 891, 930, 931, 963, 1006, 1008, 1034, 1051], "place": [28, 134, 141, 146, 163, 164, 187, 192, 197, 203, 204, 207, 221, 229, 254, 278, 414, 425, 496, 497, 505, 639, 653, 698, 699, 702, 707, 735, 744, 754, 846, 898, 909, 941, 943, 966, 967, 976, 1051], "float64": [28, 31, 93, 101, 124, 143, 144, 152, 199, 218, 231, 254, 270, 271, 272, 273, 274, 275, 293, 301, 302, 311, 346, 363, 389, 391, 473, 501, 502, 552, 553, 577, 593, 613, 616, 631, 639, 661, 695, 713, 735, 738, 827, 1033, 1038, 1051], "repres": [28, 50, 65, 90, 92, 94, 95, 96, 208, 228, 233, 254, 389, 391, 508, 561, 564, 577, 580, 593, 604, 617, 618, 621, 629, 639, 703, 711, 735, 963, 978, 1040, 1051], "empti": [28, 81, 93, 101, 102, 105, 112, 135, 136, 158, 167, 179, 254, 604, 619, 655, 656, 671, 685, 735, 738, 774, 778, 864, 1051], "table_nam": [29, 31, 254], "connect": [29, 101, 103, 106, 110, 114, 116, 117, 254, 651], "if_exist": [29, 254], "dbwritemod": [29, 254], "fail": [29, 30, 91, 104, 106, 109, 113, 132, 223, 254, 279, 349, 431, 535, 536, 537, 540, 639, 708, 735, 745, 1006, 1007, 1008, 1011, 1051], "dbwriteengin": [29, 254], "sqlalchemi": [29, 254], "databas": [29, 103, 254, 650], "creat": [29, 31, 90, 94, 96, 113, 122, 123, 124, 125, 126, 127, 135, 136, 158, 159, 184, 187, 227, 231, 254, 318, 345, 352, 431, 475, 483, 484, 486, 489, 490, 491, 529, 560, 578, 587, 588, 589, 590, 591, 626, 627, 628, 639, 655, 656, 662, 671, 672, 713, 735, 774, 778, 791, 795, 826, 833, 932, 941, 1000, 1032, 1051, 1058, 1059], "append": [29, 30, 124, 146, 172, 173, 254, 310, 471, 475, 588, 589, 630, 639, 676, 677, 735, 846, 1051], "your": [29, 31, 67, 101, 102, 119, 120, 133, 170, 196, 197, 200, 214, 231, 234, 236, 254, 268, 535, 536, 537, 568, 639, 657, 673, 680, 681, 696, 713, 717, 719, 735, 745, 1006, 1007, 1008, 1051, 1059], "special": [29, 101, 102, 112, 254, 517, 745, 988, 1051], "uri": [29, 30, 103, 104, 113, 254], "postgresql": [29, 103, 254, 465, 639], "user": [29, 103, 133, 186, 236, 254, 268, 438, 467, 495, 568, 588, 639, 689, 719, 735, 745, 964, 1051], "pass": [29, 31, 35, 55, 67, 92, 103, 105, 112, 122, 124, 126, 134, 138, 140, 157, 158, 159, 161, 175, 185, 186, 200, 207, 210, 221, 227, 231, 234, 254, 268, 325, 344, 366, 381, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 522, 528, 534, 564, 566, 573, 577, 585, 588, 597, 606, 610, 622, 623, 639, 653, 659, 670, 671, 672, 674, 681, 689, 696, 700, 702, 707, 713, 717, 719, 735, 745, 802, 825, 847, 856, 883, 993, 999, 1005, 1021, 1051], "server": [29, 103, 254], "port": [29, 101, 103, 106, 110, 114, 116, 254], "sqlite": [29, 254], "db": [29, 103, 254], "replac": [29, 30, 101, 102, 112, 147, 148, 193, 222, 231, 254, 318, 344, 440, 526, 570, 639, 665, 713, 735, 795, 825, 921, 962, 963, 997, 1051], "insert": [29, 101, 102, 106, 110, 112, 114, 115, 116, 164, 192, 222, 224, 254, 494, 509, 543, 639, 709, 735, 960, 980, 1014, 1051], "mode": [29, 30, 52, 254, 517, 613, 616, 631, 639, 735, 988, 1051, 1058], "new": [29, 30, 31, 112, 130, 133, 142, 163, 164, 183, 184, 191, 192, 211, 222, 224, 225, 231, 254, 263, 318, 365, 382, 439, 525, 526, 531, 532, 544, 545, 551, 570, 639, 650, 660, 693, 706, 709, 713, 719, 735, 741, 791, 795, 845, 932, 943, 996, 997, 1002, 1003, 1015, 1017, 1023, 1029, 1051, 1058], "alreadi": [29, 30, 254, 309, 409, 639, 785, 893, 1051], "adbc": [29, 103, 254], "deltalak": [30, 104, 113, 118, 254], "deltat": [30, 254], "ignor": [30, 123, 124, 126, 177, 209, 254, 312, 344, 360, 361, 362, 408, 459, 460, 639, 788, 825, 841, 842, 843, 892, 930, 931, 1051], "overwrite_schema": [30, 254], "storage_opt": [30, 101, 104, 106, 110, 113, 114, 116, 254], "delta_write_opt": [30, 254], "delta": [30, 40, 104, 113, 208, 228, 254, 489, 491, 508, 561, 580, 617, 618, 621, 629, 639, 650, 703, 711, 735, 955, 957, 978, 1040, 1051], "like": [30, 91, 100, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 166, 168, 172, 217, 254, 316, 409, 452, 474, 516, 588, 589, 623, 627, 628, 639, 664, 692, 735, 744, 793, 893, 940, 987, 1051], "categor": [30, 58, 75, 172, 215, 216, 254, 294, 295, 440, 554, 639, 738, 768, 769, 770, 786, 938, 1034, 1051], "protocol": [30, 91, 103, 132, 254], "object": [30, 31, 32, 35, 57, 74, 91, 92, 97, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 113, 122, 124, 126, 132, 157, 158, 159, 174, 197, 217, 218, 254, 588, 589, 604, 627, 628, 650, 671, 672, 692, 735, 738, 768, 792, 884, 979, 1027, 1051], "handl": [30, 74, 97, 101, 102, 112, 117, 254, 312, 408, 543, 639, 788, 892, 1014, 1051], "throw": [30, 91, 254, 293, 518, 519, 639, 767, 989, 990, 1051], "add": [30, 31, 102, 133, 146, 158, 231, 232, 254, 469, 547, 591, 595, 630, 639, 671, 676, 713, 714, 715, 735, 744, 846, 1051], "anyth": [30, 195, 254, 517, 988], "updat": [30, 254, 735], "extra": [30, 35, 48, 101, 104, 105, 106, 110, 113, 114, 116, 146, 158, 254, 671, 735, 744, 846, 1051], "storag": [30, 101, 104, 106, 110, 113, 114, 116, 254], "backend": [30, 103, 104, 113, 254], "cloud": [30, 104, 113, 117, 254], "configur": [30, 104, 113, 254], "authent": [30, 104, 113, 254], "see": [30, 31, 38, 103, 104, 105, 113, 119, 120, 124, 126, 138, 172, 186, 254, 298, 299, 300, 368, 396, 438, 503, 510, 516, 517, 525, 588, 589, 615, 639, 738, 775, 776, 777, 881, 974, 981, 987, 988, 996, 1051, 1059], "here": [30, 31, 35, 90, 92, 93, 94, 96, 103, 104, 108, 109, 113, 122, 124, 126, 254, 519, 735, 990], "gc": [30, 104, 113, 254], "azur": [30, 104, 113, 254], "keyword": [30, 55, 104, 110, 113, 138, 186, 195, 200, 231, 234, 254, 467, 619, 622, 639, 689, 696, 713, 717, 735, 1051], "argument": [30, 35, 101, 104, 110, 113, 138, 140, 145, 157, 158, 159, 177, 185, 186, 187, 200, 201, 207, 209, 218, 224, 227, 231, 234, 254, 345, 352, 363, 383, 465, 467, 483, 484, 485, 486, 487, 489, 490, 491, 506, 522, 528, 534, 564, 566, 571, 572, 573, 576, 577, 578, 579, 585, 588, 593, 606, 610, 616, 619, 622, 623, 627, 639, 659, 663, 670, 671, 672, 689, 696, 697, 702, 709, 713, 717, 735, 744, 826, 833, 861, 993, 999, 1005, 1033, 1051], "while": [30, 102, 104, 105, 113, 124, 126, 170, 179, 222, 254, 685, 735], "lake": [30, 104, 113, 254, 650], "instanti": [30, 31, 200, 231, 254, 696, 713, 735], "basic": [30, 31, 254, 1059], "filesystem": [30, 104, 113, 254], "table_path": [30, 104, 113, 254], "doe": [30, 67, 74, 84, 90, 92, 93, 94, 96, 97, 101, 102, 104, 105, 108, 109, 112, 113, 117, 119, 120, 146, 171, 172, 195, 196, 197, 223, 231, 254, 292, 355, 431, 440, 558, 588, 594, 603, 639, 664, 676, 681, 708, 713, 735, 744, 836, 846, 854, 921, 969, 1041, 1051], "match": [30, 31, 38, 74, 84, 90, 92, 93, 94, 96, 108, 109, 119, 120, 148, 173, 195, 254, 446, 488, 510, 511, 514, 516, 517, 518, 519, 525, 526, 533, 535, 536, 537, 577, 639, 666, 677, 735, 738, 870, 877, 954, 981, 982, 985, 987, 988, 989, 990, 996, 997, 1004, 1006, 1007, 1008, 1051], "version": [30, 72, 104, 113, 118, 254, 292, 337, 338, 535, 537, 570, 588, 615, 616, 627, 639, 744, 818, 819, 1006, 1008, 1051], "old": [30, 191, 254, 693, 735], "existing_table_path": [30, 254], "store": [30, 101, 110, 146, 170, 196, 254, 294, 744, 769, 846, 1051], "bucket": [30, 104, 113, 254, 345, 352, 826, 833, 857, 1051], "prefix": [30, 130, 254, 263, 290, 439, 533, 543, 547, 639, 738, 765, 1004, 1014], "aws_region": [30, 113, 254], "the_aws_region": [30, 254], "aws_access_key_id": [30, 104, 113, 254], "the_aws_access_key_id": [30, 104, 113, 254], "aws_secret_access_kei": [30, 104, 113, 254], "the_aws_secret_access_kei": [30, 104, 113, 254], "workbook": [31, 254], "worksheet": [31, 254], "posit": [31, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 360, 361, 362, 363, 465, 471, 506, 524, 564, 566, 571, 572, 573, 576, 577, 578, 579, 585, 593, 606, 610, 619, 622, 623, 639, 659, 663, 670, 696, 697, 702, 709, 713, 717, 735, 841, 842, 843, 938, 995, 1051], "tupl": [31, 103, 133, 170, 195, 196, 197, 202, 233, 254, 478, 639, 690, 700, 735, 738, 944, 1051], "a1": [31, 68, 70, 254], "table_styl": [31, 254], "column_format": [31, 254], "dtype_format": [31, 254], "oneormoredatatyp": [31, 122, 254, 877, 1051], "conditional_format": [31, 254], "conditionalformatdict": [31, 254], "column_tot": [31, 254], "columntotalsdefinit": [31, 254], "column_width": [31, 254], "row_tot": [31, 254], "rowtotalsdefinit": [31, 254], "row_height": [31, 254], "sparklin": [31, 254], "sequenc": [31, 59, 67, 73, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 122, 123, 124, 134, 139, 145, 146, 172, 173, 179, 183, 186, 187, 197, 207, 215, 221, 223, 224, 225, 226, 227, 254, 387, 431, 438, 467, 506, 545, 568, 573, 583, 584, 595, 597, 605, 615, 620, 639, 653, 663, 676, 677, 689, 702, 707, 708, 709, 710, 735, 744, 787, 789, 846, 915, 963, 1017, 1051], "formula": [31, 254, 357, 639, 839, 1051], "autofilt": [31, 254], "autofit": [31, 254], "hidden_column": [31, 254], "hide_gridlin": [31, 254], "sheet_zoom": [31, 254], "freeze_pan": [31, 254], "excel": [31, 105, 254, 650], "open": [31, 101, 102, 105, 106, 110, 114, 116, 254], "xlsxwriter": [31, 118, 254], "ha": [31, 67, 112, 132, 158, 159, 227, 254, 268, 291, 292, 309, 449, 483, 484, 485, 486, 487, 489, 490, 491, 555, 568, 570, 639, 671, 672, 719, 735, 766, 774, 785, 787, 854, 867, 1035, 1051], "close": [31, 158, 159, 254, 383, 435, 483, 484, 485, 486, 487, 489, 490, 491, 503, 588, 589, 627, 628, 639, 671, 672, 735, 861, 974, 1051], "xlsx": [31, 105, 254], "work": [31, 39, 102, 105, 192, 254, 268, 284, 297, 298, 299, 300, 363, 372, 409, 432, 465, 481, 523, 557, 639, 760, 772, 775, 776, 777, 852, 893, 916, 994, 1036, 1051], "directori": [31, 35, 110, 254], "sheet1": [31, 254], "valid": [31, 38, 52, 106, 110, 126, 130, 144, 172, 254, 309, 510, 511, 516, 517, 519, 525, 526, 588, 589, 639, 676, 735, 738, 785, 840, 854, 981, 982, 987, 988, 990, 996, 997, 1051], "notat": [31, 254], "integ": [31, 43, 44, 45, 46, 61, 62, 63, 64, 122, 124, 125, 127, 158, 159, 254, 265, 297, 346, 372, 373, 464, 471, 476, 483, 484, 485, 486, 487, 488, 489, 490, 491, 524, 563, 570, 597, 601, 602, 616, 639, 671, 672, 735, 738, 772, 827, 852, 870, 938, 942, 954, 963, 995, 1051, 1058, 1059], "medium": [31, 254], "kei": [31, 67, 72, 74, 158, 170, 172, 173, 180, 185, 187, 191, 194, 196, 197, 254, 622, 671, 676, 677, 686, 693, 694, 735], "follow": [31, 35, 72, 101, 102, 104, 112, 113, 133, 158, 159, 173, 186, 227, 254, 268, 341, 345, 352, 467, 474, 483, 484, 485, 486, 487, 488, 489, 490, 491, 545, 556, 568, 588, 630, 632, 633, 635, 639, 640, 641, 645, 646, 647, 671, 672, 677, 689, 735, 822, 826, 833, 940, 962, 963, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057, 1059], "first_column": [31, 254], "last_column": [31, 254], "banded_column": [31, 254], "banded_row": [31, 254], "sheet": [31, 105, 254], "chart": [31, 254, 690, 735], "subsequ": [31, 57, 190, 218, 254, 431, 630, 662, 735], "colnam": [31, 112, 124, 143, 254, 661, 735], "given": [31, 52, 53, 67, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 121, 122, 124, 125, 126, 127, 133, 134, 144, 145, 147, 158, 159, 169, 185, 186, 195, 197, 203, 204, 207, 221, 226, 254, 268, 310, 316, 319, 348, 350, 351, 357, 383, 406, 425, 431, 433, 465, 467, 471, 474, 477, 478, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 494, 496, 497, 517, 535, 537, 545, 568, 593, 605, 616, 617, 618, 639, 653, 663, 671, 672, 681, 689, 698, 699, 702, 707, 710, 719, 735, 738, 745, 786, 793, 796, 829, 831, 832, 839, 840, 857, 861, 880, 890, 909, 915, 917, 920, 932, 938, 940, 944, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 960, 966, 967, 988, 1006, 1008, 1038, 1042, 1051, 1058, 1059], "dd": [31, 254], "mm": [31, 254], "yyyi": [31, 254], "00": [31, 124, 158, 173, 227, 254, 316, 317, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 535, 537, 540, 588, 591, 627, 628, 639, 671, 677, 735, 738, 793, 794, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 836, 837, 1006, 1008, 1011], "overridden": [31, 90, 92, 94, 96, 108, 109, 128, 254, 735], "basi": [31, 124, 254], "param": [31, 90, 92, 93, 94, 96, 101, 102, 108, 109, 112, 123, 124, 126, 195, 254, 735], "It": [31, 180, 186, 236, 254, 268, 292, 448, 481, 588, 589, 639, 681, 686, 719, 735, 962, 963, 1051], "also": [31, 57, 93, 112, 122, 123, 124, 125, 126, 130, 143, 157, 158, 159, 180, 183, 196, 200, 207, 231, 254, 305, 308, 344, 383, 474, 483, 484, 485, 486, 487, 489, 490, 491, 506, 528, 534, 583, 584, 588, 595, 597, 615, 630, 638, 639, 644, 670, 671, 672, 686, 696, 702, 713, 714, 735, 738, 825, 861, 940, 1005, 1051], "group": [31, 35, 48, 52, 102, 157, 158, 159, 171, 183, 185, 187, 197, 225, 227, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 254, 262, 268, 365, 371, 409, 465, 471, 474, 481, 505, 506, 510, 516, 517, 525, 550, 568, 639, 670, 671, 672, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 735, 738, 893, 947, 981, 987, 988, 996, 1051], "float_dtyp": [31, 254], "simplifi": [31, 47, 48, 73, 254, 657, 662, 664, 690, 700, 735], "uniform": [31, 254], "condit": [31, 142, 149, 195, 254, 514, 533, 574, 595, 630, 660, 667, 735], "suppli": [31, 90, 92, 93, 94, 96, 108, 109, 195, 254, 467, 639, 735], "typenam": [31, 254], "3_color_scal": [31, 254], "data_bar": [31, 254], "make": [31, 59, 74, 90, 95, 101, 102, 106, 110, 114, 116, 180, 183, 190, 227, 254, 361, 362, 440, 465, 477, 493, 499, 531, 543, 639, 686, 735, 842, 843, 857, 921, 1002, 1014, 1051, 1059], "icon": [31, 254], "multipl": [31, 55, 73, 74, 101, 102, 112, 114, 115, 116, 122, 126, 134, 140, 144, 146, 149, 157, 158, 163, 171, 185, 187, 197, 200, 201, 207, 221, 231, 234, 254, 430, 445, 449, 456, 457, 465, 478, 481, 483, 484, 485, 486, 487, 489, 490, 491, 506, 564, 566, 570, 573, 577, 583, 584, 585, 595, 605, 606, 610, 615, 620, 623, 630, 639, 653, 659, 667, 670, 671, 696, 697, 702, 707, 713, 717, 719, 735, 744, 840, 846, 914, 926, 944, 1051], "singl": [31, 53, 67, 74, 101, 102, 103, 110, 112, 122, 133, 140, 141, 142, 146, 154, 169, 172, 185, 195, 197, 207, 254, 268, 341, 369, 407, 430, 438, 475, 478, 506, 509, 562, 564, 566, 568, 570, 573, 577, 578, 579, 585, 602, 605, 606, 610, 620, 623, 628, 639, 659, 660, 676, 702, 735, 744, 822, 846, 891, 914, 941, 944, 980, 1027, 1051], "across": [31, 67, 254, 564, 565, 566, 567, 585, 586, 606, 607, 610, 611, 623, 624], "effect": [31, 132, 152, 158, 217, 254, 268, 324, 588, 589, 639, 671, 715, 735, 793, 801], "heatmap": [31, 254], "min": [31, 35, 48, 139, 148, 158, 159, 187, 254, 298, 300, 306, 368, 465, 474, 486, 611, 619, 639, 666, 671, 672, 735, 775, 777, 782, 787, 849, 940, 952, 1051, 1059], "entir": [31, 254], "final": [31, 67, 116, 254, 360, 361, 362, 639, 664, 735, 841, 842, 843, 1051], "made": [31, 254, 1032, 1051], "up": [31, 59, 103, 170, 173, 196, 197, 214, 254, 268, 297, 543, 639, 676, 677, 681, 735, 738, 772, 1014, 1051], "abov": [31, 254, 630], "order": [31, 47, 48, 52, 54, 67, 73, 93, 119, 133, 134, 157, 158, 159, 185, 186, 187, 198, 201, 207, 217, 221, 223, 227, 254, 268, 278, 284, 295, 427, 432, 474, 493, 494, 495, 499, 505, 506, 545, 557, 558, 573, 639, 649, 653, 670, 671, 672, 685, 697, 702, 707, 708, 735, 745, 754, 760, 770, 786, 876, 911, 916, 938, 940, 945, 959, 960, 964, 976, 1017, 1036, 1037, 1051], "than": [31, 47, 48, 89, 94, 96, 101, 102, 112, 117, 124, 126, 133, 135, 156, 157, 170, 173, 185, 195, 196, 198, 225, 236, 254, 268, 376, 377, 398, 437, 451, 481, 493, 503, 521, 527, 532, 543, 568, 580, 588, 623, 639, 670, 677, 719, 735, 745, 947, 959, 974, 992, 998, 1003, 1014, 1051, 1059], "total": [31, 144, 254, 840, 1051], "export": [31, 170, 171, 196, 197, 214, 217, 254], "numer": [31, 173, 254, 261, 298, 299, 300, 373, 383, 435, 455, 457, 468, 477, 498, 546, 556, 639, 650, 677, 735, 738, 775, 776, 777, 787, 861, 875, 961, 968, 1032, 1051, 1059], "associ": [31, 53, 55, 67, 103, 123, 197, 254], "sum": [31, 52, 144, 152, 157, 158, 159, 169, 187, 234, 254, 268, 307, 308, 357, 369, 431, 483, 486, 490, 562, 564, 583, 585, 586, 595, 615, 624, 639, 657, 662, 664, 670, 671, 672, 688, 690, 700, 717, 735, 738, 783, 784, 839, 840, 949, 950, 952, 955, 956, 957, 1051], "must": [31, 91, 92, 104, 113, 139, 145, 158, 159, 173, 180, 195, 254, 310, 431, 438, 471, 483, 484, 485, 486, 487, 489, 490, 491, 639, 663, 671, 672, 677, 681, 686, 735, 786, 787, 938, 1051], "funcnam": [31, 254], "averag": [31, 254, 360, 361, 362, 474, 639, 841, 842, 843, 940, 1051], "count_num": [31, 254], "count": [31, 52, 101, 102, 106, 110, 112, 114, 115, 116, 139, 158, 159, 173, 183, 184, 187, 222, 226, 227, 232, 236, 245, 254, 269, 304, 341, 399, 407, 458, 463, 483, 484, 485, 486, 487, 489, 490, 491, 511, 558, 560, 569, 612, 639, 671, 672, 677, 688, 710, 715, 719, 728, 735, 787, 822, 857, 891, 928, 933, 982, 1037, 1039, 1051], "std_dev": [31, 254], "var": [31, 127, 254, 491, 639, 735, 1051], "pixel": [31, 254], "unit": [31, 38, 40, 124, 126, 144, 254, 317, 318, 325, 350, 355, 436, 449, 535, 537, 559, 588, 589, 597, 639, 690, 735, 738, 794, 795, 802, 831, 836, 840, 920, 1006, 1008, 1038, 1051, 1059], "hand": [31, 101, 102, 112, 254, 421, 422, 423, 424, 905, 906, 907, 908], "side": [31, 158, 159, 254, 383, 421, 422, 423, 424, 483, 484, 485, 486, 487, 489, 490, 491, 494, 588, 589, 627, 628, 639, 671, 672, 735, 861, 905, 906, 907, 908, 960, 1051], "call": [31, 56, 102, 124, 126, 130, 133, 157, 158, 159, 174, 253, 254, 268, 305, 308, 395, 469, 547, 606, 610, 639, 651, 670, 671, 672, 734, 735, 738, 745, 1051], "ad": [31, 93, 132, 158, 222, 231, 254, 267, 366, 583, 584, 639, 671, 713, 735, 847, 1051], "end": [31, 101, 102, 110, 112, 158, 254, 286, 289, 290, 316, 341, 345, 346, 363, 383, 426, 510, 514, 517, 529, 533, 570, 577, 588, 589, 593, 601, 602, 627, 628, 630, 639, 671, 673, 680, 690, 735, 738, 764, 793, 822, 826, 861, 910, 985, 988, 1000, 1004, 1051], "wise": [31, 67, 152, 254, 270, 271, 272, 273, 274, 275, 301, 302, 364, 434, 500, 501, 502, 552, 553, 583, 584, 595, 606, 610, 615, 639, 746, 747, 748, 749, 750, 751, 779, 780, 844, 918, 919, 971, 972, 973, 1024, 1025, 1051], "particip": [31, 254], "distinct": [31, 126, 185, 254, 284, 432, 474, 591, 639, 760, 916, 940, 1051, 1059], "referenc": [31, 254, 545], "differ": [31, 101, 117, 119, 146, 158, 159, 170, 196, 197, 214, 222, 254, 312, 322, 341, 342, 344, 353, 359, 408, 421, 423, 440, 459, 460, 462, 493, 499, 535, 558, 588, 594, 597, 603, 639, 662, 671, 672, 735, 738, 744, 745, 788, 799, 822, 823, 825, 834, 846, 892, 905, 907, 930, 931, 961, 1006, 1051], "row_index": [31, 254], "height": [31, 142, 254], "provid": [31, 47, 48, 55, 101, 102, 103, 104, 112, 113, 124, 126, 169, 254, 268, 287, 288, 431, 438, 512, 513, 516, 518, 519, 597, 622, 639, 650, 735, 745, 762, 763, 857, 880, 983, 984, 987, 989, 990, 1051, 1058, 1059], "intersect": [31, 254, 422, 738, 906], "bodi": [31, 254], "start": [31, 100, 101, 102, 103, 106, 110, 112, 114, 115, 116, 128, 157, 158, 174, 206, 227, 232, 254, 286, 289, 290, 322, 325, 326, 328, 329, 334, 336, 342, 343, 345, 346, 350, 352, 353, 354, 356, 363, 383, 426, 483, 484, 486, 489, 490, 491, 504, 510, 514, 517, 529, 533, 570, 577, 583, 588, 589, 593, 595, 601, 602, 627, 628, 630, 639, 670, 671, 690, 701, 715, 735, 738, 765, 794, 796, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 836, 837, 861, 910, 975, 985, 988, 1000, 1004, 1051, 1059], "zero": [31, 90, 91, 100, 101, 102, 106, 110, 123, 132, 148, 170, 195, 212, 217, 218, 254, 368, 431, 435, 494, 503, 543, 556, 639, 655, 666, 735, 774, 849, 915, 974, 1014, 1027, 1032, 1033, 1051], "unless": [31, 67, 92, 218, 254, 528, 534, 616, 735, 1005, 1033, 1041, 1051], "marker": [31, 254], "compliant": [31, 254], "case": [31, 67, 90, 92, 93, 94, 96, 108, 109, 116, 128, 133, 146, 158, 159, 170, 196, 197, 214, 254, 316, 438, 439, 448, 449, 510, 517, 525, 639, 671, 672, 735, 738, 744, 793, 846, 981, 988, 996, 1051], "three": [31, 220, 254, 431, 494, 639, 915], "avail": [31, 99, 103, 104, 113, 122, 130, 253, 254, 474, 570, 632, 633, 635, 638, 639, 640, 641, 644, 645, 646, 647, 650, 651, 664, 734, 735, 738, 940, 1043, 1045, 1047, 1051, 1052, 1055, 1056, 1057], "insert_befor": [31, 254], "insert_aft": [31, 254], "respect": [31, 254], "direct": [31, 103, 110, 116, 225, 254], "far": [31, 254], "thei": [31, 93, 112, 119, 130, 174, 197, 254, 482, 639, 738, 948, 1051], "strongli": [31, 128, 133, 195, 236, 254, 268, 639, 745, 1051], "advis": [31, 174, 254], "structur": [31, 81, 85, 87, 146, 186, 197, 217, 254, 467, 639, 689, 735, 744, 846, 1051], "wherev": [31, 133, 236, 254, 268, 639, 745, 1051], "possibl": [31, 101, 133, 134, 157, 170, 196, 221, 223, 236, 254, 268, 436, 448, 532, 559, 639, 653, 670, 702, 707, 708, 735, 745, 1003, 1051], "simpl": [31, 126, 183, 254], "colx": [31, 57, 254, 738, 1059], "coli": [31, 254, 738, 1059], "after": [31, 57, 74, 93, 100, 101, 102, 106, 110, 112, 114, 115, 116, 146, 224, 253, 254, 363, 440, 465, 474, 543, 639, 709, 735, 744, 846, 921, 940, 1014, 1051], "befor": [31, 101, 112, 128, 130, 146, 158, 173, 224, 254, 307, 308, 309, 440, 465, 466, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 543, 548, 617, 618, 639, 671, 674, 677, 709, 735, 744, 783, 784, 785, 846, 921, 934, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1014, 1020, 1051], "most": [31, 54, 90, 101, 102, 103, 112, 254, 449, 456, 466, 532, 560, 639, 926, 934, 1003, 1039, 1051, 1058], "mandatori": [31, 254], "return_dtyp": [31, 133, 254, 268, 438, 440, 568, 605, 639, 745, 921, 1051], "latter": [31, 146, 254, 744, 846, 1051], "appropri": [31, 217, 254, 474, 639, 940, 1051], "pure": [31, 254, 1032, 1051], "actual": [31, 93, 105, 124, 126, 197, 254, 969, 1051], "indic": [31, 100, 101, 102, 106, 110, 112, 121, 122, 126, 158, 159, 173, 197, 201, 215, 227, 254, 279, 341, 345, 352, 385, 388, 389, 391, 392, 393, 430, 444, 447, 483, 484, 485, 486, 487, 489, 490, 491, 494, 500, 550, 573, 574, 639, 671, 672, 677, 697, 735, 738, 822, 826, 833, 865, 869, 871, 872, 873, 874, 914, 960, 971, 1022, 1028, 1051, 1059], "calcul": [31, 67, 158, 208, 228, 254, 312, 360, 361, 362, 396, 408, 421, 436, 488, 503, 508, 559, 561, 568, 580, 617, 618, 621, 629, 639, 671, 703, 711, 735, 788, 841, 842, 843, 881, 892, 905, 954, 974, 978, 1040, 1051], "individu": [31, 48, 124, 159, 217, 254, 268, 517, 639, 672, 735, 773, 988, 1051], "gridlin": [31, 254], "zoom": [31, 254], "level": [31, 35, 48, 112, 114, 115, 116, 124, 133, 152, 183, 254, 369, 639, 673, 680, 735], "freez": [31, 254], "pane": [31, 254], "top": [31, 134, 221, 254, 653, 707, 735], "index": [31, 91, 95, 98, 118, 132, 150, 158, 159, 164, 169, 170, 173, 187, 193, 195, 196, 206, 217, 219, 227, 232, 254, 262, 276, 277, 278, 280, 341, 403, 404, 412, 426, 430, 431, 483, 484, 485, 486, 487, 489, 490, 491, 494, 504, 516, 529, 550, 639, 671, 672, 677, 701, 715, 735, 752, 753, 754, 755, 756, 822, 880, 887, 888, 896, 910, 914, 915, 932, 960, 963, 975, 987, 1000, 1022, 1051], "thu": [31, 146, 254, 580, 744, 846, 1051], "altern": [31, 254, 1032, 1051], "a2": [31, 68, 70, 254], "occur": [31, 73, 254, 407, 456, 474, 639, 657, 662, 664, 690, 700, 735, 891, 926, 940, 1051], "equival": [31, 90, 92, 93, 94, 96, 104, 108, 109, 158, 169, 183, 254, 261, 265, 358, 359, 373, 376, 377, 398, 437, 455, 457, 461, 462, 464, 468, 523, 546, 556, 563, 639, 671, 735, 880, 994, 1010, 1012, 1013, 1051], "top_row": [31, 254], "top_col": [31, 254], "base": [31, 36, 142, 149, 158, 159, 254, 316, 324, 357, 360, 361, 362, 433, 434, 471, 524, 639, 660, 667, 671, 672, 735, 738, 793, 801, 839, 841, 842, 843, 857, 917, 918, 938, 995, 1042, 1051, 1059], "scroll": [31, 254], "region": [31, 254], "initit": [31, 254], "5th": [31, 254], "definit": [31, 122, 254, 396, 639, 881, 1051], "take": [31, 124, 130, 152, 158, 180, 186, 187, 211, 217, 254, 341, 506, 551, 588, 589, 592, 594, 603, 639, 671, 686, 706, 735, 822, 1023, 1042, 1051], "care": [31, 254, 268, 495, 639, 964, 1051], "rel": [31, 103, 104, 113, 119, 120, 254, 341, 360, 361, 362, 485, 487, 489, 491, 639, 822, 841, 842, 843, 1051], "readthedoc": [31, 254], "io": [31, 254], "working_with_conditional_format": [31, 254], "html": [31, 91, 132, 138, 254], "similarli": [31, 93, 158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "well": [31, 101, 102, 112, 145, 187, 254, 383, 588, 639, 663, 735, 861, 1051], "adjac": [31, 254], "two": [31, 57, 92, 94, 96, 103, 152, 179, 180, 187, 220, 235, 236, 254, 262, 313, 318, 431, 505, 550, 570, 571, 572, 580, 582, 617, 618, 639, 685, 686, 718, 719, 735, 789, 795, 915, 1051], "help": [31, 254, 664, 735], "appear": [31, 93, 119, 254, 558, 639, 1037, 1051], "working_with_sparklin": [31, 254], "inject": [31, 67, 254], "locat": [31, 146, 193, 219, 224, 254, 494, 639, 709, 735, 744, 846, 960, 963, 1022, 1051], "syntax": [31, 133, 183, 254, 510, 516, 517, 525, 700, 735, 981, 987, 988, 996, 1051], "ensur": [31, 75, 103, 123, 124, 126, 157, 185, 195, 254, 383, 560, 639, 670, 681, 735, 738, 1032, 1039, 1051], "correctli": [31, 254], "microsoft": [31, 118, 254], "com": [31, 103, 254, 360, 361, 362, 516, 517, 639, 841, 842, 843, 987, 988, 1051], "u": [31, 38, 40, 55, 97, 254, 317, 318, 325, 350, 355, 535, 537, 588, 589, 597, 738, 794, 795, 802, 831, 836, 1006, 1008], "offic": [31, 254], "f5ed2452": [31, 254], "2337": [31, 254], "4f71": [31, 254], "bed3": [31, 254], "c8ae6d2b276": [31, 254], "random": [31, 122, 124, 126, 160, 198, 254, 378, 474, 493, 499, 639, 855, 940, 959, 970, 1051], "date": [31, 38, 67, 101, 102, 103, 112, 117, 124, 139, 156, 158, 159, 171, 173, 227, 254, 317, 318, 319, 322, 323, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 406, 483, 484, 485, 486, 487, 489, 490, 491, 535, 536, 554, 588, 589, 597, 604, 626, 639, 671, 672, 677, 735, 738, 793, 794, 795, 796, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 877, 890, 891, 930, 931, 963, 1006, 1007, 1034, 1051], "dtm": [31, 254, 318, 795], "2023": [31, 97, 118, 171, 254, 318, 588, 604, 738, 795], "num": [31, 220, 254, 383, 386, 543, 639, 861, 1051], "500": [31, 170, 226, 254, 664, 710, 735, 810, 812, 820, 827, 949, 950, 952, 1051], "val": [31, 194, 254, 295, 467, 554, 630, 639, 694, 735, 770], "10_000": [31, 254], "20_000": [31, 254], "30_000": [31, 254], "increas": [31, 67, 101, 110, 254, 309, 639, 785, 1051], "b4": [31, 254], "light": [31, 254], "twice": [31, 105, 254], "each": [31, 67, 102, 115, 119, 120, 122, 133, 156, 157, 158, 159, 171, 173, 185, 218, 224, 227, 233, 234, 236, 237, 239, 248, 254, 268, 310, 341, 345, 352, 413, 429, 435, 471, 474, 481, 485, 487, 489, 491, 506, 517, 531, 532, 568, 570, 573, 579, 588, 601, 602, 622, 639, 671, 672, 677, 690, 709, 717, 719, 720, 722, 731, 735, 773, 786, 822, 826, 833, 897, 913, 938, 940, 947, 988, 1002, 1003, 1019, 1051, 1059], "titl": [31, 52, 254], "explicit": [31, 112, 122, 254, 615], "integr": [31, 254, 1059], "multi_fram": [31, 254], "wb": [31, 254], "coordin": [31, 254], "advanc": [31, 254, 431, 915, 1059], "min_color": [31, 254], "76933c": [31, 254], "mid_color": [31, 254], "c4d79b": [31, 254], "max_color": [31, 254], "ebf1d": [31, 254], "data_bar_2010": [31, 254], "bar_color": [31, 254], "9bbb59": [31, 254], "bar_negative_color_sam": [31, 254], "bar_negative_border_color_sam": [31, 254], "000": [31, 254, 1059], "white": [31, 254], "w": [31, 54, 55, 158, 197, 254, 516, 565, 567, 671, 735, 738, 987, 999], "get_worksheet_by_nam": [31, 254], "fmt_titl": [31, 254], "add_format": [31, 254], "font_color": [31, 254], "4f6228": [31, 254], "font_siz": [31, 254], "12": [31, 67, 97, 123, 124, 158, 159, 173, 227, 254, 263, 307, 316, 318, 322, 326, 329, 336, 337, 338, 342, 343, 345, 352, 354, 356, 466, 473, 490, 498, 538, 584, 587, 588, 590, 605, 639, 671, 672, 673, 677, 680, 700, 705, 735, 738, 745, 786, 793, 795, 817, 826, 833, 958, 1009, 1051, 1059], "ital": [31, 254], "bold": [31, 254], "customis": [31, 254], "trend": [31, 254], "win_loss": [31, 254], "subtl": [31, 254], "tone": [31, 254], "hidden": [31, 254], "id": [31, 74, 222, 236, 254, 481, 525, 526, 558, 560, 639, 719, 947, 1037, 1051, 1059], "q1": [31, 254], "55": [31, 69, 254], "20": [31, 124, 146, 163, 164, 180, 186, 188, 192, 193, 254, 276, 277, 278, 312, 345, 352, 378, 382, 467, 483, 484, 486, 597, 604, 627, 639, 686, 689, 735, 738, 788, 833, 1051], "35": [31, 118, 254, 312, 639, 788, 1051], "q2": [31, 254], "30": [31, 146, 158, 163, 186, 192, 193, 213, 231, 254, 276, 277, 278, 312, 318, 323, 329, 337, 343, 344, 345, 352, 356, 378, 498, 588, 604, 627, 639, 671, 689, 713, 735, 738, 786, 788, 795, 800, 818, 825, 826, 833, 1051, 1059], "15": [31, 118, 123, 133, 158, 159, 164, 254, 309, 312, 338, 345, 352, 467, 490, 584, 616, 627, 639, 671, 672, 735, 738, 785, 788, 826, 833, 1051], "60": [31, 146, 254, 346, 347, 490, 535, 639, 823, 827, 828, 1006], "q3": [31, 254], "40": [31, 146, 186, 254, 345, 352, 378, 538, 639, 689, 735, 806, 833, 1009], "80": [31, 254], "q4": [31, 254], "75": [31, 139, 254, 265, 464, 483, 484, 485, 486, 489, 490, 491, 639, 787, 857, 938, 1051, 1059], "account": [31, 97, 103, 254, 341, 360, 361, 362, 639, 822, 841, 842, 843, 1051], "flavour": [31, 254], "integer_dtyp": [31, 200, 254, 696, 735, 738], "0_": [31, 254], "just": [31, 112, 179, 254, 685, 735], "unifi": [31, 254, 738], "multi": [31, 101, 102, 254, 363, 516, 606, 610, 639, 987], "2_color_scal": [31, 254], "95b3d7": [31, 254], "ffffff": [31, 254], "standardis": [31, 254], "z": [31, 54, 74, 97, 122, 124, 144, 166, 168, 172, 179, 196, 197, 254, 261, 263, 265, 295, 378, 405, 439, 464, 469, 477, 517, 535, 537, 547, 565, 567, 586, 607, 611, 616, 624, 639, 676, 685, 701, 735, 738, 770, 849, 988, 1006, 1008, 1051, 1058], "score": [31, 254], "conjunct": [31, 105, 254], "a123": [31, 254], "b345": [31, 254], "c567": [31, 254], "d789": [31, 254], "e101": [31, 254], "45": [31, 159, 254, 318, 345, 352, 490, 511, 517, 571, 572, 604, 627, 639, 672, 735, 738, 795, 826, 833, 982, 988, 1059], "85": [31, 254, 1059], "font": [31, 254], "consola": [31, 254], "standard": [31, 118, 208, 217, 254, 329, 361, 489, 503, 508, 519, 621, 639, 703, 735, 806, 842, 974, 978, 990, 1051, 1059], "stdev": [31, 254], "ipccompress": [32, 254], "arrow": [32, 47, 76, 90, 103, 106, 114, 170, 196, 197, 212, 214, 254, 735, 1027, 1032, 1051], "ipc": [32, 106, 107, 114, 117, 254, 650], "binari": [32, 254, 286, 288, 289, 290, 761, 764, 765], "feather": [32, 106, 114, 254, 650], "lz4": [32, 35, 47, 48, 254, 735], "zstd": [32, 35, 47, 48, 254, 735], "pretti": [33, 254], "row_ori": [33, 254], "iobas": [33, 34, 108, 109, 254, 453, 692, 716, 735], "serial": [33, 34, 254], "represent": [33, 34, 216, 254, 295, 322, 326, 329, 330, 332, 334, 336, 339, 342, 343, 346, 353, 354, 356, 554, 639, 662, 679, 735, 770, 799, 803, 805, 806, 810, 812, 815, 817, 820, 823, 824, 827, 834, 835, 837, 1030, 1034, 1051], "orient": [33, 68, 70, 94, 96, 254, 735], "slower": [33, 94, 96, 133, 157, 185, 227, 236, 254, 268, 568, 639, 670, 719, 735, 745, 1051], "common": [33, 67, 73, 74, 254, 439, 588, 589, 639, 644, 657, 662, 664, 690, 700, 735], "write_ndjson": [33, 254], "newlin": [34, 109, 115, 254], "delimit": [34, 101, 102, 109, 112, 115, 187, 215, 254, 509, 980, 1028, 1051], "parquetcompress": [35, 254], "compression_level": [35, 48, 254, 735], "statist": [35, 48, 101, 102, 110, 116, 139, 254, 361, 362, 396, 483, 484, 485, 486, 487, 488, 489, 490, 491, 503, 639, 735, 787, 842, 843, 881, 954, 974, 1051], "row_group_s": [35, 48, 254, 735], "use_pyarrow": [35, 48, 101, 106, 110, 254, 735, 1031, 1032, 1051], "pyarrow_opt": [35, 104, 110, 113, 254], "parquet": [35, 48, 110, 111, 116, 254, 650, 735], "gzip": [35, 48, 254, 735], "lzo": [35, 48, 254, 735], "brotli": [35, 48, 254, 735], "choos": [35, 47, 48, 187, 254, 735], "good": [35, 47, 48, 170, 254, 735], "perform": [35, 47, 48, 67, 81, 91, 101, 102, 106, 110, 112, 114, 115, 116, 132, 133, 134, 157, 158, 159, 173, 190, 197, 221, 236, 254, 268, 465, 535, 536, 537, 639, 653, 671, 672, 677, 702, 707, 715, 735, 745, 1006, 1007, 1008, 1051], "fast": [35, 47, 48, 125, 127, 254, 366, 495, 639, 735, 847, 964, 1051, 1059], "decompress": [35, 47, 48, 254, 735], "backward": [35, 48, 148, 173, 254, 285, 338, 368, 639, 666, 677, 735, 819, 849, 1051], "guarante": [35, 48, 91, 101, 102, 223, 254, 664, 708, 735], "deal": [35, 48, 170, 254, 344, 352, 474, 535, 639, 735, 825, 833, 940, 1006, 1051], "older": [35, 48, 254, 735], "reader": [35, 48, 99, 101, 102, 106, 110, 254, 651, 735], "higher": [35, 48, 189, 246, 254, 472, 487, 614, 639, 691, 729, 735, 939, 953, 1051], "mean": [35, 48, 101, 102, 106, 110, 112, 139, 148, 157, 158, 159, 173, 187, 227, 234, 254, 341, 345, 352, 365, 368, 483, 484, 485, 486, 487, 489, 490, 491, 503, 516, 570, 575, 588, 639, 666, 670, 671, 672, 677, 681, 717, 735, 787, 822, 826, 833, 845, 849, 854, 950, 974, 987, 1051], "smaller": [35, 48, 144, 254, 664, 735, 840, 1051], "disk": [35, 47, 48, 106, 254, 700, 735], "11": [35, 48, 118, 124, 159, 254, 263, 314, 315, 329, 337, 338, 341, 345, 352, 382, 466, 474, 490, 504, 543, 563, 577, 623, 628, 639, 657, 672, 673, 680, 690, 705, 735, 738, 745, 822, 826, 833, 948, 1051], "22": [35, 48, 123, 254, 322, 342, 345, 352, 354, 355, 483, 484, 486, 489, 490, 491, 535, 577, 639, 735, 738, 826, 833, 836, 1006, 1059], "comput": [35, 48, 73, 74, 78, 144, 157, 173, 218, 223, 234, 246, 254, 260, 270, 271, 272, 273, 274, 275, 281, 282, 283, 296, 301, 302, 304, 305, 306, 307, 308, 313, 357, 364, 396, 417, 418, 419, 421, 422, 423, 424, 433, 434, 435, 456, 465, 466, 470, 471, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 500, 501, 502, 503, 507, 552, 553, 564, 565, 566, 567, 571, 572, 573, 580, 582, 585, 592, 606, 610, 617, 618, 623, 639, 670, 674, 676, 677, 708, 714, 717, 729, 735, 740, 746, 747, 748, 749, 750, 751, 757, 758, 759, 771, 779, 780, 781, 782, 783, 784, 789, 839, 840, 844, 881, 901, 902, 903, 905, 906, 907, 908, 917, 918, 919, 926, 934, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 971, 972, 973, 974, 977, 1024, 1025, 1033, 1051], "512": [35, 254, 468, 639, 934, 1051], "implement": [35, 91, 132, 133, 236, 254, 268, 395, 469, 547, 568, 639, 719, 745, 962, 963, 1051], "v": [35, 54, 55, 144, 254, 494, 639, 786, 960, 1051], "At": [35, 254], "moment": [35, 138, 254, 396, 503, 639, 881, 974, 1051], "pyarrow": [35, 90, 95, 101, 103, 104, 106, 110, 113, 117, 118, 171, 212, 217, 218, 254, 651, 1027, 1031, 1032, 1033, 1051], "write_t": [35, 254], "partition_col": [35, 103, 254], "write_to_dataset": [35, 254], "similar": [35, 128, 152, 173, 254, 348, 351, 465, 481, 630, 639, 677, 735, 829, 832, 947, 1051], "spark": [35, 254], "partit": [35, 103, 104, 110, 113, 117, 171, 185, 254], "we": [35, 101, 102, 105, 112, 158, 159, 173, 227, 254, 268, 341, 345, 352, 409, 438, 483, 484, 485, 486, 487, 489, 490, 491, 524, 604, 630, 639, 671, 672, 677, 681, 735, 745, 822, 826, 833, 857, 893, 995, 1051], "use_pyarrow_write_to_dataset": [35, 254], "first": [35, 67, 101, 102, 105, 112, 115, 123, 133, 156, 158, 161, 173, 175, 182, 186, 187, 210, 215, 216, 222, 223, 227, 239, 254, 278, 280, 309, 338, 341, 345, 379, 386, 400, 409, 412, 413, 431, 440, 467, 494, 506, 516, 518, 519, 525, 526, 576, 583, 588, 595, 599, 630, 639, 657, 671, 673, 677, 680, 689, 708, 714, 722, 735, 738, 754, 785, 787, 819, 822, 826, 856, 866, 883, 893, 896, 897, 915, 921, 960, 987, 989, 990, 996, 997, 1021, 1030, 1051, 1059], "watermark": [35, 254], "partitioned_object": [35, 254], "calendar": [37, 38, 158, 159, 173, 227, 254, 329, 341, 345, 352, 356, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 806, 822, 826, 833, 837], "time_unit": [38, 40, 97, 317, 318, 325, 350, 355, 535, 537, 588, 589, 597, 738, 794, 795, 802, 831, 836, 1006, 1008], "time_zon": [38, 97, 319, 344, 352, 537, 588, 589, 738, 793, 796, 801, 825, 833, 1008], "timezon": [38, 738], "m": [38, 40, 55, 172, 254, 316, 317, 318, 324, 325, 348, 350, 351, 355, 516, 535, 536, 537, 540, 588, 589, 597, 676, 735, 738, 793, 794, 795, 801, 802, 829, 831, 832, 836, 987, 1006, 1007, 1008, 1011], "zone": [38, 316, 319, 344, 535, 537, 588, 589, 738, 793, 796, 825, 1006, 1008], "zoneinfo": [38, 738], "run": [38, 47, 48, 73, 125, 127, 133, 157, 174, 187, 223, 236, 254, 268, 309, 409, 480, 481, 619, 639, 657, 662, 664, 670, 681, 685, 690, 700, 708, 735, 738, 745, 785, 893, 946, 947, 1051, 1058, 1059], "available_timezon": [38, 738], "check": [38, 101, 102, 112, 119, 120, 153, 158, 159, 167, 169, 172, 254, 264, 266, 286, 289, 290, 383, 387, 406, 510, 514, 533, 639, 671, 672, 676, 681, 735, 742, 743, 761, 764, 765, 862, 864, 867, 868, 870, 875, 876, 877, 879, 880, 890, 961, 981, 985, 1004, 1051], "128": [39, 69, 934, 1051], "bit": [39, 41, 42, 43, 44, 45, 46, 61, 62, 63, 64, 476, 510, 639, 942, 981, 1051], "neg": [39, 158, 159, 161, 175, 203, 204, 206, 210, 254, 425, 426, 467, 496, 497, 504, 529, 639, 671, 672, 698, 699, 701, 715, 735, 856, 883, 909, 910, 966, 967, 975, 1000, 1021, 1051], "scale": [39, 144, 254, 467, 538, 639, 840, 1009, 1051], "experiment": [39, 117, 200, 225, 226, 231, 254, 309, 345, 483, 484, 485, 486, 487, 489, 490, 491, 639, 696, 710, 713, 735, 785, 826, 857, 938, 1051], "progress": 39, "expect": [39, 82, 84, 89, 268, 568, 604, 639, 679, 681, 735], "32": [41, 44, 62, 69, 159, 169, 254, 457, 498, 639, 672, 735, 789, 823, 934, 953, 1051], "sign": [43, 44, 45, 46, 341, 476, 543, 639, 822, 870, 942, 1014, 1051], "maintain_ord": [47, 48, 134, 157, 185, 187, 221, 223, 227, 235, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 262, 268, 284, 432, 550, 557, 581, 639, 653, 657, 662, 664, 670, 690, 700, 702, 707, 708, 718, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 760, 916, 1036, 1051], "type_coercion": [47, 48, 73, 657, 662, 664, 690, 700, 735], "predicate_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "projection_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "simplify_express": [47, 48, 73, 657, 662, 664, 690, 700, 735], "no_optim": [47, 48, 73, 657, 664, 681, 690, 735], "slice_pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 700, 735], "persist": [47, 48, 57, 735], "larger": [47, 48, 580, 735], "ram": [47, 48, 735], "maintain": [47, 48, 134, 221, 254, 284, 432, 494, 557, 639, 653, 702, 707, 735, 760, 916, 960, 1036, 1051], "slightli": [47, 48, 735], "faster": [47, 48, 146, 217, 225, 254, 268, 482, 523, 558, 639, 735, 744, 745, 846, 948, 994, 1051], "coercion": [47, 48, 73, 477, 639, 657, 662, 664, 690, 700, 735], "optim": [47, 48, 73, 110, 112, 114, 115, 116, 170, 174, 186, 190, 196, 223, 254, 657, 662, 664, 681, 690, 700, 708, 715, 719, 735, 771, 1051], "predic": [47, 48, 73, 112, 114, 115, 116, 117, 149, 169, 195, 254, 369, 562, 595, 639, 657, 662, 664, 667, 681, 690, 693, 700, 715, 735, 850, 962, 963, 1051], "pushdown": [47, 48, 73, 657, 662, 664, 681, 690, 693, 700, 715, 735, 962, 963, 1051], "project": [47, 48, 73, 112, 114, 115, 116, 268, 505, 506, 639, 657, 662, 664, 681, 690, 693, 700, 716, 735], "turn": [47, 48, 73, 101, 102, 112, 541, 560, 639, 657, 662, 664, 681, 690, 735, 1012], "off": [47, 48, 73, 101, 102, 112, 560, 639, 657, 662, 664, 681, 690, 735], "certain": [47, 48, 80, 104, 113, 164, 227, 254, 577, 657, 690, 735, 1051], "slice": [47, 48, 68, 73, 144, 161, 171, 210, 254, 413, 429, 482, 483, 484, 485, 486, 487, 489, 490, 491, 639, 657, 662, 664, 681, 690, 700, 735, 840, 856, 897, 913, 948, 949, 950, 951, 952, 953, 955, 956, 957, 1021, 1051], "lf": [47, 48, 653, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 670, 671, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 685, 687, 688, 689, 690, 691, 693, 694, 695, 696, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 711, 712, 713, 714, 715, 716, 735, 1058, 1059], "scan_csv": [47, 48, 101, 102, 735], "my_larger_than_ram_fil": [47, 48, 735], "data_pagesize_limit": [48, 735], "reduc": [48, 101, 102, 110, 112, 114, 115, 116, 241, 242, 244, 247, 498, 595, 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721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 738, 868, 1051, 1058, 1059], "global": [54, 58, 75, 124, 129, 217, 254, 440, 639, 1058], "scope": [54, 57, 130, 649, 738, 1058], "automat": [54, 57, 90, 92, 93, 94, 96, 97, 101, 102, 103, 108, 109, 112, 124, 126, 128, 200, 231, 254, 292, 440, 522, 528, 534, 639, 696, 713, 735, 921, 993, 999, 1005, 1051, 1058], "map": [54, 55, 92, 93, 101, 102, 106, 107, 110, 111, 112, 114, 133, 143, 151, 170, 191, 195, 196, 197, 213, 254, 345, 352, 439, 440, 465, 481, 560, 568, 639, 661, 693, 735, 787, 826, 833, 921, 947, 1051, 1058], "recent": [54, 466, 639, 934, 1051, 1058], "df1": [54, 56, 57, 58, 67, 74, 75, 119, 146, 153, 180, 218, 229, 254, 686, 735], "x": [54, 55, 67, 74, 97, 101, 102, 103, 112, 122, 124, 144, 163, 166, 168, 172, 179, 187, 196, 197, 254, 261, 263, 264, 265, 266, 268, 310, 311, 358, 359, 373, 376, 377, 378, 398, 405, 414, 435, 437, 438, 439, 455, 457, 461, 462, 464, 468, 469, 471, 477, 481, 500, 503, 531, 546, 547, 554, 556, 563, 565, 567, 568, 571, 572, 583, 584, 586, 595, 604, 605, 607, 611, 615, 624, 639, 676, 681, 685, 701, 735, 738, 745, 849, 879, 971, 974, 1002, 1034, 1051, 1058], "df2": [54, 56, 57, 58, 67, 74, 75, 119, 146, 153, 218, 229, 254], "t": [54, 55, 74, 105, 112, 133, 158, 186, 223, 225, 254, 268, 309, 316, 324, 344, 357, 360, 361, 362, 409, 465, 467, 471, 516, 522, 535, 537, 568, 630, 639, 671, 689, 708, 735, 738, 745, 785, 825, 839, 841, 842, 843, 893, 938, 961, 987, 993, 999, 1006, 1008, 1032, 1041, 1051], "join": [54, 58, 67, 73, 74, 75, 173, 226, 254, 382, 465, 639, 657, 662, 664, 677, 690, 700, 710, 714, 735], "named_fram": [55, 1058], "lf1": [55, 57], "o": [55, 106, 114, 291, 309, 515, 555, 639, 766, 785, 986, 1005, 1035, 1051], "lf2": [55, 57, 735], "p": [55, 69, 186, 254, 467, 622, 639, 689, 735], "q": [55, 197, 254, 310, 471, 622, 639, 938, 1051], "r": [55, 467, 511, 515, 516, 517, 525, 639, 738, 982, 986, 987, 988, 996], "lf3": [55, 735], "lf4": [55, 735], "either": [55, 124, 159, 169, 174, 177, 185, 195, 209, 217, 254, 471, 535, 564, 573, 622, 639, 672, 679, 735, 738, 938, 1006, 1051], "tbl1": [55, 57], "tbl2": [55, 57], "tbl3": 55, "tbl4": 55, "statement": [56, 630], "hello_world": 56, "baz": [56, 164, 165, 187, 224, 254, 530, 532, 675, 709, 735, 738, 1003], "hello_data": 56, "foo_bar": [56, 604], "registr": [57, 650], "lifetim": [57, 130, 649], "context": [57, 58, 128, 183, 237, 254, 262, 268, 292, 303, 369, 409, 448, 505, 506, 560, 568, 581, 594, 597, 603, 616, 619, 638, 639, 649, 650, 714, 720, 735, 893, 1058], "manag": [57, 58, 649, 650, 1058], "often": [57, 130, 158, 159, 254, 407, 477, 639, 671, 672, 735, 891], "want": [57, 93, 133, 146, 183, 254, 268, 298, 299, 300, 352, 369, 438, 440, 481, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 613, 616, 631, 639, 657, 673, 680, 735, 738, 744, 745, 775, 776, 777, 833, 846, 947, 1032, 1051], "df0": [57, 180, 254, 686, 735], "exit": [57, 58, 130, 1058], "construct": [57, 90, 92, 93, 94, 95, 96, 254, 375, 440, 613, 616, 631, 639, 669, 692, 735, 1051], "through": [57, 738, 1051], "tbl0": 57, "remain": [57, 101, 102, 112, 144, 254, 531, 532, 570, 681, 735, 840, 1002, 1003, 1051], "text": [57, 523, 525, 526, 620, 994, 1059], "misc": 57, "testing1234": 57, "test1": 57, "test2": 57, "test3": 57, "temporarili": [58, 128, 130, 158, 159, 254, 671, 672, 735], "cach": [58, 73, 75, 106, 112, 114, 116, 129, 440, 483, 484, 485, 486, 487, 489, 490, 491, 535, 536, 537, 540, 639, 649, 657, 662, 664, 690, 700, 735, 1006, 1007, 1008, 1011], "categori": [58, 75, 215, 254, 294, 295, 310, 471, 639, 769, 770, 786, 857, 938, 1051], "until": [58, 174, 254, 588], "finish": [58, 78, 146, 254, 744, 846, 1051], "invalid": [58, 101, 102, 112, 518, 519, 524, 556, 588, 589, 639, 989, 990, 995], "outermost": 58, "color": [58, 75, 236, 286, 288, 289, 290, 719], "red": [58, 75, 236, 719], "green": [58, 75, 236, 719], "blue": [58, 75, 286, 288, 289, 290], "orang": [58, 75, 137, 237, 238, 240, 241, 242, 244, 246, 247, 254, 720, 721, 723, 724, 725, 727, 729, 730], "uint8": [58, 75, 121, 123, 216, 217, 254, 307, 308, 440, 548, 563, 639, 738, 783, 784, 1020, 1051, 1059], "yellow": [58, 75, 286, 288, 289, 290], "black": [58, 75, 133, 254, 286, 288, 289, 290], "succe": [58, 101, 102, 112], "df_join": [58, 75], "cat": [58, 75, 216, 254, 310, 471, 510, 539, 542, 554, 579, 635, 639, 738, 786, 857, 938, 981, 1010, 1013, 1047, 1051], "u8": [58, 75, 215, 216, 254, 440, 563, 639, 738, 1028, 1051, 1059], "composit": [59, 123, 1059], "schemadict": [59, 90, 92, 93, 94, 95, 96, 112, 199, 254, 622, 681, 695, 719, 735], "struct_seri": [59, 719], "dai": [60, 158, 159, 171, 173, 227, 254, 325, 329, 336, 337, 338, 341, 342, 343, 345, 350, 352, 353, 354, 356, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 590, 591, 639, 671, 672, 677, 735, 738, 818, 819, 822, 823, 826, 833, 835], "unsign": [61, 62, 63, 64, 476, 639, 870, 942, 1051], "could": [65, 78, 142, 158, 254, 293, 583, 595, 639, 660, 671, 735, 767, 1051], "static": [65, 719], "utf": 66, "frametyp": [67, 1058], "joinstrategi": [67, 172, 254, 676, 735], "outer": [67, 74, 172, 254, 676, 735], "descend": [67, 134, 201, 207, 221, 254, 278, 427, 474, 495, 505, 506, 573, 639, 653, 697, 702, 707, 735, 754, 876, 911, 940, 964, 976, 1051], "fill": [67, 74, 135, 147, 148, 204, 225, 254, 285, 305, 308, 367, 368, 374, 382, 483, 484, 486, 490, 497, 521, 527, 543, 596, 613, 616, 631, 639, 665, 666, 699, 714, 735, 848, 849, 860, 932, 949, 950, 952, 955, 956, 957, 967, 992, 998, 1014, 1051], "sort": [67, 68, 119, 123, 134, 158, 159, 173, 180, 186, 187, 201, 221, 227, 239, 248, 254, 278, 295, 369, 465, 495, 506, 560, 562, 573, 639, 653, 662, 671, 672, 677, 686, 689, 690, 697, 700, 707, 722, 731, 735, 738, 754, 770, 876, 964, 1036, 1039, 1051, 1059], "origin": [67, 101, 102, 223, 254, 344, 395, 440, 465, 476, 477, 478, 511, 516, 517, 519, 521, 527, 543, 571, 572, 639, 708, 735, 786, 825, 921, 938, 944, 982, 987, 988, 990, 992, 998, 1014, 1051], "In": [67, 104, 113, 116, 124, 126, 130, 133, 144, 146, 158, 159, 183, 217, 254, 268, 588, 639, 671, 672, 735, 744, 840, 846, 941, 1051], "duplic": [67, 79, 166, 172, 173, 223, 254, 263, 384, 395, 471, 639, 676, 677, 708, 735, 863, 938, 1051], "behaviour": [67, 74, 510, 516, 517, 525, 556, 639, 981, 987, 988, 996], "strategi": [67, 74, 101, 121, 122, 123, 124, 126, 148, 158, 172, 173, 182, 254, 268, 368, 431, 639, 666, 671, 676, 677, 735, 849, 915, 1051], "suitabl": [67, 74, 122, 133, 254, 268, 494, 639, 745, 960, 1051, 1059], "get": [67, 98, 107, 111, 128, 134, 137, 143, 151, 154, 155, 158, 159, 161, 162, 166, 168, 169, 175, 182, 195, 199, 202, 206, 210, 221, 230, 239, 248, 254, 262, 276, 277, 278, 280, 284, 294, 304, 305, 306, 307, 308, 325, 341, 370, 379, 384, 386, 394, 397, 400, 411, 415, 416, 432, 441, 442, 443, 448, 450, 454, 459, 460, 472, 480, 504, 508, 520, 523, 548, 549, 557, 561, 588, 594, 599, 603, 606, 607, 608, 609, 610, 611, 621, 625, 629, 639, 653, 658, 661, 668, 671, 672, 673, 678, 680, 695, 701, 705, 707, 712, 722, 731, 735, 752, 753, 754, 755, 756, 760, 769, 773, 781, 782, 783, 784, 802, 822, 853, 856, 861, 863, 866, 878, 883, 895, 899, 900, 916, 922, 924, 925, 927, 930, 931, 935, 936, 939, 946, 975, 978, 991, 994, 1021, 1027, 1028, 1036, 1040, 1041, 1051], "speedup": [67, 133, 170, 254, 268, 639, 745, 1051], "receiv": [67, 112, 133, 186, 254, 467, 639, 689, 735, 1059], "now": [67, 159, 254, 292, 471, 535, 537, 639, 672, 735, 1006, 1008], "One": [67, 139, 183, 187, 254, 265, 464, 620, 639, 738, 787, 1051], "whose": [67, 173, 187, 254, 363, 593, 639, 677, 735], "uniqu": [67, 121, 122, 123, 126, 168, 172, 183, 197, 245, 254, 269, 280, 310, 386, 394, 458, 471, 535, 536, 537, 540, 558, 560, 569, 612, 639, 676, 728, 735, 756, 786, 787, 866, 878, 928, 938, 1006, 1007, 1008, 1011, 1037, 1039, 1051], "post": 67, "constrain": 67, "newli": 67, "boolean": [67, 134, 149, 152, 174, 207, 221, 254, 264, 265, 266, 286, 289, 290, 328, 369, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 401, 402, 406, 464, 506, 518, 562, 563, 573, 574, 622, 630, 639, 653, 656, 667, 679, 702, 707, 735, 738, 742, 743, 755, 761, 763, 805, 850, 861, 862, 863, 865, 866, 868, 869, 871, 872, 873, 874, 878, 885, 886, 890, 935, 936, 962, 981, 1032, 1042, 1051], "know": [67, 431, 523, 994, 1032, 1041, 1051], "2022": [67, 139, 156, 158, 159, 173, 227, 254, 318, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 535, 588, 589, 591, 597, 639, 671, 672, 677, 735, 738, 795, 806, 822, 826, 833, 1006], "y": [67, 74, 97, 122, 124, 144, 166, 168, 172, 179, 187, 196, 197, 254, 261, 263, 264, 265, 266, 348, 351, 358, 359, 376, 377, 398, 405, 414, 437, 439, 461, 462, 464, 469, 477, 481, 535, 536, 537, 547, 556, 563, 565, 567, 571, 572, 586, 604, 607, 611, 624, 639, 676, 685, 701, 735, 738, 829, 832, 1006, 1007, 1008], "df3": [67, 254], "set_tbl_format": 67, "09": [67, 124, 159, 254, 318, 329, 337, 338, 538, 628, 672, 735, 799, 1009], "01": [67, 124, 139, 156, 158, 159, 227, 254, 316, 317, 318, 319, 323, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 355, 356, 483, 484, 486, 489, 490, 491, 535, 536, 537, 540, 588, 589, 591, 639, 671, 672, 735, 738, 794, 795, 796, 797, 798, 799, 800, 802, 803, 804, 805, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 1006, 1007, 1008, 1011], "02": [67, 156, 158, 159, 173, 227, 254, 317, 318, 325, 326, 327, 334, 335, 336, 337, 338, 341, 344, 345, 346, 347, 350, 352, 353, 355, 483, 484, 485, 486, 487, 489, 490, 491, 492, 535, 536, 537, 540, 588, 589, 591, 639, 671, 672, 677, 735, 738, 794, 795, 797, 798, 802, 803, 804, 807, 808, 809, 810, 812, 814, 815, 816, 817, 818, 819, 820, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 836, 915, 1006, 1007, 1008, 1011], "03": [67, 97, 158, 159, 227, 254, 317, 318, 319, 323, 325, 327, 335, 336, 337, 338, 344, 345, 346, 347, 348, 350, 351, 352, 353, 355, 483, 484, 486, 489, 490, 491, 536, 540, 588, 589, 639, 671, 672, 735, 794, 795, 796, 799, 800, 802, 803, 804, 807, 808, 809, 810, 812, 814, 816, 817, 818, 819, 820, 823, 824, 825, 826, 827, 828, 829, 831, 832, 834, 835, 836, 1007, 1011], "af1": 67, "af2": 67, "af3": 67, "keep": [67, 101, 102, 112, 223, 226, 227, 254, 395, 440, 576, 639, 708, 710, 735, 921, 1051], "easili": [67, 200, 231, 234, 254, 577, 622, 696, 713, 717, 735], "dot": [67, 639, 700, 735, 1051], "product": [67, 138, 254, 307, 313, 568, 639, 783, 789, 1051], "fill_nul": [67, 147, 254, 639, 665, 714, 735, 1051], "sum_horizont": [67, 623], "167": 67, "47": 67, "callabl": [68, 69, 70, 71, 112, 133, 152, 186, 236, 254, 268, 431, 438, 439, 467, 482, 568, 583, 584, 595, 605, 615, 639, 681, 689, 719, 735, 745, 915, 948, 1051], "decor": [68, 69, 70, 71, 124, 126, 133, 254, 268, 639, 649, 650, 745, 1051], "under": [68, 69, 70, 71, 632, 633, 635, 640, 641, 645, 646, 647, 1043, 1045, 1047, 1052, 1055, 1056, 1057], "access": [68, 69, 70, 71, 170, 195, 196, 254, 440, 639, 714, 735, 1059], "by_first_letter_of_column_nam": 68, "f": [68, 102, 139, 156, 171, 212, 222, 225, 254, 261, 268, 431, 498, 515, 535, 537, 568, 578, 605, 639, 915, 986, 1006, 1008], "fromkei": [68, 70], "by_first_letter_of_column_valu": 68, "starts_with": [68, 286, 289, 510, 514, 738, 985], "to_seri": [68, 154, 254, 535, 574, 616, 1006], "xx": [68, 70, 123, 126, 738], "xy": [68, 70], "yy": [68, 70, 123, 126, 738], "yz": [68, 70], "b1": [68, 70], "b2": [68, 70], "pow_n": 69, "powersofn": 69, "next": [69, 158, 159, 173, 227, 254, 285, 341, 345, 352, 474, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 822, 826, 833, 940, 1051], "ceil": [69, 639, 1051], "previou": [69, 130, 466, 469, 545, 547, 639, 744, 934, 1051], "floor": [69, 639, 1051], "nearest": [69, 173, 189, 246, 254, 297, 372, 382, 472, 487, 614, 639, 677, 691, 729, 735, 772, 852, 860, 939, 953, 1051], "24": [69, 118, 133, 144, 158, 159, 173, 227, 254, 307, 309, 322, 327, 341, 342, 345, 352, 354, 457, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 785, 804, 822, 826, 833, 880, 1051], "001": [69, 330, 331, 333, 340, 591, 811, 813, 821], "next_pow2": 69, "prev_pow2": 69, "nearest_pow2": 69, "split_by_column_dtyp": 70, "collect_al": 70, "31": [71, 124, 318, 322, 323, 336, 337, 341, 353, 535, 587, 588, 590, 597, 604, 738, 793, 795, 799, 800, 818, 822, 1006, 1059], "42": [71, 159, 160, 180, 254, 672, 686, 735, 855, 1051], "961": 71, "1764": 71, "4160": 71, "build": [72, 91, 738, 1059], "wa": 72, "compil": [72, 403, 404, 887, 888], "gate": 72, "info": [72, 104, 113, 118], "otherwis": [72, 123, 128, 133, 180, 197, 200, 236, 254, 268, 287, 298, 299, 300, 344, 352, 360, 361, 362, 431, 467, 476, 510, 512, 564, 566, 568, 585, 606, 610, 623, 630, 639, 686, 696, 719, 735, 745, 762, 775, 776, 777, 825, 833, 841, 842, 843, 915, 942, 962, 963, 981, 983, 1051], "depend": [72, 118, 268, 403, 404, 448, 471, 474, 568, 594, 597, 603, 639, 887, 888, 938, 940, 1051, 1059], "host": [72, 101, 106, 110, 114, 116], "git": 72, "lazy_fram": 73, "comm_subplan_elim": [73, 657, 662, 664, 690, 700, 735], "comm_subexpr_elim": [73, 292, 639, 657, 662, 664, 690, 700, 735], "graph": [73, 174, 254, 674, 714, 735], "parallel": [73, 74, 99, 103, 110, 116, 158, 173, 174, 186, 254, 309, 409, 639, 650, 671, 676, 677, 719, 735, 785, 893, 1051], "threadpool": [73, 128], "Will": [73, 657, 662, 664, 690, 700, 735, 1051], "try": [73, 85, 87, 101, 102, 105, 106, 110, 112, 114, 116, 657, 662, 664, 690, 700, 735], "branch": [73, 657, 662, 664, 690, 700, 735], "subplan": [73, 657, 662, 664, 690, 700, 735], "union": [73, 74, 424, 657, 662, 664, 690, 700, 735, 738, 908], "subexpress": [73, 657, 662, 664, 690, 700, 735], "reus": [73, 657, 662, 664, 690, 700, 735], "part": [73, 90, 124, 517, 531, 532, 657, 662, 664, 690, 700, 714, 735, 988, 1002, 1003], "fashion": [73, 172, 254, 657, 662, 664, 690, 700, 735], "item": [74, 102, 195, 198, 254, 365, 406, 412, 414, 493, 532, 639, 845, 890, 896, 898, 959, 1003, 1051], "iter": [74, 125, 127, 134, 157, 158, 159, 170, 171, 185, 195, 196, 197, 200, 201, 207, 221, 222, 231, 233, 234, 254, 309, 363, 465, 506, 564, 565, 566, 567, 573, 576, 577, 578, 579, 585, 586, 593, 606, 607, 610, 611, 619, 622, 623, 624, 639, 653, 670, 671, 672, 696, 697, 702, 707, 713, 717, 735, 785, 1051, 1059], "polarstyp": 74, "concatmethod": 74, "vertic": [74, 146, 225, 229, 254, 509, 980], "rechunk": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 146, 254, 639, 744, 773, 846, 927, 1051], "combin": [74, 85, 87, 158, 159, 160, 173, 227, 254, 265, 279, 341, 352, 464, 522, 528, 534, 588, 639, 671, 672, 677, 700, 735, 822, 826, 833, 993, 999, 1005], "concaten": [74, 152, 186, 254, 467, 578, 579, 639, 689, 735, 773, 927, 1051], "diagon": [74, 222, 254], "vstack": [74, 146, 254], "vertical_relax": 74, "coerc": [74, 477, 639], "equal": [74, 75, 101, 102, 112, 119, 120, 134, 153, 158, 173, 180, 221, 254, 292, 358, 359, 376, 398, 431, 462, 477, 482, 483, 484, 485, 486, 487, 489, 490, 491, 520, 521, 527, 543, 617, 618, 639, 653, 671, 677, 686, 702, 707, 735, 915, 948, 949, 950, 951, 952, 953, 955, 956, 957, 961, 991, 992, 998, 1014, 1051, 1059], "supertyp": [74, 148, 254, 267, 639, 666, 735], "find": [74, 150, 254, 494, 639, 960, 1051], "miss": [74, 101, 102, 112, 147, 254, 285, 360, 361, 362, 374, 389, 391, 639, 665, 735, 841, 842, 843, 1041, 1051], "stack": [74, 163, 229, 254], "don": [74, 133, 223, 225, 254, 268, 309, 409, 465, 471, 568, 639, 708, 735, 738, 745, 785, 893, 938, 961, 1041, 1051], "auto": [74, 90, 92, 93, 94, 96, 108, 109, 110, 116, 222, 254, 735, 1059], "logic": [74, 133, 236, 254, 268, 554, 568, 639, 669, 676, 692, 716, 719, 735, 745, 1034, 1051], "align_fram": 74, "pattern": [74, 101, 102, 112, 114, 115, 116, 170, 254, 446, 510, 511, 516, 517, 525, 526, 719, 738, 962, 963, 981, 982, 987, 988, 996, 997, 1051], "collis": 74, "need": [74, 97, 101, 102, 103, 105, 119, 120, 158, 159, 197, 205, 217, 254, 431, 440, 498, 520, 538, 613, 616, 631, 639, 671, 672, 735, 968, 991, 1009, 1051], "sure": [74, 90, 95, 101, 102, 106, 110, 158, 159, 190, 254, 671, 672, 735], "contigu": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 190, 254], "relev": 74, "df_h1": 74, "l1": 74, "l2": 74, "df_h2": 74, "r1": 74, "r2": 74, "r3": 74, "df_d1": 74, "df_d2": 74, "df_a1": 74, "df_a2": 74, "df_a3": 74, "disabl": [75, 129, 170, 254], "encount": [76, 158, 254, 459, 460, 518, 519, 580, 583, 595, 639, 671, 735, 930, 931, 989, 990, 1051], "least": [82, 124, 466, 560, 639, 934, 1039, 1051], "unexpect": [83, 254, 268, 438, 639, 745, 1051], "caus": [83, 91, 101, 102, 112, 132, 146, 254, 744, 846, 1051], "panic": 83, "mismatch": [85, 109], "incompat": 87, "pa": [90, 117], "chunkedarrai": [90, 182, 254, 789, 1051], "recordbatch": [90, 171, 254], "schemadefinit": [90, 92, 93, 94, 96, 108, 109, 254, 735], "schema_overrid": [90, 92, 93, 94, 95, 96, 108, 109, 171, 217, 254, 284, 735, 738, 760], "copi": [90, 91, 132, 135, 136, 171, 212, 217, 218, 231, 254, 366, 543, 639, 655, 656, 713, 735, 774, 778, 791, 847, 1014, 1027, 1032, 1033, 1051], "closest": 90, "pair": [90, 92, 93, 94, 96, 108, 109, 123, 191, 254, 693, 735, 1059], "sever": [90, 92, 93, 94, 96, 108, 109, 254, 735, 1059], "wai": [90, 92, 93, 94, 96, 108, 109, 140, 157, 171, 186, 207, 234, 254, 465, 467, 506, 516, 577, 639, 659, 670, 689, 702, 717, 719, 735, 987], "form": [90, 92, 93, 94, 96, 108, 109, 170, 196, 225, 254, 466, 639, 735, 934, 1051], "them": [90, 92, 93, 94, 96, 108, 109, 112, 146, 158, 159, 173, 180, 227, 254, 383, 414, 459, 460, 465, 578, 639, 671, 672, 677, 686, 735, 738, 744, 846, 898, 930, 931, 1051], "dimens": [90, 92, 94, 96, 108, 109, 254, 478, 639, 735, 944, 1051], "allow_copi": [91, 132], "interchang": [91, 132], "__dataframe__": 91, "convers": [91, 132, 170, 171, 196, 197, 214, 218, 254, 535, 536, 537, 540, 588, 650, 1006, 1007, 1008, 1011, 1031, 1032, 1033, 1051], "detail": [91, 103, 119, 120, 132, 254, 735, 1059], "latest": [91, 104, 113, 132, 344, 352, 374, 449, 639, 825, 833], "runtimeerror": 91, "from_panda": [91, 105], "from_arrow": 91, "effici": [91, 171, 254], "clone": [92, 93, 94, 95, 96, 135, 217, 218, 254, 655, 735, 774, 1031, 1032, 1033, 1041, 1051], "dimension": [92, 94, 96, 217, 254, 735, 1051], "infer_schema_length": [93, 96, 101, 102, 105, 112, 115, 254, 518, 735, 989], "NOT": [93, 119, 120, 447, 1058], "typic": [93, 133, 254, 324, 738, 745, 801, 1051], "clearer": 93, "load": [93, 95, 104, 113, 125, 127, 254, 650, 673, 680, 735, 1059], "_partial_": [93, 254, 735], "omit": [93, 97, 122, 124, 126, 130, 183, 197, 254, 627, 628, 738], "mani": [93, 96, 103, 146, 254, 518, 744, 846, 989, 1051], "scan": [93, 96, 101, 102, 110, 112, 113, 114, 115, 116, 117, 158, 159, 254, 664, 671, 672, 673, 680, 735], "slow": [93, 96, 101, 102, 112, 268, 309, 639, 719, 785, 1051], "partial": 93, "present": [93, 119, 124, 387, 639, 1041, 1051], "np": [94, 149, 217, 254, 550, 639, 735, 865, 869, 871, 872, 948, 1022, 1051], "ndarrai": [94, 149, 217, 254, 550, 639, 735, 789, 960, 963, 1022, 1032, 1051], "numpi": [94, 118, 138, 170, 196, 197, 214, 217, 218, 254, 459, 460, 639, 735, 865, 869, 871, 872, 930, 931, 948, 1032, 1033, 1041, 1051], "columnar": [94, 96, 170, 196, 254], "interpret": [94, 96, 101, 102, 112, 254, 735], "yield": [94, 96, 101, 102, 112, 144, 146, 222, 254, 465, 639, 735, 744, 840, 846, 1051], "conclus": [94, 96, 254, 735], "nan_to_nul": [95, 254, 735, 1051], "include_index": 95, "pd": [95, 105, 554, 639, 1033, 1034, 1051], "panda": [95, 105, 118, 158, 218, 254, 337, 338, 554, 639, 671, 735, 818, 819, 1033, 1034, 1051], "instal": [95, 101, 102, 103, 106, 110, 118, 138, 217, 218, 254, 700, 735, 1033, 1051], "nan": [95, 119, 120, 124, 132, 147, 218, 254, 314, 315, 358, 359, 367, 376, 377, 382, 389, 391, 392, 393, 398, 437, 441, 454, 459, 460, 461, 462, 556, 580, 639, 665, 735, 747, 751, 790, 848, 871, 872, 930, 931, 948, 1032, 1033, 1051, 1059], "convert": [95, 104, 105, 113, 132, 213, 214, 215, 216, 217, 220, 254, 311, 319, 348, 351, 431, 439, 473, 524, 535, 536, 537, 538, 540, 639, 735, 796, 829, 832, 915, 995, 1006, 1007, 1008, 1009, 1011, 1019, 1030, 1031, 1032, 1033, 1041, 1051], "pd_df": 95, "pd_seri": 95, "tbl": [97, 99, 102], "reconstruct": 97, "repr": [97, 124, 126], "trim": 97, "whitespac": [97, 522, 528, 534, 993, 999, 1005], "prompt": 97, "extract": [97, 320, 322, 323, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 339, 340, 342, 343, 346, 347, 349, 353, 354, 356, 467, 517, 518, 519, 639, 797, 798, 799, 800, 803, 804, 806, 810, 811, 812, 813, 815, 816, 817, 820, 821, 823, 824, 827, 828, 830, 834, 835, 837, 988, 989, 990], "to_init_repr": [97, 254, 1051], "truncat": [97, 158, 170, 196, 197, 214, 254, 341, 671, 690, 735, 822], "identifi": [97, 179, 185, 223, 254, 685, 708, 735], "compound": [97, 197, 254, 738], "struct": [97, 183, 200, 220, 224, 231, 254, 310, 431, 440, 471, 480, 481, 518, 531, 532, 560, 583, 584, 586, 605, 639, 696, 709, 713, 719, 735, 786, 915, 938, 946, 989, 1000, 1002, 1003, 1051], "neither": [97, 105, 198, 254, 431, 915], "source_ac": 97, "source_cha": 97, "ident": [97, 135, 136, 254, 348, 480, 481, 639, 655, 656, 735, 774, 778, 829, 946, 947, 1051], "timestamp": [97, 344, 597, 825], "tor_id": 97, "nnel_id": 97, "\u03bc": [97, 124, 158, 159, 170, 173, 196, 197, 214, 227, 254, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 333, 334, 335, 336, 337, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348, 350, 351, 352, 353, 354, 355, 356, 483, 484, 486, 489, 490, 491, 535, 537, 588, 591, 597, 639, 671, 672, 677, 735, 738, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 836, 837, 1006, 1008], "asia": [97, 738, 797, 798, 830], "tokyo": [97, 738], "123456780": 97, "9876543210": 97, "56": [97, 552, 639], "59": [97, 123, 318, 334, 346, 590, 626, 627, 628, 738, 795, 815, 827], "663053": 97, "jst": [97, 738], "803065983": 97, "2055938745": 97, "38": [97, 124], "18": [97, 159, 180, 254, 292, 308, 345, 352, 355, 382, 395, 535, 537, 570, 588, 627, 639, 672, 686, 690, 735, 738, 744, 836, 1006, 1008, 1051], "050545": 97, "source_actor_id": 97, "source_channel_id": 97, "sr": 97, "to_list": [97, 159, 254, 672, 735, 981, 1051], "datatypeclass": 98, "uint32": [98, 118, 144, 254, 278, 322, 326, 330, 332, 334, 336, 339, 342, 343, 346, 353, 354, 403, 404, 416, 477, 511, 520, 523, 550, 554, 639, 738, 755, 767, 799, 803, 810, 812, 815, 817, 820, 823, 824, 827, 834, 835, 840, 870, 887, 888, 900, 961, 982, 991, 994, 1034, 1051], "regular": [98, 170, 195, 196, 227, 254, 363, 510, 511, 516, 517, 525, 526, 577, 593, 606, 610, 623, 639, 738, 981, 982, 987, 988, 996, 997], "uint64": [98, 160, 254, 378, 403, 404, 476, 639, 855, 887, 888, 942, 1051], "bigidx": 98, "read": 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702, 707, 713, 717, 735, 861, 1051], "stop": [100, 101, 102, 106, 110, 112, 114, 115, 116, 322, 325, 326, 328, 329, 334, 336, 342, 343, 345, 346, 350, 352, 353, 354, 356, 483, 484, 486, 489, 490, 491, 639, 794, 796, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 836, 837], "textio": 101, "new_column": [101, 102, 105, 112, 192, 254], "comment_char": [101, 102, 112], "quote_char": [101, 102, 112], "skip_row": [101, 102, 112], "missing_utf8_is_empty_str": [101, 102, 112], "ignore_error": [101, 102, 109, 112], "n_thread": [101, 102], "8192": 101, "csvencod": [101, 102, 112], "low_memori": [101, 102, 110, 112, 115, 116], "skip_rows_after_head": [101, 102, 112], "row_count_nam": [101, 102, 106, 110, 112, 114, 115, 116], "row_count_offset": [101, 102, 106, 110, 112, 114, 115, 116], "sample_s": [101, 102], "eol_char": [101, 102, 112], "handler": [101, 102, 105], "g": [101, 102, 104, 105, 106, 110, 113, 114, 116, 158, 159, 173, 217, 225, 227, 254, 261, 268, 310, 341, 345, 352, 363, 471, 483, 484, 485, 486, 487, 489, 490, 491, 498, 593, 639, 671, 672, 677, 693, 735, 822, 826, 833, 961, 1032, 1051], "builtin": [101, 102, 105], "stringio": [101, 102], "fsspec": [101, 102, 106, 110, 113, 114, 116, 118], "remot": [101, 102, 106, 110], "autogener": [101, 102, 112], "column_x": [101, 102, 112], "enumer": [101, 102, 112, 171, 254], "shorter": [101, 102], "comment": [101, 102, 112], "instanc": [101, 102, 112, 124, 126, 130, 146, 152, 254, 293, 449, 483, 484, 485, 486, 487, 489, 490, 491, 583, 595, 639, 681, 715, 735, 744, 767, 846, 1051], "escap": [101, 102, 112], "dure": [101, 102, 112, 130, 146, 254, 744, 846, 1051], "would": [101, 102, 112, 278, 412, 448, 474, 573, 588, 639, 738, 754, 896, 940, 1032, 1051], "prefer": [101, 102, 104, 112, 127, 133, 146, 170, 195, 196, 236, 254, 268, 482, 639, 735, 744, 745, 846, 948, 1051, 1059], "treat": [101, 102, 112, 510, 525, 526, 981, 996, 997], "10000": [101, 171, 254, 543], "might": [101, 102, 112, 128, 134, 217, 221, 254, 268, 558, 639, 653, 702, 707, 735, 1033, 1051], "issu": [101, 102, 105, 112, 307, 308, 548, 639, 783, 784, 1020, 1051], "iso8601": [101, 102, 112], "physic": [101, 102, 173, 254, 295, 554, 639, 654, 676, 677, 735, 770, 1034, 1051], "cpu": [101, 102], "system": [101, 102], "wrongli": 101, "done": [101, 102, 112, 117, 156, 173, 254, 267, 269, 293, 465, 521, 527, 569, 639, 677, 735, 767, 992, 998, 1051], "buffer": [101, 102, 144, 170, 254, 840, 1051], "modifi": [101, 102, 112, 128, 130, 146, 163, 170, 229, 254, 279, 344, 355, 510, 516, 517, 525, 639, 744, 825, 836, 846, 943, 981, 987, 988, 996, 1010, 1012, 1013, 1051], "upper": [101, 102, 144, 158, 254, 298, 299, 383, 431, 559, 570, 588, 589, 601, 602, 627, 628, 639, 671, 735, 776, 840, 861, 920, 1038, 1051], "bound": [101, 102, 144, 158, 254, 298, 299, 300, 383, 412, 430, 431, 436, 559, 570, 588, 589, 601, 602, 627, 628, 639, 671, 735, 776, 777, 840, 861, 896, 914, 920, 1038, 1051], "lossi": [101, 102, 112], "decod": 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106, 110, 114, 116], "password": [101, 103, 106, 110, 114, 116], "skip": [101, 102, 105, 110, 112, 116, 225, 254, 745, 1051], "offset": [101, 102, 106, 110, 112, 114, 115, 116, 158, 159, 206, 227, 232, 254, 316, 324, 337, 338, 341, 345, 352, 426, 504, 529, 535, 537, 588, 589, 639, 671, 672, 701, 715, 735, 793, 801, 818, 819, 822, 826, 833, 910, 975, 1000, 1006, 1008, 1051], "row_count": [101, 102, 106, 110, 112, 114, 115, 116], "sampl": [101, 102, 236, 254, 503, 639, 719, 974, 1051], "estim": [101, 102, 144, 254, 269, 361, 362, 396, 569, 639, 735, 840, 842, 843, 881, 1051], "alloc": [101, 102, 144, 190, 254, 840, 1051], "lazili": [101, 102, 112, 113, 114, 115, 116, 440, 639], "glob": [101, 102, 112, 114, 115, 116], "continu": [101, 110, 310, 471, 503, 639, 786, 938, 974, 1051], "benchmark": [101, 110], "50000": 102, "batchedcsvread": [102, 650], "upon": 102, "creation": 102, "gather": 102, "next_batch": 102, "big": 102, "interest": 102, "seen_group": 102, "big_fil": 102, "df_current_batch": 102, "concat": [102, 773, 927, 1051], "partition_df": 102, "partition_bi": [102, 171, 254], "as_dict": [102, 185, 254], "fh": 102, "write_csv": [102, 112, 254], "els": [102, 630], "partition_on": 103, "partition_rang": 103, "partition_num": 103, "dbreadengin": 103, "raw": 103, "connectorx": [103, 118], "driver": 103, "snowflak": 103, "warehous": 103, "role": 103, "transfer": 103, "document": [103, 105, 348, 351, 519, 535, 536, 537, 540, 829, 832, 990, 1006, 1007, 1008, 1011], "redshift": 103, "mysql": 103, "mariadb": 103, "clickhous": 103, "oracl": 103, "bigqueri": 103, "pleas": [103, 158, 254, 671, 735], "doc": [103, 138, 254], "github": 103, "sfu": 103, "connector": 103, "destin": 103, "small": [103, 123, 174, 254, 345, 664, 735, 1059], "still": 103, "develop": [103, 124, 126], "explicitli": [103, 122, 124, 130, 440, 622, 639, 649], "test_tabl": 103, "compani": 103, "testdb": 103, "public": [103, 254, 639, 650, 735, 1051], "myrol": 103, "delta_table_opt": [104, 113], "root": [104, 113, 296, 395, 439, 450, 469, 507, 547, 638, 639, 771, 977, 1051], "absolut": [104, 113, 119, 120, 260, 360, 361, 362, 639, 740, 841, 842, 843, 1051], "sinc": [104, 113, 134, 221, 254, 292, 325, 535, 537, 570, 588, 597, 616, 627, 639, 653, 702, 707, 735, 744, 802, 1006, 1008, 1051], "avoid": [104, 196, 254, 263, 639], "year": [104, 113, 158, 159, 173, 227, 254, 328, 329, 341, 342, 345, 352, 353, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 590, 639, 671, 672, 677, 735, 805, 806, 822, 823, 826, 833, 834], "2021": [104, 113, 139, 156, 158, 227, 254, 535, 604, 671, 735, 738, 797, 798, 806, 830, 877, 1006, 1051], "aw": [104, 113], "googl": [104, 113], "service_account": [104, 113], "service_account_json_absolute_path": [104, 113], "az": [104, 113], "adl": [104, 113], "abf": [104, 113], "azure_storage_account_nam": [104, 113], "azure_storage_account_kei": [104, 113], "without_fil": [104, 113], "track": [104, 113, 133, 254, 431, 1058], "sheet_id": 105, "sheet_nam": 105, "xlsx2csv_option": 105, "read_csv_opt": 105, "noreturn": 105, "xlsx2csv": [105, 118], "read_csv": [105, 112], "nor": [105, 198, 254], "skip_empty_lin": 105, "my": [105, 117, 541, 1012], "datasheet": 105, "correct": [105, 361, 362, 396, 488, 503, 639, 681, 735, 842, 843, 881, 954, 974, 1051], "look": [105, 286, 431, 761], "whole": [105, 505, 506, 516, 639, 719, 735, 987], "With": [105, 133, 169, 254, 268, 483, 484, 485, 486, 487, 489, 490, 491, 639, 745, 880, 1051], "1000": [105, 144, 216, 254, 330, 331, 543, 811, 1030, 1051, 1059], "spreadsheet": [105, 187, 254], "xl": 105, "xlsm": 105, "xlsb": 105, "odf": 105, "od": 105, "odt": 105, "memory_map": [106, 110, 114], "v2": [106, 114], "greatli": [106, 114], "repeat": [106, 114, 310, 471, 475, 477, 613, 631, 639], "give": [106, 110, 114, 115, 116, 179, 223, 254, 325, 396, 477, 556, 639, 650, 685, 708, 735, 802, 881, 1051], "That": [106, 681, 735], "filenam": 106, "my_fil": 106, "write_ipc": [106, 254], "read_ndjson": 108, "becaus": 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352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "hard": [112, 681, 735], "mydf": 112, "lambda": [112, 133, 152, 186, 236, 254, 268, 360, 361, 362, 431, 438, 439, 482, 568, 583, 584, 595, 605, 615, 639, 681, 689, 719, 735, 745, 841, 842, 843, 915, 1051], "lower": [112, 158, 189, 246, 254, 298, 300, 383, 436, 439, 472, 476, 487, 570, 588, 589, 601, 602, 614, 627, 628, 639, 664, 671, 691, 729, 735, 777, 861, 920, 939, 942, 953, 1038, 1051], "simpli": [112, 465, 583, 595, 639], "idx": [112, 158, 171, 254, 431, 671, 735, 915, 963, 1051], "uint16": [112, 307, 308, 548, 639, 738, 783, 784, 1020, 1051], "u16": [112, 124, 1059], "eu": 113, "central": [113, 396, 503, 639, 881, 974, 1051], "allow_pyarrow_filt": 117, "comparison": 117, "dset": 117, "folder": 117, "05": [117, 119, 120, 124, 173, 227, 254, 318, 319, 323, 344, 346, 348, 351, 588, 677, 735, 738, 795, 796, 797, 798, 799, 800, 825, 826, 829, 830, 832, 833, 835], "04": [117, 158, 227, 254, 318, 319, 323, 327, 334, 335, 336, 337, 338, 344, 345, 346, 347, 348, 351, 353, 355, 535, 588, 591, 671, 735, 738, 796, 800, 804, 810, 812, 815, 816, 817, 818, 819, 820, 824, 825, 827, 828, 829, 832, 834, 835, 836, 1006], "stdout": [118, 130, 156, 254, 451], "17": [118, 124, 234, 492, 597, 616, 627, 639, 717, 779, 826, 1051, 1059], "platform": 118, "linux": 118, "90": [118, 538, 1009], "wsl2": 118, "x86_64": 118, "glibc2": 118, "main": 118, "apr": 118, "14": [118, 124, 133, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 316, 329, 382, 474, 627, 639, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 738, 793, 826], "44": [118, 180, 254, 313, 639, 686, 735], "51": 118, "gcc": 118, "matplotlib": [118, 690, 700, 735], "check_dtyp": [119, 120], "check_exact": [119, 120], "rtol": [119, 120], "1e": [119, 120], "atol": [119, 120], "08": [119, 120, 124, 159, 254, 318, 345, 535, 588, 591, 672, 735, 738, 826, 833, 1006], "nans_compare_equ": [119, 120], "check_column_ord": 119, "check_row_ord": 119, "assertionerror": [119, 120], "compar": [119, 120, 153, 254, 358, 359, 376, 377, 398, 437, 461, 462, 639, 961, 1051], "exactli": [119, 120, 123, 124, 126, 531, 532, 969, 1002, 1003, 1051], "toler": [119, 120, 173, 254, 677, 735], "inexact": [119, 120], "assert": [119, 120, 122, 124, 126, 159, 254, 650, 672, 735, 738, 854, 1051], "irrespect": 119, "unsort": 119, "check_nam": 120, "s1": [120, 152, 254, 880, 915, 1033, 1042, 1051], "searchstrategi": [121, 123, 124, 126], "null_prob": [121, 124, 126], "percentag": [121, 124, 126, 466, 639, 934, 1051], "chanc": [121, 124, 126, 1059], "independ": [121, 122, 124, 126], "flag": [121, 254, 495, 510, 516, 517, 525, 639, 662, 681, 735, 964, 981, 987, 988, 996, 1051], "hypothesi": [121, 122, 123, 124, 125, 126, 1059], "sampled_from": [121, 1059], "unique_small_int": 121, "ccy": [121, 1059], "gbp": [121, 1059], "eur": [121, 139, 156, 254, 1059], "jpy": [121, 1059], "min_col": [122, 124], "max_col": [122, 124], "standalon": [122, 124], "mincol": 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"n_chunk": [124, 126, 254, 744, 846, 1051], "randomis": 124, "onto": 124, "pct": 124, "preced": [124, 738], "disallow": [124, 126], "inf": [124, 126, 275, 310, 385, 388, 471, 556, 639, 751, 786, 857, 865, 869, 920, 934, 938, 1038, 1051], "exclud": [124, 126, 305, 308, 383, 577, 639, 861, 877, 1051], "deploi": [124, 126], "characterist": [124, 126], "concret": [124, 126], "test_repr": 124, "isinst": [124, 126, 254], "0x11f561580": 124, "known": [124, 431, 719, 915], "0565": 124, "34715": 124, "5844": 124, "33": [124, 180, 254, 492, 577, 639, 686, 735, 953, 1051], "076854": 124, "3382": 124, "48662": 124, "7540": 124, "29": [124, 158, 159, 173, 227, 254, 316, 337, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 793, 818, 822, 826, 833], "836271": 124, "4063": 124, "06": [124, 227, 254, 318, 329, 343, 344, 356, 738, 825, 826, 835], "39092": 124, "1889": 124, "13": [124, 135, 136, 147, 148, 155, 159, 164, 174, 182, 231, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 263, 482, 538, 578, 583, 586, 595, 624, 629, 639, 655, 656, 665, 666, 672, 679, 713, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 745, 826, 833, 834, 1009, 1051], "41": [124, 490, 639, 1059], "874455": 124, "15836": 124, "1755e": 124, "575050513": 124, "profil": [125, 127, 735], "balanc": [125, 127, 1059], "set_environ": 125, "polars_hypothesis_profil": [125, 127], "1500": 125, "constructor": 126, "normal": [126, 158, 254, 357, 396, 503, 639, 671, 735, 839, 881, 974, 1051], "test_repr_is_valid_str": 126, "experi": 126, "create_list_strategi": [126, 1059], "polars_max_thread": 128, "behind": 128, "lock": 128, "reason": 128, "pyspark": 128, "udf": [128, 133, 186, 236, 254, 268, 467, 568, 639, 689, 719, 735, 745, 1051], "recommend": [128, 170, 186, 254, 588, 738], "initi": [130, 583, 595, 630], "whatev": 130, "were": 130, "enter": 130, "advantag": [130, 186, 254], "initialis": [130, 735, 1058], "set_": 130, "set_verbos": 130, "do_various_th": 130, "restor": 130, "cleaner": 130, "breviti": 130, "vein": 130, "durat": [130, 173, 227, 254, 316, 323, 324, 327, 331, 333, 335, 340, 347, 352, 355, 554, 588, 589, 627, 628, 639, 677, 735, 738, 793, 800, 801, 804, 811, 813, 816, 821, 828, 833, 836, 1034, 1051], "set_ascii_t": 130, "write_ascii_frame_to_stdout": 130, "sy": 130, "nan_as_nul": 132, "_pyarrowdatafram": 132, "nullabl": 132, "extens": [132, 218, 254, 1033, 1051], "propag": [132, 177, 209, 254, 359, 459, 460, 462, 639, 930, 931, 1051], "inference_s": [133, 254], "256": [133, 254, 934, 1051], "much": [133, 225, 236, 254, 268, 309, 430, 568, 639, 719, 745, 785, 914, 1051], "almost": [133, 236, 254, 535, 536, 537, 588, 745, 1006, 1007, 1008, 1051], "_significantly_": [133, 236, 254, 745, 1051], "intens": [133, 236, 254, 465, 639, 745, 1051], "forc": [133, 173, 236, 254, 676, 677, 719, 735, 745, 1051], "materi": [133, 236, 254, 690, 719, 735, 738, 745, 1051], "parallelis": [133, 236, 254, 745, 1051], "optimis": [133, 197, 236, 254, 735, 745, 962, 963, 1051], "achiev": [133, 236, 254, 268, 639, 745, 1051], "best": [133, 236, 254, 268, 639, 745, 1051], "tri": [133, 254], "arbitrarili": [133, 254], "rearrang": [133, 254], "transform": [133, 254, 438, 539, 541, 542, 639], "preserv": [133, 157, 217, 218, 254, 500, 639, 971, 1033, 1051], "lru_cach": [133, 254, 268, 639, 745, 1051], "magnitud": [133, 254, 268, 639, 745, 1051], "column_1": [133, 222, 254], "better": [133, 217, 236, 254, 268, 560, 639, 719, 962, 963, 1051], "scalar": [133, 169, 195, 254, 494, 568, 604, 639, 880, 960, 1051], "k": [134, 197, 221, 254, 291, 295, 396, 555, 639, 653, 707, 735, 766, 770, 881, 1035, 1051], "intoexpr": [134, 157, 158, 159, 200, 207, 221, 231, 234, 254, 267, 383, 407, 421, 422, 423, 424, 465, 497, 506, 564, 565, 566, 567, 570, 573, 576, 578, 579, 583, 585, 586, 588, 589, 595, 601, 602, 606, 607, 610, 611, 616, 619, 622, 623, 624, 627, 628, 630, 639, 653, 667, 670, 671, 672, 696, 702, 707, 713, 717, 735, 861, 1051], "nulls_last": [134, 207, 221, 254, 278, 505, 639, 653, 702, 707, 735, 754, 1051], "smallest": [134, 221, 254, 291, 639, 653, 707, 735, 766, 1051], "largest": [134, 158, 159, 173, 221, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 555, 639, 653, 671, 672, 677, 707, 735, 822, 826, 833, 1035, 1051], "last": [134, 161, 173, 175, 187, 197, 207, 210, 221, 223, 248, 254, 278, 309, 322, 337, 342, 353, 395, 412, 429, 469, 505, 532, 547, 549, 588, 625, 639, 653, 677, 702, 705, 707, 708, 731, 735, 738, 754, 785, 799, 818, 823, 834, 856, 883, 896, 913, 1003, 1021, 1051], "wors": [134, 221, 254, 653, 702, 707, 735], "search": [134, 173, 221, 254, 653, 677, 702, 707, 735], "top_k": [134, 254, 291, 639, 653, 735, 766, 1051], "greater": [135, 173, 254, 376, 377, 503, 532, 639, 677, 735, 974, 1003, 1051], "cheap": [135, 136, 254, 655, 656, 735, 744, 774, 778, 1051], "deepcopi": [135, 136, 254, 655, 656, 735, 774, 778, 1051], "clear": [136, 254, 656, 735, 778, 1051], "properti": [137, 143, 151, 162, 199, 202, 230, 254, 658, 661, 695, 712, 735, 738, 1059], "appl": [137, 163, 172, 191, 193, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 479, 514, 533, 639, 676, 693, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730, 735, 985, 1004], "banana": [137, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 479, 639, 720, 721, 723, 724, 725, 726, 727, 728, 729, 730], "pairwis": [138, 254], "pearson": [138, 254, 396, 503, 580, 639, 881, 974, 1051], "correl": [138, 254, 580, 617], "coeffici": [138, 254, 503, 639, 974, 1051], "corrcoef": [138, 254], "percentil": [139, 254, 787, 1051], "summari": [139, 254, 787, 1051], "glimps": [139, 161, 254], "usd": [139, 156, 254, 1059], "2020": [139, 156, 159, 254, 319, 323, 324, 327, 330, 331, 333, 335, 340, 344, 347, 348, 351, 352, 535, 536, 537, 672, 735, 738, 796, 800, 801, 804, 811, 813, 816, 821, 825, 828, 829, 832, 833, 1006, 1007, 1008], "null_count": [139, 142, 254, 309, 639, 735, 785, 787, 1051], "266667": [139, 254], "666667": [139, 177, 228, 242, 254, 360, 639, 711, 725, 735], "std": [139, 254, 482, 489, 639, 735, 787, 955, 1051], "101514": [139, 254], "707107": [139, 254, 361, 489, 639, 842, 1051], "57735": [139, 254], "median": [139, 187, 254, 368, 485, 639, 714, 735, 787, 951, 1051], "more_column": [140, 145, 201, 224, 254, 363, 593, 639, 659, 663, 697, 709, 735], "Or": [140, 157, 158, 159, 173, 207, 227, 234, 254, 465, 506, 577, 630, 639, 659, 670, 671, 672, 677, 702, 717, 735], "subset": [142, 183, 223, 254, 660, 708, 735], "snippet": [142, 254, 660, 735], "all_horizont": [142, 254, 564, 660, 735], "is_nul": [142, 254, 639, 660, 735, 1051], "sizeunit": [144, 254, 840, 1051], "heap": [144, 254, 840, 1051], "its": [144, 254, 318, 345, 352, 506, 639, 795, 826, 833, 840, 1051], "bitmap": [144, 254, 840, 1051], "therefor": [144, 254, 630, 840, 1051], "structarrai": [144, 254, 840, 1051], "constant": [144, 159, 254, 316, 366, 639, 672, 735, 793, 840, 847, 1051], "unchang": [144, 254, 554, 639, 681, 719, 735, 840, 1034, 1051], "capac": [144, 205, 254, 840, 969, 1051], "ffi": [144, 254, 840, 1051], "kb": [144, 254, 840, 1051], "mb": [144, 254, 840, 1051], "gb": [144, 254, 840, 1051], "tb": [144, 254, 840, 1051], "revers": [144, 254, 304, 305, 306, 307, 308, 439, 469, 547, 639, 735, 781, 782, 783, 784, 1051], "1_000_000": [144, 254, 840, 1051], "25888898": [144, 254], "689577102661133": [144, 254], "long": [145, 179, 225, 254, 663, 685, 735], "letter": [145, 239, 248, 254, 363, 517, 593, 639, 663, 722, 731, 735, 738, 988], "onlin": [146, 254, 744, 846, 1051], "rerun": [146, 254, 744, 846, 1051], "conveni": [146, 254, 744, 846, 1051], "evalu": [147, 149, 173, 254, 265, 279, 309, 381, 401, 402, 431, 440, 464, 564, 566, 570, 574, 588, 589, 592, 601, 602, 613, 616, 622, 627, 628, 630, 631, 639, 667, 674, 676, 677, 735, 755, 785, 885, 886, 1042, 1051], "Not": [147, 254, 389, 391, 440, 639, 665, 735], "To": [147, 254, 314, 315, 341, 368, 510, 516, 517, 525, 541, 623, 639, 665, 735, 822, 981, 987, 988, 996, 1012, 1032, 1051], "fillnullstrategi": [148, 254, 368, 639, 666, 735, 849, 1051], "matches_supertyp": [148, 254, 666, 735], "forward": [148, 173, 254, 337, 368, 374, 639, 666, 677, 735, 818, 849, 1051], "consecut": [148, 254, 285, 368, 374, 509, 639, 666, 735, 849, 980, 1051], "fill_nan": [148, 254, 639, 735, 1051], "OR": [149, 254, 566, 567, 667, 735, 738], "reduct": [152, 254], "supercast": [152, 254], "parent": [152, 254], "rule": [152, 254], "arithmet": [152, 254], "zip_with": [152, 254, 1051], "foo11": [152, 254], "bar22": [152, 254], "null_equ": [153, 254, 961, 1051], "retriev": [154, 254, 403, 404, 544, 887, 888, 1015], "return_as_str": [156, 254, 451], "preview": [156, 254], "wide": [156, 179, 225, 254, 685, 735], "nice": [156, 254], "few": [156, 254], "rather": [156, 173, 254, 451, 481, 543, 639, 677, 735, 947, 1014, 1051], "head": [156, 175, 210, 254, 267, 400, 639, 680, 735, 883, 1021, 1051], "tail": [156, 161, 254, 267, 503, 639, 735, 856, 974, 1051], "more_bi": [157, 185, 207, 254, 506, 639, 670, 702, 735], "consist": [157, 185, 254, 535, 670, 735, 744, 846, 1006, 1051], "regardless": [157, 254, 519, 990], "agg": [157, 158, 159, 254, 262, 268, 369, 371, 505, 506, 550, 562, 581, 639, 657, 662, 664, 670, 671, 672, 690, 700, 735, 738], "index_column": [158, 159, 254, 671, 672, 735], "timedelta": [158, 159, 227, 254, 322, 325, 326, 329, 334, 336, 341, 342, 343, 345, 346, 350, 352, 353, 354, 356, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 738, 807, 809, 814, 822, 826, 833, 930, 931, 1051], "period": [158, 159, 203, 204, 254, 345, 352, 360, 361, 362, 425, 466, 496, 497, 588, 589, 627, 628, 639, 671, 672, 698, 699, 735, 826, 833, 841, 842, 843, 909, 934, 966, 967, 1051], "include_boundari": [158, 254, 671, 735], "closedinterv": [158, 159, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 861, 1051], "start_bi": [158, 254, 671, 735], "startbi": [158, 254, 671, 735], "window": [158, 159, 254, 309, 345, 352, 360, 361, 362, 465, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 578, 617, 618, 639, 671, 672, 735, 785, 826, 833, 841, 842, 843, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1051], "check_sort": [158, 159, 254, 671, 672, 735], "dynamicgroupbi": [158, 254], "groupbi": [158, 159, 183, 254, 262, 268, 309, 369, 371, 409, 465, 505, 506, 550, 562, 568, 581, 639, 657, 662, 664, 671, 672, 690, 700, 735, 738, 785, 893, 1051], "member": [158, 254, 671, 735, 868, 1051], "seen": [158, 254, 285, 374, 639, 671, 735], "roll": [158, 159, 254, 337, 338, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 578, 617, 618, 639, 671, 672, 735, 818, 819, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 1051], "slot": [158, 254, 309, 312, 408, 639, 671, 735, 785, 788, 892, 1051], "interv": [158, 159, 227, 254, 310, 328, 345, 346, 352, 383, 471, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 627, 628, 639, 671, 672, 735, 786, 799, 802, 803, 805, 810, 812, 815, 817, 820, 823, 824, 826, 827, 831, 833, 834, 835, 837, 861, 938, 1051], "1n": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "nanosecond": [158, 159, 173, 227, 254, 341, 345, 346, 352, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 822, 826, 827, 833], "1u": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "microsecond": [158, 159, 170, 173, 196, 197, 214, 227, 254, 341, 345, 346, 352, 483, 484, 485, 486, 487, 489, 490, 491, 590, 591, 626, 639, 671, 672, 677, 690, 735, 738, 822, 826, 833], "1m": [158, 159, 173, 227, 254, 330, 331, 333, 340, 341, 345, 347, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 811, 813, 821, 822, 826, 828, 833], "millisecond": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 738, 822, 826, 833], "minut": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 590, 591, 626, 627, 639, 671, 672, 677, 735, 738, 822, 826, 833], "1h": [158, 159, 173, 227, 254, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 627, 628, 639, 671, 672, 677, 735, 801, 803, 822, 826, 833], "hour": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 590, 591, 626, 627, 639, 671, 672, 677, 735, 738, 822, 826, 833], "1d": [158, 159, 173, 227, 254, 317, 327, 335, 341, 345, 352, 355, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 604, 639, 671, 672, 677, 735, 738, 794, 802, 804, 807, 808, 809, 814, 816, 822, 826, 831, 833, 835, 836], "1w": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "week": [158, 159, 173, 227, 254, 341, 345, 352, 354, 483, 484, 485, 486, 487, 489, 490, 491, 591, 639, 671, 672, 677, 735, 738, 822, 826, 833, 835], "1mo": [158, 159, 173, 227, 254, 319, 323, 337, 338, 341, 344, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 639, 671, 672, 677, 735, 796, 800, 817, 818, 819, 822, 823, 824, 825, 826, 833, 834], "month": [158, 159, 173, 227, 254, 322, 337, 338, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 587, 588, 589, 590, 639, 671, 672, 677, 735, 799, 818, 819, 822, 826, 833], "1q": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "quarter": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "1y": [158, 159, 173, 227, 254, 328, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 805, 822, 826, 833, 837], "1i": [158, 159, 173, 227, 254, 341, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822], "3d12h4m25": [158, 159, 173, 227, 254, 345, 352, 671, 672, 677, 735, 826, 833], "suffix": [158, 159, 172, 173, 200, 227, 231, 234, 254, 263, 289, 341, 345, 352, 389, 391, 392, 393, 439, 465, 469, 479, 483, 484, 485, 486, 487, 489, 490, 491, 514, 639, 671, 672, 676, 677, 696, 713, 714, 717, 735, 738, 764, 822, 826, 833, 985], "_satur": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 588, 589, 639, 671, 672, 677, 735, 822, 826, 833], "satur": [158, 159, 173, 227, 254, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 822, 826, 833], "28": [158, 159, 173, 213, 227, 254, 261, 341, 344, 345, 352, 355, 483, 484, 485, 486, 487, 489, 490, 491, 588, 639, 671, 672, 677, 735, 822, 825, 826, 833, 836, 1059], "correspond": [158, 159, 173, 217, 227, 254, 329, 341, 345, 352, 474, 481, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 806, 822, 826, 833, 940, 947, 1051], "due": [158, 159, 173, 197, 227, 254, 263, 293, 324, 341, 345, 352, 395, 469, 483, 484, 485, 486, 487, 489, 490, 491, 493, 499, 547, 639, 671, 672, 677, 735, 767, 801, 822, 826, 833, 1051], "daylight": [158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 672, 677, 735, 801, 822, 826, 833], "10i": [158, 159, 254, 671, 672, 735], "ascend": [158, 159, 254, 671, 672, 735], "dynam": [158, 254, 431, 483, 484, 485, 486, 487, 489, 490, 491, 639, 671, 735, 915], "matter": [158, 159, 170, 196, 197, 214, 254, 671, 672, 735], "_lower_bound": [158, 254, 671, 735], "_upper_bound": [158, 254, 671, 735], "harder": [158, 254, 671, 735], "tempor": [158, 159, 170, 196, 197, 214, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 639, 650, 671, 672, 735, 738, 861, 877, 1051], "inclus": [158, 159, 254, 383, 483, 484, 485, 486, 487, 489, 490, 491, 530, 531, 570, 588, 589, 601, 602, 627, 628, 639, 671, 672, 735, 861, 1001, 1002, 1051], "datapoint": [158, 254, 671, 735], "mondai": [158, 254, 352, 354, 671, 735, 833, 835], "tuesdai": [158, 254, 671, 735], "wednesdai": [158, 254, 671, 735], "thursdai": [158, 254, 671, 735], "fridai": [158, 254, 671, 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module polars)": [[95, "polars.from_pandas"]], "from_records() (in module polars)": [[96, "polars.from_records"]], "from_repr() (in module polars)": [[97, "polars.from_repr"]], "get_index_type() (in module polars)": [[98, "polars.get_index_type"]], "next_batches() (polars.io.csv.batched_reader.batchedcsvreader method)": [[99, "polars.io.csv.batched_reader.BatchedCsvReader.next_batches"]], "read_avro() (in module polars)": [[100, "polars.read_avro"]], "read_csv() (in module polars)": [[101, "polars.read_csv"]], "read_csv_batched() (in module polars)": [[102, "polars.read_csv_batched"]], "read_database() (in module polars)": [[103, "polars.read_database"]], "read_delta() (in module polars)": [[104, "polars.read_delta"]], "read_excel() (in module polars)": [[105, "polars.read_excel"]], "read_ipc() (in module polars)": [[106, "polars.read_ipc"]], "read_ipc_schema() (in module polars)": [[107, "polars.read_ipc_schema"]], "read_json() (in module polars)": [[108, "polars.read_json"]], "read_ndjson() (in module polars)": [[109, "polars.read_ndjson"]], "read_parquet() (in module polars)": [[110, "polars.read_parquet"]], "read_parquet_schema() (in module polars)": [[111, "polars.read_parquet_schema"]], "scan_csv() (in module polars)": [[112, "polars.scan_csv"]], "scan_delta() (in module polars)": [[113, "polars.scan_delta"]], "scan_ipc() (in module polars)": [[114, "polars.scan_ipc"]], "scan_ndjson() (in module polars)": [[115, "polars.scan_ndjson"]], "scan_parquet() (in module polars)": [[116, "polars.scan_parquet"]], "scan_pyarrow_dataset() (in module polars)": [[117, "polars.scan_pyarrow_dataset"]], "show_versions() (in module polars)": [[118, "polars.show_versions"]], "assert_frame_equal() (in module polars.testing)": [[119, "polars.testing.assert_frame_equal"]], "assert_series_equal() (in module polars.testing)": [[120, "polars.testing.assert_series_equal"]], "__init__() (polars.testing.parametric.column method)": [[121, "polars.testing.parametric.column.__init__"]], "column (class in polars.testing.parametric)": [[121, "polars.testing.parametric.column"]], "columns() (in module polars.testing.parametric)": [[122, "polars.testing.parametric.columns"]], "create_list_strategy() (in module polars.testing.parametric)": [[123, "polars.testing.parametric.create_list_strategy"]], "dataframes() (in module polars.testing.parametric)": [[124, "polars.testing.parametric.dataframes"]], "load_profile() (in module polars.testing.parametric)": [[125, "polars.testing.parametric.load_profile"]], "series() (in module polars.testing.parametric)": [[126, "polars.testing.parametric.series"]], "set_profile() (in module polars.testing.parametric)": [[127, "polars.testing.parametric.set_profile"]], "threadpool_size() (in module polars)": [[128, "polars.threadpool_size"]], "using_string_cache() (in module polars)": [[129, "polars.using_string_cache"]], "__dataframe__() (polars.dataframe method)": [[132, 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"polars.all"]], "all_horizontal() (in module polars)": [[565, "polars.all_horizontal"]], "any() (in module polars)": [[566, "polars.any"]], "any_horizontal() (in module polars)": [[567, "polars.any_horizontal"]], "apply() (in module polars)": [[568, "polars.apply"]], "approx_unique() (in module polars)": [[569, "polars.approx_unique"]], "arange() (in module polars)": [[570, "polars.arange"]], "arctan2() (in module polars)": [[571, "polars.arctan2"]], "arctan2d() (in module polars)": [[572, "polars.arctan2d"]], "arg_sort_by() (in module polars)": [[573, "polars.arg_sort_by"]], "arg_where() (in module polars)": [[574, "polars.arg_where"]], "avg() (in module polars)": [[575, "polars.avg"]], "coalesce() (in module polars)": [[576, "polars.coalesce"]], "col() (in module polars)": [[577, "polars.col"]], "concat_list() (in module polars)": [[578, "polars.concat_list"]], "concat_str() (in module polars)": [[579, "polars.concat_str"]], "corr() (in module polars)": [[580, "polars.corr"]], "count() (in module polars)": [[581, "polars.count"]], "cov() (in module polars)": [[582, "polars.cov"]], "cumfold() (in module polars)": [[583, "polars.cumfold"]], "cumreduce() (in module polars)": [[584, "polars.cumreduce"]], "cumsum() (in module polars)": [[585, "polars.cumsum"]], "cumsum_horizontal() (in module polars)": [[586, "polars.cumsum_horizontal"]], "date() (in module polars)": [[587, "polars.date"]], "date_range() (in module polars)": [[588, "polars.date_range"]], "date_ranges() (in module polars)": [[589, "polars.date_ranges"]], "datetime() (in module polars)": [[590, "polars.datetime"]], "duration() (in module polars)": [[591, "polars.duration"]], "element() (in module polars)": [[592, "polars.element"]], "exclude() (in module polars)": [[593, "polars.exclude"]], "first() (in module polars)": [[594, "polars.first"]], "fold() (in module polars)": [[595, "polars.fold"]], "format() (in module polars)": [[596, "polars.format"]], "from_epoch() (in module polars)": [[597, 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"fill_null() (polars.lazyframe method)": [[666, "polars.LazyFrame.fill_null"]], "filter() (polars.lazyframe method)": [[667, "polars.LazyFrame.filter"]], "first() (polars.lazyframe method)": [[668, "polars.LazyFrame.first"]], "from_json() (polars.lazyframe class method)": [[669, "polars.LazyFrame.from_json"]], "groupby() (polars.lazyframe method)": [[670, "polars.LazyFrame.groupby"]], "groupby_dynamic() (polars.lazyframe method)": [[671, "polars.LazyFrame.groupby_dynamic"]], "groupby_rolling() (polars.lazyframe method)": [[672, "polars.LazyFrame.groupby_rolling"]], "head() (polars.lazyframe method)": [[673, "polars.LazyFrame.head"]], "inspect() (polars.lazyframe method)": [[674, "polars.LazyFrame.inspect"]], "interpolate() (polars.lazyframe method)": [[675, "polars.LazyFrame.interpolate"]], "join() (polars.lazyframe method)": [[676, "polars.LazyFrame.join"]], "join_asof() (polars.lazyframe method)": [[677, "polars.LazyFrame.join_asof"]], "last() (polars.lazyframe method)": [[678, 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(polars.lazyframe class method)": [[692, "polars.LazyFrame.read_json"]], "rename() (polars.lazyframe method)": [[693, "polars.LazyFrame.rename"]], "reverse() (polars.lazyframe method)": [[694, "polars.LazyFrame.reverse"]], "schema (polars.lazyframe property)": [[695, "polars.LazyFrame.schema"]], "select() (polars.lazyframe method)": [[696, "polars.LazyFrame.select"]], "set_sorted() (polars.lazyframe method)": [[697, "polars.LazyFrame.set_sorted"]], "shift() (polars.lazyframe method)": [[698, "polars.LazyFrame.shift"]], "shift_and_fill() (polars.lazyframe method)": [[699, "polars.LazyFrame.shift_and_fill"]], "show_graph() (polars.lazyframe method)": [[700, "polars.LazyFrame.show_graph"]], "slice() (polars.lazyframe method)": [[701, "polars.LazyFrame.slice"]], "sort() (polars.lazyframe method)": [[702, "polars.LazyFrame.sort"]], "std() (polars.lazyframe method)": [[703, "polars.LazyFrame.std"]], "sum() (polars.lazyframe method)": [[704, "polars.LazyFrame.sum"]], "tail() (polars.lazyframe method)": [[705, "polars.LazyFrame.tail"]], "take_every() (polars.lazyframe method)": [[706, "polars.LazyFrame.take_every"]], "top_k() (polars.lazyframe method)": [[707, "polars.LazyFrame.top_k"]], "unique() (polars.lazyframe method)": [[708, "polars.LazyFrame.unique"]], "unnest() (polars.lazyframe method)": [[709, "polars.LazyFrame.unnest"]], "update() (polars.lazyframe method)": [[710, "polars.LazyFrame.update"]], "var() (polars.lazyframe method)": [[711, "polars.LazyFrame.var"]], "width (polars.lazyframe property)": [[712, "polars.LazyFrame.width"]], "with_columns() (polars.lazyframe method)": [[713, "polars.LazyFrame.with_columns"]], "with_context() (polars.lazyframe method)": [[714, "polars.LazyFrame.with_context"]], "with_row_count() (polars.lazyframe method)": [[715, "polars.LazyFrame.with_row_count"]], "write_json() (polars.lazyframe method)": [[716, "polars.LazyFrame.write_json"]], "agg() (polars.lazyframe.groupby.lazygroupby method)": [[717, "polars.lazyframe.groupby.LazyGroupBy.agg"]], "all() (polars.lazyframe.groupby.lazygroupby method)": [[718, "polars.lazyframe.groupby.LazyGroupBy.all"]], "apply() (polars.lazyframe.groupby.lazygroupby method)": [[719, "polars.lazyframe.groupby.LazyGroupBy.apply"]], "count() (polars.lazyframe.groupby.lazygroupby method)": [[720, "polars.lazyframe.groupby.LazyGroupBy.count"]], "first() (polars.lazyframe.groupby.lazygroupby method)": [[721, "polars.lazyframe.groupby.LazyGroupBy.first"]], "head() (polars.lazyframe.groupby.lazygroupby method)": [[722, "polars.lazyframe.groupby.LazyGroupBy.head"]], "last() (polars.lazyframe.groupby.lazygroupby method)": [[723, "polars.lazyframe.groupby.LazyGroupBy.last"]], "max() (polars.lazyframe.groupby.lazygroupby method)": [[724, "polars.lazyframe.groupby.LazyGroupBy.max"]], "mean() (polars.lazyframe.groupby.lazygroupby method)": [[725, "polars.lazyframe.groupby.LazyGroupBy.mean"]], "median() (polars.lazyframe.groupby.lazygroupby method)": [[726, 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\ No newline at end of file