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.../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/dev/reference/series/index.html | 350 +++++++++--------- py-polars/dev/searchindex.js | 2 +- 497 files changed, 847 insertions(+), 847 deletions(-) diff --git a/py-polars/dev/reference/api/polars.DataFrame.write_avro.html b/py-polars/dev/reference/api/polars.DataFrame.write_avro.html index 892bf1ca176c..482b41b2e1ea 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_avro.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_avro.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_csv.html b/py-polars/dev/reference/api/polars.DataFrame.write_csv.html index 50c341b111ca..b7b15d6e7c82 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_csv.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_csv.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_database.html b/py-polars/dev/reference/api/polars.DataFrame.write_database.html index 83a207d394cb..f3a0b05b8f3e 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_database.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_database.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_delta.html b/py-polars/dev/reference/api/polars.DataFrame.write_delta.html index d9a98eb85fdf..242cbf1cb96a 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_delta.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_delta.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_excel.html b/py-polars/dev/reference/api/polars.DataFrame.write_excel.html index 21ff7e224e88..a559b644765c 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_excel.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_excel.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_ipc.html b/py-polars/dev/reference/api/polars.DataFrame.write_ipc.html index 630e6f8246cb..6f6118224482 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_ipc.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_ipc.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_json.html b/py-polars/dev/reference/api/polars.DataFrame.write_json.html index 29dcdbb83a22..8e085ace4d60 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_json.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_json.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_ndjson.html b/py-polars/dev/reference/api/polars.DataFrame.write_ndjson.html index f9754ebb45da..7f24ed8c49a8 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_ndjson.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_ndjson.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.DataFrame.write_parquet.html b/py-polars/dev/reference/api/polars.DataFrame.write_parquet.html index 0ac6bbb29da6..88c83ee6dddb 100644 --- a/py-polars/dev/reference/api/polars.DataFrame.write_parquet.html +++ b/py-polars/dev/reference/api/polars.DataFrame.write_parquet.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.LazyFrame.sink_ipc.html b/py-polars/dev/reference/api/polars.LazyFrame.sink_ipc.html index bfba17e4a391..d5e871f0dadf 100644 --- a/py-polars/dev/reference/api/polars.LazyFrame.sink_ipc.html +++ b/py-polars/dev/reference/api/polars.LazyFrame.sink_ipc.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.LazyFrame.sink_parquet.html b/py-polars/dev/reference/api/polars.LazyFrame.sink_parquet.html index bcade280af42..46bb0c1cce8f 100644 --- a/py-polars/dev/reference/api/polars.LazyFrame.sink_parquet.html +++ b/py-polars/dev/reference/api/polars.LazyFrame.sink_parquet.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.StringCache.html b/py-polars/dev/reference/api/polars.StringCache.html index d51066226d54..cc68a59157bc 100644 --- a/py-polars/dev/reference/api/polars.StringCache.html +++ b/py-polars/dev/reference/api/polars.StringCache.html @@ -1621,7 +1621,7 @@

polars.StringCache#

-class polars.StringCache[source]#
+class polars.StringCache[source]#

Context manager that allows data sources to share the same categorical features.

This will temporarily cache the string categories until the context manager is finished. If StringCaches are nested, the global cache will only be invalidated diff --git a/py-polars/dev/reference/api/polars.collect_all.html b/py-polars/dev/reference/api/polars.collect_all.html index 0273fecb376a..ce77e5aa1eb3 100644 --- a/py-polars/dev/reference/api/polars.collect_all.html +++ b/py-polars/dev/reference/api/polars.collect_all.html @@ -1621,7 +1621,7 @@

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/dev/reference/api/polars.enable_string_cache.html b/py-polars/dev/reference/api/polars.enable_string_cache.html index 860ffb2850ae..24df7d42ed8f 100644 --- a/py-polars/dev/reference/api/polars.enable_string_cache.html +++ b/py-polars/dev/reference/api/polars.enable_string_cache.html @@ -1621,7 +1621,7 @@

polars.enable_string_cache#

-polars.enable_string_cache(enable: bool) → None[source]#
+polars.enable_string_cache(enable: bool) → None[source]#

Enable (or disable) the global string cache.

This ensures that casts to Categorical dtypes will have the same category values when string values are equal.

diff --git a/py-polars/dev/reference/api/polars.get_index_type.html b/py-polars/dev/reference/api/polars.get_index_type.html index 85ade8d20cb3..8b904c17c69d 100644 --- a/py-polars/dev/reference/api/polars.get_index_type.html +++ b/py-polars/dev/reference/api/polars.get_index_type.html @@ -1621,7 +1621,7 @@

polars.get_index_type#

-polars.get_index_type() → DataTypeClass[source]#
+polars.get_index_type() → DataTypeClass[source]#

Get the datatype used for Polars indexing.

Returns:
diff --git a/py-polars/dev/reference/api/polars.scan_pyarrow_dataset.html b/py-polars/dev/reference/api/polars.scan_pyarrow_dataset.html index c122d1fd064a..c84383d6f78c 100644 --- a/py-polars/dev/reference/api/polars.scan_pyarrow_dataset.html +++ b/py-polars/dev/reference/api/polars.scan_pyarrow_dataset.html @@ -1621,7 +1621,7 @@

polars.scan_pyarrow_dataset#

-polars.scan_pyarrow_dataset(source: pa.dataset.Dataset, *, allow_pyarrow_filter: bool = True) → LazyFrame[source]#
+polars.scan_pyarrow_dataset(source: pa.dataset.Dataset, *, allow_pyarrow_filter: bool = True) → LazyFrame[source]#

Scan a pyarrow dataset.

This can be useful to connect to cloud or partitioned datasets.

diff --git a/py-polars/dev/reference/api/polars.threadpool_size.html b/py-polars/dev/reference/api/polars.threadpool_size.html index 50475c267b52..8d17c5bbe010 100644 --- a/py-polars/dev/reference/api/polars.threadpool_size.html +++ b/py-polars/dev/reference/api/polars.threadpool_size.html @@ -1621,7 +1621,7 @@

polars.threadpool_size#

-polars.threadpool_size() → int[source]#
+polars.threadpool_size() → int[source]#

Get the number of threads in the Polars thread pool.

Notes

The threadpool size can be overridden by setting the POLARS_MAX_THREADS diff --git a/py-polars/dev/reference/api/polars.using_string_cache.html b/py-polars/dev/reference/api/polars.using_string_cache.html index 697261e03bda..9c68058063f6 100644 --- a/py-polars/dev/reference/api/polars.using_string_cache.html +++ b/py-polars/dev/reference/api/polars.using_string_cache.html @@ -1621,7 +1621,7 @@

polars.using_string_cache#

-polars.using_string_cache() → bool[source]#
+polars.using_string_cache() → bool[source]#

Return the current state of the global string cache (enabled/disabled).

diff --git a/py-polars/dev/reference/dataframe/api/polars.DataFrame.__dataframe__.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.__dataframe__.html index b553d6bb7616..ea1771b724b2 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.__dataframe__.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.__dataframe__.html @@ -1626,7 +1626,7 @@

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/dev/reference/dataframe/api/polars.DataFrame.apply.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.apply.html index d3fe09f31686..944a37cc6638 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.apply.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.apply.html @@ -1626,7 +1626,7 @@

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/dev/reference/dataframe/api/polars.DataFrame.bottom_k.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.bottom_k.html index ca2c20c1f26d..990a105c394a 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.bottom_k.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.bottom_k.html @@ -1626,7 +1626,7 @@

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/dev/reference/dataframe/api/polars.DataFrame.clear.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.clear.html index 0860133b48cb..f16fef4abb6a 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.clear.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.clear.html @@ -1626,7 +1626,7 @@

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/dev/reference/dataframe/api/polars.DataFrame.clone.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.clone.html index 05cc8aa51ecc..b89e282cd17e 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.clone.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.clone.html @@ -1626,7 +1626,7 @@

polars.DataFrame.clone#

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

Cheap deepcopy/clone.

See also

diff --git a/py-polars/dev/reference/dataframe/api/polars.DataFrame.columns.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.columns.html index 6811eaef3e09..9672103ae4ac 100644 --- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.columns.html +++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.columns.html @@ -1626,7 +1626,7 @@

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/dev/reference/dataframe/api/polars.DataFrame.corr.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.corr.html
index efe84c1b106c..853ec9bfcd9d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.corr.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.corr.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.describe.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.describe.html
index e91667f910ac..c60e1d62b3b2 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.describe.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.describe.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.drop.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop.html
index 3cc7f81282f2..e2ef753e1dea 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.drop_in_place.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_in_place.html
index f39bc31aa858..96a698d8001d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_in_place.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_in_place.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.drop_nulls.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_nulls.html
index 17016bb3cedc..18d87657a9d3 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_nulls.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.drop_nulls.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.dtypes.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.dtypes.html
index 155bacb33bd3..1601b55aca77 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.dtypes.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.dtypes.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.estimated_size.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.estimated_size.html
index 5cfdbd2396c8..fa67101e35c9 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.estimated_size.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.estimated_size.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.explode.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.explode.html
index d0bc3e79aa63..566406420b9e 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.explode.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.explode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.extend.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.extend.html
index 4b0b196ee191..a80badf9b05b 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.extend.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.extend.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.fill_nan.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_nan.html
index 39a30453e679..28a8355954e8 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_nan.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_nan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.fill_null.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_null.html
index ad11276b7268..42f34b94c5db 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_null.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fill_null.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.filter.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.filter.html
index 3ed7fcd9b55e..59081c59853d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.filter.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.filter.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
index 60057ca008d3..43d2793cc6bd 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.find_idx_by_name.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.flags.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.flags.html
index 87cebc27a6cc..14cf59df6922 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.flags.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.flags.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.fold.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fold.html
index 2e280d18b2bb..529f9e14f4e5 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.fold.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.fold.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.frame_equal.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.frame_equal.html
index 92e1dee7703d..f20cfaec6e3b 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.frame_equal.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.frame_equal.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.get_column.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_column.html
index 224ef79c0eff..6c3ec4ae1b27 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_column.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_column.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.get_columns.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_columns.html
index be27f4b4d716..081a7b8e8959 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_columns.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.get_columns.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.glimpse.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.glimpse.html
index 652f53f4662b..c152828924e0 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.glimpse.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.glimpse.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.groupby.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby.html
index c9bcfaf733f8..128d696a3e03 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
index 89f7ea1686d9..bbb4a52ba5d4 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_dynamic.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.groupby_rolling.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
index 7f76e9ead7bf..2391574b7105 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.groupby_rolling.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.hash_rows.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.hash_rows.html
index 354673e29224..61592f24cfe1 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.hash_rows.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.hash_rows.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.head.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.head.html
index 69d4b68e179e..0f562aa0dd82 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.head.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.head.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.height.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.height.html
index 8cdbb130cc9b..c3993d84d07e 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.height.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.height.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.hstack.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.hstack.html
index 662cacd453d1..5dd0727fec1d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.hstack.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.hstack.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.insert_at_idx.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
index 101a51bcaa67..44a58a51e7ce 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.insert_at_idx.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.interpolate.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.interpolate.html
index 78615444a109..4e9fa0f639e3 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.interpolate.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.interpolate.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.is_duplicated.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_duplicated.html
index c95c380f8a81..d59c96225b6d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_duplicated.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_duplicated.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.is_empty.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_empty.html
index 261f48e8847d..2eb6853baf93 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_empty.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_empty.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.is_unique.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_unique.html
index 3c86bcfd507e..0cb2ef380cbe 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_unique.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.is_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.item.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.item.html
index d359c372871a..ab55b807109b 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.item.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.item.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.iter_rows.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_rows.html
index 92a3c936d2ae..99087610ad24 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_rows.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_rows.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.iter_slices.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_slices.html
index 823c69e7712d..8c90e0d5d8b0 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_slices.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.iter_slices.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.join.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.join.html
index 91e910497862..94bdda513e4f 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.join.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.join.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.join_asof.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.join_asof.html
index d74eb075d4a0..94b3a06a7c50 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.join_asof.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.join_asof.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.lazy.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.lazy.html
index 48d8966da89c..fa8c010319c5 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.lazy.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.lazy.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.limit.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.limit.html
index cd45ab954ca4..ae3c9642c42c 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.limit.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.limit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.max.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.max.html
index df98b83a8602..349943a9f890 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.max.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.mean.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.mean.html
index 08289e6928ae..8a7e9be15f1f 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.mean.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.mean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.median.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.median.html
index 752b5df006f8..4bcc695748cb 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.median.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.median.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.melt.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.melt.html
index 923b64faf686..1ef8636eadae 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.melt.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.melt.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.merge_sorted.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.merge_sorted.html
index a09c0850c8bc..3a00c67614a4 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.merge_sorted.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.merge_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.min.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.min.html
index 2477810f022a..473c805aae14 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.min.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.n_chunks.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_chunks.html
index 023780af0e3e..e6f48eedc500 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_chunks.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_chunks.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.n_unique.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_unique.html
index 2fc39c8cd6b3..1c2bc507a854 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_unique.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.n_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.null_count.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.null_count.html
index bba1d8eb811f..aea5d54069e2 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.null_count.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.null_count.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.partition_by.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.partition_by.html
index 67cc54b533a5..5d220bd941b2 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.partition_by.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.partition_by.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.pipe.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.pipe.html
index c0ad8dfa2065..4c06b29bff41 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.pipe.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.pipe.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.pivot.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.pivot.html
index acd79ff4d9f5..a9c5c62b8fde 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.pivot.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.pivot.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.product.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.product.html
index fa873278cf76..fff2f2e686a3 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.product.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.product.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.quantile.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.quantile.html
index b66c105ecd96..2d90182c59a0 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.quantile.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.quantile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.rechunk.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rechunk.html
index 262f71a744cf..c999dcb64744 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.rechunk.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rechunk.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.rename.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rename.html
index 0a1c73d17a2a..a10812e20201 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.rename.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rename.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.replace.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace.html
index a968890f47b2..a4009bbfbc16 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.replace_at_idx.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
index f0edd54e95d9..fe6980ce0b9e 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.replace_at_idx.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.reverse.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.reverse.html
index 51a94bba32b9..bff378e2d2e9 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.reverse.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.reverse.html
@@ -1626,7 +1626,7 @@
 
polars.DataFrame.reverse#

 

 

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

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

 
Examples

 
>>> df = pl.DataFrame(
diff --git a/py-polars/dev/reference/dataframe/api/polars.DataFrame.row.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.row.html
index acb9b65b25df..aaa694fd6480 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.row.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.row.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.rows.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows.html
index 795e80f9c432..0f0959b6939c 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.rows_by_key.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows_by_key.html
index 291864cecf64..495fe4d9c891 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows_by_key.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.rows_by_key.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.sample.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sample.html
index a2b98f3e87b0..83d3dca09c23 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.sample.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sample.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.schema.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.schema.html
index c346f0231807..9e49fe1bbdb5 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.schema.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.schema.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.select.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.select.html
index 08ef665733f1..9efc15c335ad 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.select.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.select.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.set_sorted.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.set_sorted.html
index b156027691cc..61a9cb57feb5 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.set_sorted.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.set_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.shape.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shape.html
index 0c68dd18eb5d..b1d4b8c3b667 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.shape.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shape.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.shift.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift.html
index 64064b79ec54..8188bdd79c5a 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.shift_and_fill.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
index cb3d351ade37..085e93edf47a 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shift_and_fill.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
index 4cf21a1fb892..f7074ef85d85 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.shrink_to_fit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.slice.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.slice.html
index 47c9ba7229bb..7be9535fe479 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.slice.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.slice.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.sort.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sort.html
index c0825d3a4f03..516cc1396a12 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.sort.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sort.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.std.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.std.html
index 0bf5873aa3c4..a18cdc88503b 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.std.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.std.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.sum.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sum.html
index ac57230aadfc..a8e6f06da5b9 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.sum.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.sum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.tail.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.tail.html
index 703a3d100158..aa2182b21c3a 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.tail.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.tail.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.take_every.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.take_every.html
index 8b164d7bd1ab..40f725bd998a 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.take_every.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.take_every.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_arrow.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_arrow.html
index 5c5c015457cb..bd7da97cf73c 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_arrow.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_arrow.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_dict.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dict.html
index 6b242cd2b775..3e7147fafb52 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dict.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dict.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_dicts.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dicts.html
index 12f33531c0d0..440fd02557de 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dicts.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dicts.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_dummies.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dummies.html
index c7bec1e37223..e64232626a6f 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dummies.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_dummies.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_init_repr.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_init_repr.html
index efad9ede0c36..b33bb81c5b5e 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_init_repr.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_init_repr.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_numpy.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_numpy.html
index 4666cf64965f..3e285134ad9f 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_numpy.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_numpy.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_pandas.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_pandas.html
index 306ff747230d..320f7d176c3d 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_pandas.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_pandas.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_series.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_series.html
index d08ce984aa09..4f7838d0f50c 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_series.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_series.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.to_struct.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_struct.html
index faafa5d894cc..3d610c746252 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_struct.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.to_struct.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.top_k.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.top_k.html
index ed6167ba584d..a02bdd2bb88e 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.top_k.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.top_k.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.transpose.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.transpose.html
index 9327edc8f1a2..8f9f468353e7 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.transpose.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.transpose.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.unique.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unique.html
index 00b23ce82403..3e2bdbd5e1a5 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.unique.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.unnest.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unnest.html
index c142079f44be..44427f0a7659 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.unnest.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unnest.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.unstack.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unstack.html
index 766558158855..cc4f1b56e543 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.unstack.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.unstack.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.update.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.update.html
index e0bfdcfaed5a..8ae342691516 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.update.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.update.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.upsample.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.upsample.html
index d53c0aa8f5fd..cce7ae1e7268 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.upsample.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.upsample.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.var.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.var.html
index fb110cbebb30..20c4e471a699 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.var.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.var.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.vstack.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.vstack.html
index 558b687c0fa0..a4a55c30e4f8 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.vstack.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.vstack.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.width.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.width.html
index 2c82a279b48b..51275d7a7a52 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.width.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.width.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.with_columns.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_columns.html
index dd2234e8b9d0..48aebdcbca03 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_columns.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_columns.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/api/polars.DataFrame.with_row_count.html b/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_row_count.html
index 3613ba95b435..b773d633f2a8 100644
--- a/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_row_count.html
+++ b/py-polars/dev/reference/dataframe/api/polars.DataFrame.with_row_count.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/dataframe/index.html b/py-polars/dev/reference/dataframe/index.html
index 484bca49b409..62dbdf97c91f 100644
--- a/py-polars/dev/reference/dataframe/index.html
+++ b/py-polars/dev/reference/dataframe/index.html
@@ -1617,7 +1617,7 @@ 
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:

@@ -2106,7 +2106,7 @@ 
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

@@ -2195,7 +2195,7 @@ 
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.

 

@@ -2267,7 +2267,7 @@ 
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.

@@ -2320,7 +2320,7 @@ 
DataFrame
 

-clone() → Self[source]

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

 

 
See also

@@ -2355,7 +2355,7 @@ 
DataFrame
 

-property columns: list[str][source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -2388,7 +2388,7 @@ 
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

@@ -2421,7 +2421,7 @@ 
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:

@@ -2473,7 +2473,7 @@ 
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:

@@ -2539,7 +2539,7 @@ 
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:

@@ -2577,7 +2577,7 @@ 
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.

 

@@ -2666,7 +2666,7 @@ 
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.

 

@@ -2703,7 +2703,7 @@ 
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
@@ -2741,7 +2741,7 @@ 
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:

@@ -2800,7 +2800,7 @@ 
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
@@ -2856,7 +2856,7 @@ 
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:

@@ -2908,7 +2908,7 @@ 
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:

@@ -3002,7 +3002,7 @@ 
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:

@@ -3064,7 +3064,7 @@ 
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:

@@ -3086,7 +3086,7 @@ 
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:

@@ -3100,7 +3100,7 @@ 
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
@@ -3188,7 +3188,7 @@ 
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:

@@ -3225,7 +3225,7 @@ 
DataFrame
 

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

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

 

 
Parameters:

@@ -3257,7 +3257,7 @@ 
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]})
@@ -3313,7 +3313,7 @@ 
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.

@@ -3360,7 +3360,7 @@ 
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:

@@ -3498,7 +3498,7 @@ 
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
@@ -3807,7 +3807,7 @@ 
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
@@ -3935,7 +3935,7 @@ 
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.

 

@@ -3974,7 +3974,7 @@ 
DataFrame
 

-head(n: int = 5) → Self[source]

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

 

 
Parameters:

@@ -4029,7 +4029,7 @@ 
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]})
@@ -4041,7 +4041,7 @@ 
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:

@@ -4079,7 +4079,7 @@ 
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:

@@ -4133,7 +4133,7 @@ 
DataFrame
 

-interpolate() → DataFrame[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -4161,7 +4161,7 @@ 
DataFrame
 

-is_duplicated() → Series[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -4198,7 +4198,7 @@ 
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]})
@@ -4212,7 +4212,7 @@ 
DataFrame
 

-is_unique() → Series[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -4249,7 +4249,7 @@ 
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:

@@ -4285,7 +4285,7 @@ 
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.

@@ -4347,7 +4347,7 @@ 
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:

@@ -4405,7 +4405,7 @@ 
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:

@@ -4551,7 +4551,7 @@ 
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.

@@ -4671,7 +4671,7 @@ 
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:

@@ -4727,7 +4727,7 @@ 
DataFrame
 

-limit(n: int = 5) → Self[source]

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

 
Alias for DataFrame.head().

 

@@ -4749,7 +4749,7 @@ 
DataFrame
 

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

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

 max(axis: Literal[1]) → Series

 

@@ -4778,7 +4778,7 @@ 
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

 

@@ -4826,7 +4826,7 @@ 
DataFrame
 

-median() → Self[source]

+median() → Self[source]

 
Aggregate the columns of this DataFrame to their median value.

 
Examples

 
>>> df = pl.DataFrame(
@@ -4851,7 +4851,7 @@ 
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
@@ -4901,7 +4901,7 @@ 
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
@@ -4968,7 +4968,7 @@ 
DataFrame
 

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

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

 min(axis: Literal[1]) → Series

 

@@ -4997,7 +4997,7 @@ 
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.

@@ -5028,7 +5028,7 @@ 
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:

@@ -5087,7 +5087,7 @@ 
DataFrame
 

-null_count() → Self[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -5112,7 +5112,7 @@ 
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.

@@ -5244,7 +5244,7 @@ 
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:

@@ -5309,7 +5309,7 @@ 
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:

@@ -5395,7 +5395,7 @@ 
DataFrame
 

-product() → DataFrame[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -5422,7 +5422,7 @@ 
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:

@@ -5457,7 +5457,7 @@ 
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.

@@ -5465,7 +5465,7 @@ 
DataFrame
 

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

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

 

 
Parameters:

@@ -5496,7 +5496,7 @@ 
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:

@@ -5528,7 +5528,7 @@ 
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:

@@ -5566,7 +5566,7 @@ 
DataFrame
 

-reverse() → DataFrame[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -5592,7 +5592,7 @@ 
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.

@@ -5665,7 +5665,7 @@ 
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.

@@ -5726,7 +5726,7 @@ 
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
@@ -5822,7 +5822,7 @@ 
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:

@@ -5868,7 +5868,7 @@ 
DataFrame
 

-property schema: SchemaDict[source]

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

 
Examples

 
>>> df = pl.DataFrame(
@@ -5886,7 +5886,7 @@ 
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:

@@ -5989,7 +5989,7 @@ 
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:

@@ -6007,7 +6007,7 @@ 
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]})
@@ -6019,7 +6019,7 @@ 
DataFrame
 

-shift(periods: int) → Self[source]

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

 

 
Parameters:

@@ -6071,7 +6071,7 @@ 
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:

@@ -6108,14 +6108,14 @@ 
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:

@@ -6152,7 +6152,7 @@ 
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:

@@ -6238,7 +6238,7 @@ 
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:

@@ -6282,7 +6282,7 @@ 
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

 

@@ -6329,7 +6329,7 @@ 
DataFrame
 

-tail(n: int = 5) → Self[source]

+tail(n: int = 5) → Self[source]

 
Get the last n rows.

 

 
Parameters:

@@ -6384,7 +6384,7 @@ 
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]})
@@ -6404,7 +6404,7 @@ 
DataFrame
 

-to_arrow() → Table[source]

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

 
This operation is mostly zero copy.

 

@@ -6430,7 +6430,7 @@ 
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]]

 

@@ -6522,7 +6522,7 @@ 
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
@@ -6539,7 +6539,7 @@ 
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:

@@ -6578,7 +6578,7 @@ 
DataFrame
 

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

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

 

 
Parameters:

@@ -6631,7 +6631,7 @@ 
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.

 

@@ -6690,7 +6690,7 @@ 
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.

@@ -6767,7 +6767,7 @@ 
DataFrame
 

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

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

 

 
Parameters:

@@ -6805,7 +6805,7 @@ 
DataFrame
 

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

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

 

 
Parameters:

@@ -6838,7 +6838,7 @@ 
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.

 

@@ -6910,7 +6910,7 @@ 
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:

@@ -7023,7 +7023,7 @@ 
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:

@@ -7107,7 +7107,7 @@ 
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.

@@ -7158,7 +7158,7 @@ 
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.

 

@@ -7234,7 +7234,7 @@ 
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:

@@ -7311,7 +7311,7 @@ 
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:

@@ -7401,7 +7401,7 @@ 
DataFrame
 

-var(ddof: int = 1) → Self[source]

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

 

 
Parameters:

@@ -7445,7 +7445,7 @@ 
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:

@@ -7496,7 +7496,7 @@ 
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]})
@@ -7508,7 +7508,7 @@ 
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.

 

@@ -7653,7 +7653,7 @@ 
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:

@@ -7689,7 +7689,7 @@ 
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:

@@ -7719,7 +7719,7 @@ 
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.

@@ -7777,7 +7777,7 @@ 
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:

@@ -7805,7 +7805,7 @@ 
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.

@@ -7885,7 +7885,7 @@ 
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:

@@ -8211,7 +8211,7 @@ 
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.

@@ -8244,7 +8244,7 @@ 
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.

@@ -8284,7 +8284,7 @@ 
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.

@@ -8312,7 +8312,7 @@ 
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/dev/reference/expressions/api/polars.Expr.str.concat.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.concat.html
index 697eb89d2746..76e102221a0f 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.concat.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.concat.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.contains.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.contains.html
index 8bf23d73ac24..41c4981fef7f 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.contains.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.contains.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.count_match.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.count_match.html
index f29c6f13e129..4083064d3daa 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.count_match.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.count_match.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.decode.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.decode.html
index ea300401ca99..8a8ded1fe670 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.decode.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.decode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.encode.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.encode.html
index 21495ac1ec3b..47e2a5d29e09 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.encode.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.encode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.ends_with.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.ends_with.html
index 8dc7e157565b..69204bff4986 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.ends_with.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.ends_with.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.explode.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.explode.html
index 091edda6fd8b..8faba968e464 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.explode.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.explode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.extract.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.extract.html
index 6adbeddd0b66..b98230faed50 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.extract.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.extract.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.extract_all.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.extract_all.html
index 3b7d18df5ec2..3b0000788258 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.extract_all.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.extract_all.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.json_extract.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.json_extract.html
index adc444204009..3b584f21bc18 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.json_extract.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.json_extract.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.json_path_match.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.json_path_match.html
index 40de8cfdbd1d..4b487688919c 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.json_path_match.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.json_path_match.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.lengths.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.lengths.html
index 2cb0c8ba69e9..0d30ba4eddea 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.lengths.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.lengths.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.ljust.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.ljust.html
index aa10bcc22af2..e3ce8fb5ed84 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.ljust.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.ljust.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.lstrip.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.lstrip.html
index 4f5e93e7d700..dabebefc7c23 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.lstrip.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.lstrip.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.n_chars.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.n_chars.html
index 0d771ecd470e..8aa94753c367 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.n_chars.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.n_chars.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.parse_int.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.parse_int.html
index 4149ad26d054..49833477078d 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.parse_int.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.parse_int.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.replace.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.replace.html
index f318318d5e50..6f76f1e95d2b 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.replace.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.replace.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.replace_all.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.replace_all.html
index cbfe47f8c5f8..ffa59a7f9cbd 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.replace_all.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.replace_all.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.rjust.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.rjust.html
index 95f3f4504acf..eef832610357 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.rjust.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.rjust.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.rstrip.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.rstrip.html
index fe4232d75f78..6d620646887f 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.rstrip.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.rstrip.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.slice.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.slice.html
index dc0c57f303ea..8dc0aedb42b0 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.slice.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.slice.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.split.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.split.html
index 5c041475b29c..3287c4543c42 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.split.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.split.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.split_exact.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.split_exact.html
index b41e18e605d5..b5d3692caa52 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.split_exact.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.split_exact.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.splitn.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.splitn.html
index 3d57fe208b46..bd3e1a6cd542 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.splitn.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.splitn.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.starts_with.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.starts_with.html
index 7afbdf6db4d2..be6e20c6ef66 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.starts_with.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.starts_with.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.strip.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.strip.html
index 484d23b0c672..6feb2ac6e6cb 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.strip.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.strip.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.strptime.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.strptime.html
index 0f083e3c3a0b..c13c4dab87bc 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.strptime.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.strptime.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_datetime.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_datetime.html
index 205facf9bdcd..90487827e555 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_datetime.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_datetime.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_decimal.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_decimal.html
index a1dd809ae9be..c4aff352fe56 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_decimal.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_decimal.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_lowercase.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_lowercase.html
index e025e3a59f09..18c353da9576 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_lowercase.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_lowercase.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_time.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_time.html
index e8ab240a1972..3f5b97a5d288 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_time.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_time.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_titlecase.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_titlecase.html
index 89dc793b3bce..e9554f5d6d2e 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_titlecase.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_titlecase.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.to_uppercase.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_uppercase.html
index c0b428f615d3..c64863a0d5f9 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.to_uppercase.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.to_uppercase.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.Expr.str.zfill.html b/py-polars/dev/reference/expressions/api/polars.Expr.str.zfill.html
index 3fa256dd0e8a..6fc57e902e5f 100644
--- a/py-polars/dev/reference/expressions/api/polars.Expr.str.zfill.html
+++ b/py-polars/dev/reference/expressions/api/polars.Expr.str.zfill.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.all.html b/py-polars/dev/reference/expressions/api/polars.all.html
index f99a8dfd6472..60e5ea46456e 100644
--- a/py-polars/dev/reference/expressions/api/polars.all.html
+++ b/py-polars/dev/reference/expressions/api/polars.all.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.any.html b/py-polars/dev/reference/expressions/api/polars.any.html
index f4b895c683e1..d29ab13d868b 100644
--- a/py-polars/dev/reference/expressions/api/polars.any.html
+++ b/py-polars/dev/reference/expressions/api/polars.any.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.apply.html b/py-polars/dev/reference/expressions/api/polars.apply.html
index 5dad2998d22b..cb272be711b2 100644
--- a/py-polars/dev/reference/expressions/api/polars.apply.html
+++ b/py-polars/dev/reference/expressions/api/polars.apply.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.approx_unique.html b/py-polars/dev/reference/expressions/api/polars.approx_unique.html
index e6c90994bf49..2565fd850d6a 100644
--- a/py-polars/dev/reference/expressions/api/polars.approx_unique.html
+++ b/py-polars/dev/reference/expressions/api/polars.approx_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.arange.html b/py-polars/dev/reference/expressions/api/polars.arange.html
index be1255cac8f3..cecd2dcf8245 100644
--- a/py-polars/dev/reference/expressions/api/polars.arange.html
+++ b/py-polars/dev/reference/expressions/api/polars.arange.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.arctan2.html b/py-polars/dev/reference/expressions/api/polars.arctan2.html
index 970363a315a5..00a2e8f2610f 100644
--- a/py-polars/dev/reference/expressions/api/polars.arctan2.html
+++ b/py-polars/dev/reference/expressions/api/polars.arctan2.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.arctan2d.html b/py-polars/dev/reference/expressions/api/polars.arctan2d.html
index 9eef41c206ef..346e94b8892d 100644
--- a/py-polars/dev/reference/expressions/api/polars.arctan2d.html
+++ b/py-polars/dev/reference/expressions/api/polars.arctan2d.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.arg_sort_by.html b/py-polars/dev/reference/expressions/api/polars.arg_sort_by.html
index 5f91996e1b89..408a4e50c6b5 100644
--- a/py-polars/dev/reference/expressions/api/polars.arg_sort_by.html
+++ b/py-polars/dev/reference/expressions/api/polars.arg_sort_by.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.arg_where.html b/py-polars/dev/reference/expressions/api/polars.arg_where.html
index c6eca32c9e04..163245885027 100644
--- a/py-polars/dev/reference/expressions/api/polars.arg_where.html
+++ b/py-polars/dev/reference/expressions/api/polars.arg_where.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.avg.html b/py-polars/dev/reference/expressions/api/polars.avg.html
index 3862ec76b9c0..b27527efeb91 100644
--- a/py-polars/dev/reference/expressions/api/polars.avg.html
+++ b/py-polars/dev/reference/expressions/api/polars.avg.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.coalesce.html b/py-polars/dev/reference/expressions/api/polars.coalesce.html
index 2fc0b66993f9..546cc19a3bd3 100644
--- a/py-polars/dev/reference/expressions/api/polars.coalesce.html
+++ b/py-polars/dev/reference/expressions/api/polars.coalesce.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.col.html b/py-polars/dev/reference/expressions/api/polars.col.html
index c102a22a3149..a6ac54732cb0 100644
--- a/py-polars/dev/reference/expressions/api/polars.col.html
+++ b/py-polars/dev/reference/expressions/api/polars.col.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.concat_list.html b/py-polars/dev/reference/expressions/api/polars.concat_list.html
index 901a2f317b9b..9124aecebd7e 100644
--- a/py-polars/dev/reference/expressions/api/polars.concat_list.html
+++ b/py-polars/dev/reference/expressions/api/polars.concat_list.html
@@ -1626,7 +1626,7 @@
 
polars.concat_list#

 

 

-polars.concat_list(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr[source]#

+polars.concat_list(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) → Expr[source]#
 
Horizontally concatenate columns into a single list column.

 
Operates in linear time.

 

diff --git a/py-polars/dev/reference/expressions/api/polars.concat_str.html b/py-polars/dev/reference/expressions/api/polars.concat_str.html
index 0ebad6b070d7..3ed17779bd7b 100644
--- a/py-polars/dev/reference/expressions/api/polars.concat_str.html
+++ b/py-polars/dev/reference/expressions/api/polars.concat_str.html
@@ -1626,7 +1626,7 @@
 
polars.concat_str#

 

 

-polars.concat_str(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, separator: str = '') → Expr[source]#

+polars.concat_str(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr, separator: str = '') → Expr[source]#
 
Horizontally concatenate columns into a single string column.

 
Operates in linear time.

 

diff --git a/py-polars/dev/reference/expressions/api/polars.corr.html b/py-polars/dev/reference/expressions/api/polars.corr.html
index 18dd258bfeaa..f051bf07d971 100644
--- a/py-polars/dev/reference/expressions/api/polars.corr.html
+++ b/py-polars/dev/reference/expressions/api/polars.corr.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.count.html b/py-polars/dev/reference/expressions/api/polars.count.html
index a1db30b81996..8bc1f2d74611 100644
--- a/py-polars/dev/reference/expressions/api/polars.count.html
+++ b/py-polars/dev/reference/expressions/api/polars.count.html
@@ -1626,7 +1626,7 @@
 
polars.count#

 

 

-polars.count(column: str) → Expr[source]#

+polars.count(column: str) → Expr[source]#
 

 polars.count(column: Series) → int

 

diff --git a/py-polars/dev/reference/expressions/api/polars.cov.html b/py-polars/dev/reference/expressions/api/polars.cov.html
index fe05c26f3f8a..7ac0c5e8a7c9 100644
--- a/py-polars/dev/reference/expressions/api/polars.cov.html
+++ b/py-polars/dev/reference/expressions/api/polars.cov.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.cumfold.html b/py-polars/dev/reference/expressions/api/polars.cumfold.html
index 5df5c181264a..dfc908feec5b 100644
--- a/py-polars/dev/reference/expressions/api/polars.cumfold.html
+++ b/py-polars/dev/reference/expressions/api/polars.cumfold.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.cumreduce.html b/py-polars/dev/reference/expressions/api/polars.cumreduce.html
index 11d1a385c043..d3cedae5d24e 100644
--- a/py-polars/dev/reference/expressions/api/polars.cumreduce.html
+++ b/py-polars/dev/reference/expressions/api/polars.cumreduce.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.cumsum.html b/py-polars/dev/reference/expressions/api/polars.cumsum.html
index 7930af1390ae..7b05fdc04662 100644
--- a/py-polars/dev/reference/expressions/api/polars.cumsum.html
+++ b/py-polars/dev/reference/expressions/api/polars.cumsum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.date.html b/py-polars/dev/reference/expressions/api/polars.date.html
index 08d560a94e1b..d3e65ebc235e 100644
--- a/py-polars/dev/reference/expressions/api/polars.date.html
+++ b/py-polars/dev/reference/expressions/api/polars.date.html
@@ -1626,7 +1626,7 @@
 
polars.date#

 

 

-polars.date(year: Expr | str | int, month: Expr | str | int, day: Expr | str | int) → Expr[source]#

+polars.date(year: Expr | str | int, month: Expr | str | int, day: Expr | str | int) → Expr[source]#
 
Create a Polars literal expression of type Date.

 

 
Parameters:

diff --git a/py-polars/dev/reference/expressions/api/polars.date_range.html b/py-polars/dev/reference/expressions/api/polars.date_range.html
index 44a45b43e445..82267e1bdef0 100644
--- a/py-polars/dev/reference/expressions/api/polars.date_range.html
+++ b/py-polars/dev/reference/expressions/api/polars.date_range.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.date_ranges.html b/py-polars/dev/reference/expressions/api/polars.date_ranges.html
index 162bc95b2047..83c23e9aa547 100644
--- a/py-polars/dev/reference/expressions/api/polars.date_ranges.html
+++ b/py-polars/dev/reference/expressions/api/polars.date_ranges.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.datetime.html b/py-polars/dev/reference/expressions/api/polars.datetime.html
index 0105bd5927b3..465098e7e39c 100644
--- a/py-polars/dev/reference/expressions/api/polars.datetime.html
+++ b/py-polars/dev/reference/expressions/api/polars.datetime.html
@@ -1626,7 +1626,7 @@
 
polars.datetime#

 

 

-polars.datetime(year: Expr | str | int, month: Expr | str | int, day: Expr | str | int, hour: Expr | str | int | None = None, minute: Expr | str | int | None = None, second: Expr | str | int | None = None, microsecond: Expr | str | int | None = None) → Expr[source]#

+polars.datetime(year: Expr | str | int, month: Expr | str | int, day: Expr | str | int, hour: Expr | str | int | None = None, minute: Expr | str | int | None = None, second: Expr | str | int | None = None, microsecond: Expr | str | int | None = None) → Expr[source]#

 
Create a Polars literal expression of type Datetime.

 

 
Parameters:

diff --git a/py-polars/dev/reference/expressions/api/polars.duration.html b/py-polars/dev/reference/expressions/api/polars.duration.html
index 9fdda34497d0..dafac8e7d505 100644
--- a/py-polars/dev/reference/expressions/api/polars.duration.html
+++ b/py-polars/dev/reference/expressions/api/polars.duration.html
@@ -1626,7 +1626,7 @@
 
polars.duration#

 

 

-polars.duration(*, days: Expr | str | int | None = None, seconds: Expr | str | int | None = None, nanoseconds: Expr | str | int | None = None, microseconds: Expr | str | int | None = None, milliseconds: Expr | str | int | None = None, minutes: Expr | str | int | None = None, hours: Expr | str | int | None = None, weeks: Expr | str | int | None = None) → Expr[source]#

+polars.duration(*, days: Expr | str | int | None = None, seconds: Expr | str | int | None = None, nanoseconds: Expr | str | int | None = None, microseconds: Expr | str | int | None = None, milliseconds: Expr | str | int | None = None, minutes: Expr | str | int | None = None, hours: Expr | str | int | None = None, weeks: Expr | str | int | None = None) → Expr[source]#
 
Create polars Duration from distinct time components.

 

 
Returns:

diff --git a/py-polars/dev/reference/expressions/api/polars.element.html b/py-polars/dev/reference/expressions/api/polars.element.html
index 97fb18e8d6dd..9c4fbcb2b03a 100644
--- a/py-polars/dev/reference/expressions/api/polars.element.html
+++ b/py-polars/dev/reference/expressions/api/polars.element.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.exclude.html b/py-polars/dev/reference/expressions/api/polars.exclude.html
index d0fb6e68c815..e22336506820 100644
--- a/py-polars/dev/reference/expressions/api/polars.exclude.html
+++ b/py-polars/dev/reference/expressions/api/polars.exclude.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.first.html b/py-polars/dev/reference/expressions/api/polars.first.html
index c2ad2d273a9f..99d1c1fcd6d1 100644
--- a/py-polars/dev/reference/expressions/api/polars.first.html
+++ b/py-polars/dev/reference/expressions/api/polars.first.html
@@ -1626,7 +1626,7 @@
 
polars.first#

 

 

-polars.first(column: str) → Expr[source]#

+polars.first(column: str) → Expr[source]#
 

 polars.first(column: Series) → Any

 

diff --git a/py-polars/dev/reference/expressions/api/polars.fold.html b/py-polars/dev/reference/expressions/api/polars.fold.html
index e618c257213c..9ae0d39bb100 100644
--- a/py-polars/dev/reference/expressions/api/polars.fold.html
+++ b/py-polars/dev/reference/expressions/api/polars.fold.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.format.html b/py-polars/dev/reference/expressions/api/polars.format.html
index 3b318522b6c4..3cfaf129b5ca 100644
--- a/py-polars/dev/reference/expressions/api/polars.format.html
+++ b/py-polars/dev/reference/expressions/api/polars.format.html
@@ -1626,7 +1626,7 @@
 
polars.format#

 

 

-polars.format(f_string: str, *args: Expr | str) → Expr[source]#

+polars.format(f_string: str, *args: Expr | str) → Expr[source]#
 
Format expressions as a string.

 

 
Parameters:

diff --git a/py-polars/dev/reference/expressions/api/polars.from_epoch.html b/py-polars/dev/reference/expressions/api/polars.from_epoch.html
index 92900b632821..eef93ce51584 100644
--- a/py-polars/dev/reference/expressions/api/polars.from_epoch.html
+++ b/py-polars/dev/reference/expressions/api/polars.from_epoch.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.groups.html b/py-polars/dev/reference/expressions/api/polars.groups.html
index 789ed0bf6941..beeb6c1ba25c 100644
--- a/py-polars/dev/reference/expressions/api/polars.groups.html
+++ b/py-polars/dev/reference/expressions/api/polars.groups.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.head.html b/py-polars/dev/reference/expressions/api/polars.head.html
index 7888f0936abc..77467d941530 100644
--- a/py-polars/dev/reference/expressions/api/polars.head.html
+++ b/py-polars/dev/reference/expressions/api/polars.head.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.implode.html b/py-polars/dev/reference/expressions/api/polars.implode.html
index 9f684ff70968..79e7f91bafbd 100644
--- a/py-polars/dev/reference/expressions/api/polars.implode.html
+++ b/py-polars/dev/reference/expressions/api/polars.implode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.int_range.html b/py-polars/dev/reference/expressions/api/polars.int_range.html
index a0aa356657a0..c9b5d50c8a16 100644
--- a/py-polars/dev/reference/expressions/api/polars.int_range.html
+++ b/py-polars/dev/reference/expressions/api/polars.int_range.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.int_ranges.html b/py-polars/dev/reference/expressions/api/polars.int_ranges.html
index b644644a2ee3..664f5f6592ce 100644
--- a/py-polars/dev/reference/expressions/api/polars.int_ranges.html
+++ b/py-polars/dev/reference/expressions/api/polars.int_ranges.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.last.html b/py-polars/dev/reference/expressions/api/polars.last.html
index ad426caa297b..c60d34e371cc 100644
--- a/py-polars/dev/reference/expressions/api/polars.last.html
+++ b/py-polars/dev/reference/expressions/api/polars.last.html
@@ -1626,7 +1626,7 @@
 
polars.last#

 

 

-polars.last(column: str) → Expr[source]#

+polars.last(column: str) → Expr[source]#

 

 polars.last(column: Series) → Any

 

diff --git a/py-polars/dev/reference/expressions/api/polars.lit.html b/py-polars/dev/reference/expressions/api/polars.lit.html
index e06ade8e852e..7b9546559e3a 100644
--- a/py-polars/dev/reference/expressions/api/polars.lit.html
+++ b/py-polars/dev/reference/expressions/api/polars.lit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.map.html b/py-polars/dev/reference/expressions/api/polars.map.html
index 185e326cf342..f087aa250eb5 100644
--- a/py-polars/dev/reference/expressions/api/polars.map.html
+++ b/py-polars/dev/reference/expressions/api/polars.map.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.max.html b/py-polars/dev/reference/expressions/api/polars.max.html
index 230f8d032c8a..1dc8d36984a7 100644
--- a/py-polars/dev/reference/expressions/api/polars.max.html
+++ b/py-polars/dev/reference/expressions/api/polars.max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.mean.html b/py-polars/dev/reference/expressions/api/polars.mean.html
index 2b59c90e6103..0e040a5c7dab 100644
--- a/py-polars/dev/reference/expressions/api/polars.mean.html
+++ b/py-polars/dev/reference/expressions/api/polars.mean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.median.html b/py-polars/dev/reference/expressions/api/polars.median.html
index 4c1cc5e84825..30b8ea1ced31 100644
--- a/py-polars/dev/reference/expressions/api/polars.median.html
+++ b/py-polars/dev/reference/expressions/api/polars.median.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.min.html b/py-polars/dev/reference/expressions/api/polars.min.html
index 004e8243a4cc..5cfe866363c2 100644
--- a/py-polars/dev/reference/expressions/api/polars.min.html
+++ b/py-polars/dev/reference/expressions/api/polars.min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.n_unique.html b/py-polars/dev/reference/expressions/api/polars.n_unique.html
index 2a27abb52f3e..8ebad8999283 100644
--- a/py-polars/dev/reference/expressions/api/polars.n_unique.html
+++ b/py-polars/dev/reference/expressions/api/polars.n_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.ones.html b/py-polars/dev/reference/expressions/api/polars.ones.html
index 7f9b13bc447e..7ea1703b36be 100644
--- a/py-polars/dev/reference/expressions/api/polars.ones.html
+++ b/py-polars/dev/reference/expressions/api/polars.ones.html
@@ -1626,7 +1626,7 @@
 
polars.ones#

 

 

-polars.ones(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[False] = False) → Expr[source]#

+polars.ones(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[False] = False) → Expr[source]#
 

 polars.ones(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[True]) → Series

 

diff --git a/py-polars/dev/reference/expressions/api/polars.quantile.html b/py-polars/dev/reference/expressions/api/polars.quantile.html
index ee37c16fa47e..6c7ce6dcd611 100644
--- a/py-polars/dev/reference/expressions/api/polars.quantile.html
+++ b/py-polars/dev/reference/expressions/api/polars.quantile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.reduce.html b/py-polars/dev/reference/expressions/api/polars.reduce.html
index d1a221c2775f..08838635c162 100644
--- a/py-polars/dev/reference/expressions/api/polars.reduce.html
+++ b/py-polars/dev/reference/expressions/api/polars.reduce.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.repeat.html b/py-polars/dev/reference/expressions/api/polars.repeat.html
index 18e7b244e7d5..36aed860cfef 100644
--- a/py-polars/dev/reference/expressions/api/polars.repeat.html
+++ b/py-polars/dev/reference/expressions/api/polars.repeat.html
@@ -1626,7 +1626,7 @@
 
polars.repeat#

 

 

-polars.repeat(value: IntoExpr | None, n: int | Expr, *, dtype: PolarsDataType | None = None, eager: Literal[False] = False, name: str | None = None) → Expr[source]#

+polars.repeat(value: IntoExpr | None, n: int | Expr, *, dtype: PolarsDataType | None = None, eager: Literal[False] = False, name: str | None = None) → Expr[source]#
 

 polars.repeat(value: IntoExpr | None, n: int | Expr, *, dtype: PolarsDataType | None = None, eager: Literal[True], name: str | None = None) → Series

 

diff --git a/py-polars/dev/reference/expressions/api/polars.rolling_corr.html b/py-polars/dev/reference/expressions/api/polars.rolling_corr.html
index 2fb04a24d9b6..ad69b786e78c 100644
--- a/py-polars/dev/reference/expressions/api/polars.rolling_corr.html
+++ b/py-polars/dev/reference/expressions/api/polars.rolling_corr.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.rolling_cov.html b/py-polars/dev/reference/expressions/api/polars.rolling_cov.html
index 5ef823c6cd63..6a77f64787f3 100644
--- a/py-polars/dev/reference/expressions/api/polars.rolling_cov.html
+++ b/py-polars/dev/reference/expressions/api/polars.rolling_cov.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.select.html b/py-polars/dev/reference/expressions/api/polars.select.html
index e995beaeb204..09201531f853 100644
--- a/py-polars/dev/reference/expressions/api/polars.select.html
+++ b/py-polars/dev/reference/expressions/api/polars.select.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.sql_expr.html b/py-polars/dev/reference/expressions/api/polars.sql_expr.html
index da8c96dacb50..9d8a039a7b28 100644
--- a/py-polars/dev/reference/expressions/api/polars.sql_expr.html
+++ b/py-polars/dev/reference/expressions/api/polars.sql_expr.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.std.html b/py-polars/dev/reference/expressions/api/polars.std.html
index 26b84298f3bf..30794ba47d93 100644
--- a/py-polars/dev/reference/expressions/api/polars.std.html
+++ b/py-polars/dev/reference/expressions/api/polars.std.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.struct.html b/py-polars/dev/reference/expressions/api/polars.struct.html
index 4117682ade82..c15e09e51cce 100644
--- a/py-polars/dev/reference/expressions/api/polars.struct.html
+++ b/py-polars/dev/reference/expressions/api/polars.struct.html
@@ -1626,7 +1626,7 @@
 
polars.struct#

 

 

-polars.struct(*exprs: IntoExpr | Iterable[IntoExpr], schema: SchemaDict | None = None, eager: Literal[False] = False, **named_exprs: IntoExpr) → Expr[source]#

+polars.struct(*exprs: IntoExpr | Iterable[IntoExpr], schema: SchemaDict | None = None, eager: Literal[False] = False, **named_exprs: IntoExpr) → Expr[source]#
 

 polars.struct(*exprs: IntoExpr | Iterable[IntoExpr], schema: SchemaDict | None = None, eager: Literal[True], **named_exprs: IntoExpr) → Series

 

diff --git a/py-polars/dev/reference/expressions/api/polars.sum.html b/py-polars/dev/reference/expressions/api/polars.sum.html
index bdeaa12813f4..e490840190ce 100644
--- a/py-polars/dev/reference/expressions/api/polars.sum.html
+++ b/py-polars/dev/reference/expressions/api/polars.sum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.tail.html b/py-polars/dev/reference/expressions/api/polars.tail.html
index b16e46fe9a5d..1ce8a74a99f7 100644
--- a/py-polars/dev/reference/expressions/api/polars.tail.html
+++ b/py-polars/dev/reference/expressions/api/polars.tail.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.time.html b/py-polars/dev/reference/expressions/api/polars.time.html
index 67c492500384..8dff91db44e2 100644
--- a/py-polars/dev/reference/expressions/api/polars.time.html
+++ b/py-polars/dev/reference/expressions/api/polars.time.html
@@ -1626,7 +1626,7 @@
 
polars.time#

 

 

-polars.time(hour: Expr | str | int | None = None, minute: Expr | str | int | None = None, second: Expr | str | int | None = None, microsecond: Expr | str | int | None = None) → Expr[source]#

+polars.time(hour: Expr | str | int | None = None, minute: Expr | str | int | None = None, second: Expr | str | int | None = None, microsecond: Expr | str | int | None = None) → Expr[source]#
 
Create a Polars literal expression of type Time.

 

 
Parameters:

diff --git a/py-polars/dev/reference/expressions/api/polars.time_range.html b/py-polars/dev/reference/expressions/api/polars.time_range.html
index b31d0f0b9eee..734a921dbb09 100644
--- a/py-polars/dev/reference/expressions/api/polars.time_range.html
+++ b/py-polars/dev/reference/expressions/api/polars.time_range.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.time_ranges.html b/py-polars/dev/reference/expressions/api/polars.time_ranges.html
index 0de824b01e8f..771402f9d55a 100644
--- a/py-polars/dev/reference/expressions/api/polars.time_ranges.html
+++ b/py-polars/dev/reference/expressions/api/polars.time_ranges.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.var.html b/py-polars/dev/reference/expressions/api/polars.var.html
index bc1e27934728..73ad49544d18 100644
--- a/py-polars/dev/reference/expressions/api/polars.var.html
+++ b/py-polars/dev/reference/expressions/api/polars.var.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/expressions/api/polars.zeros.html b/py-polars/dev/reference/expressions/api/polars.zeros.html
index 6df97cee2f7e..785a4857e8f2 100644
--- a/py-polars/dev/reference/expressions/api/polars.zeros.html
+++ b/py-polars/dev/reference/expressions/api/polars.zeros.html
@@ -1626,7 +1626,7 @@
 
polars.zeros#

 

 

-polars.zeros(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[False] = False) → Expr[source]#

+polars.zeros(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[False] = False) → Expr[source]#
 

 polars.zeros(n: int | Expr, dtype: PolarsDataType = Float64, *, eager: Literal[True]) → Series

 

diff --git a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.bottom_k.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
index 089170b5659c..a140a2b183a1 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.bottom_k.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.cache.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.cache.html
index dbc5ac2a97b0..8a9810618ef1 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.cache.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.cache.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.clear.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clear.html
index d0bfcc443fa9..9913856473ea 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clear.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clear.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.clone.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clone.html
index a459d03b91a8..f69c903336f0 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clone.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.clone.html
@@ -1626,7 +1626,7 @@
 
polars.LazyFrame.clone#

 

 

-LazyFrame.clone() → Self[source]#

+LazyFrame.clone() → Self[source]#
 
Very cheap deepcopy/clone.

 

 
See also

diff --git a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.collect.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.collect.html
index 45fbb0c1289a..3f5aafe9d5b1 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.collect.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.collect.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.columns.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.columns.html
index cf2c75e551fb..1aa4fe234e3c 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.columns.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.columns.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.drop.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop.html
index 9edd1adfbf99..aa60ca7e62a1 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
index 7567bcd2081e..07ffb33917b6 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.drop_nulls.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.dtypes.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.dtypes.html
index 242cbbacd158..f6b887ab1c48 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.dtypes.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.dtypes.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.explain.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explain.html
index 5627b75ca7c4..0b64c0eddd26 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explain.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explain.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.explode.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explode.html
index 7214c658c91f..d38d76c8e3e1 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explode.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.explode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.fetch.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fetch.html
index 149ada202573..86c7a7352cf6 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fetch.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fetch.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.fill_nan.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
index a8476bb9f681..645fa05ee22b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_nan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.fill_null.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_null.html
index c7302f4b62ba..53999093d7ab 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_null.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.fill_null.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.filter.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.filter.html
index b75fc3fea7ca..7c34c43274d3 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.filter.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.filter.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.first.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.first.html
index 3ae6599750ca..480358dd0b72 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.first.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.first.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.from_json.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.from_json.html
index 2c1e78a46e74..592e30f49012 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.from_json.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.from_json.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.groupby.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby.html
index d53da3dd2d06..79240f3da253 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
index b58b14b5df32..ed4302e181d7 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_dynamic.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
index 3d05dcb1fddb..e4b0c653932e 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.groupby_rolling.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.head.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.head.html
index 4e25a393b352..e03ee89174f4 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.head.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.head.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.inspect.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.inspect.html
index d98a40fb8090..7c8594f90297 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.inspect.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.inspect.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.interpolate.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.interpolate.html
index f01733e15140..e399ef3aa19b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.interpolate.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.interpolate.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.join.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join.html
index 4873c1715115..cf560b0b854f 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.join_asof.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join_asof.html
index 277d488caa50..2f73914b514b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join_asof.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.join_asof.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.last.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.last.html
index 609a3318ba1e..a5f5b0fed3e2 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.last.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.last.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.lazy.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.lazy.html
index 024d99970f07..47c5beaef53d 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.lazy.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.lazy.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.limit.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.limit.html
index d9881f88cd63..04b2cb45ac10 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.limit.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.limit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.map.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.map.html
index 271fdc5fcdb4..6b2d20dc54a4 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.map.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.map.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.max.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.max.html
index 30adcd499d47..74550b1da1bf 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.max.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.mean.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.mean.html
index 1ec8af32ea40..25c32a54088b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.mean.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.mean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.median.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.median.html
index 98cdd10d931f..7c2c928a01e6 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.median.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.median.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.melt.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.melt.html
index 8c4c91aab052..daea7290c1ce 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.melt.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.melt.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
index 257344d3b71b..913b2007ae5b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.merge_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.min.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.min.html
index c00325de35e4..5333066758aa 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.min.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.null_count.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.null_count.html
index 41d4e6abf8d7..3028af0dbed8 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.null_count.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.null_count.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.pipe.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.pipe.html
index 45cf20d898ee..36fac421fc8d 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.pipe.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.pipe.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.profile.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.profile.html
index bcb5c9774f39..7bda6978f99f 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.profile.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.profile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.quantile.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.quantile.html
index f70071da4a0c..8025d65a3d72 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.quantile.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.quantile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.read_json.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.read_json.html
index e3c6ed0e2e8f..97ab2afdc8aa 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.read_json.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.read_json.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.rename.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.rename.html
index ecc114a379a6..4a863a643f6f 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.rename.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.rename.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.reverse.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.reverse.html
index ced1601ad576..c19a423292f7 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.reverse.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.reverse.html
@@ -1626,7 +1626,7 @@
 
polars.LazyFrame.reverse#

 

 

-LazyFrame.reverse() → Self[source]#

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

 
Examples

 
>>> lf = pl.LazyFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.schema.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.schema.html
index 1bad687e7713..0513dcc1a31c 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.schema.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.schema.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.select.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.select.html
index a0fd1014216b..1d0d6865ce49 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.select.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.select.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.set_sorted.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
index da8f289ddfa4..8546a968b3d4 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.set_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.shift.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift.html
index 25482686a732..e4e1479b678a 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
index 5130cd151651..3eb6882addcc 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.shift_and_fill.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.show_graph.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.show_graph.html
index 81691dcf571f..d9ab1e4681f3 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.show_graph.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.show_graph.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.slice.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.slice.html
index 14b98f0e9366..5ebd907f8377 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.slice.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.slice.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.sort.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sort.html
index ce4de8a45837..5223401675c7 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sort.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sort.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.std.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.std.html
index 5027a243e424..d7e5ede5db42 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.std.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.std.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.sum.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sum.html
index 526167b267b0..d2c10358a75b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sum.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.sum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.tail.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.tail.html
index 977b0d774e8f..948fbe09ac8b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.tail.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.tail.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.take_every.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.take_every.html
index 6bc6a832d0f9..094e4390c467 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.take_every.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.take_every.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.top_k.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.top_k.html
index 79611bd1466c..f2efa204d644 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.top_k.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.top_k.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.unique.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unique.html
index 7fa709940cb3..e69dd38d4d27 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unique.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.unnest.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unnest.html
index 43e1207cfebe..6a6b1bab299f 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unnest.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.unnest.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.update.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.update.html
index 5c23aa4f42ed..233b19c13581 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.update.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.update.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.var.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.var.html
index 7dbf84990277..8d4328d58b92 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.var.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.var.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.width.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.width.html
index 2d62aacdbe68..81350d6afd1b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.width.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.width.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.with_columns.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_columns.html
index e1a8025ba292..df61e8c49061 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_columns.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_columns.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.with_context.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_context.html
index 92c1ff1d1250..9f1c5846dfe9 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_context.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_context.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.with_row_count.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
index a1d9426ab9d0..f80d3e20f7b7 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.with_row_count.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.LazyFrame.write_json.html b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.write_json.html
index 0ce82289412b..08e3ac3b0245 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.write_json.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.LazyFrame.write_json.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.agg.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.agg.html
index 1e1e5970fe57..e275acde158b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.agg.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.agg.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.agg#

 

 

-LazyGroupBy.agg(*aggs: IntoExpr | Iterable[IntoExpr], **named_aggs: IntoExpr) → LazyFrame[source]#

+LazyGroupBy.agg(*aggs: IntoExpr | Iterable[IntoExpr], **named_aggs: IntoExpr) → LazyFrame[source]#
 
Compute aggregations for each group of a groupby operation.

 

 
Parameters:

diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.all.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.all.html
index 5c7941a09eef..e2306177d97e 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.all.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.all.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.all#

 

 

-LazyGroupBy.all() → LazyFrame[source]#

+LazyGroupBy.all() → LazyFrame[source]#
 
Aggregate the groups into Series.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.apply.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.apply.html
index 0a83dbec074f..d9943736ecc3 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.apply.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.apply.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.apply#

 

 

-LazyGroupBy.apply(function: Callable[[DataFrame], DataFrame], schema: SchemaDict | None) → LazyFrame[source]#

+LazyGroupBy.apply(function: Callable[[DataFrame], DataFrame], schema: SchemaDict | None) → LazyFrame[source]#
 
Apply a custom/user-defined function (UDF) over the groups as a new DataFrame.

 

 
Warning

diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.count.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.count.html
index 635f955af64b..da078180e596 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.count.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.count.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.count#

 

 

-LazyGroupBy.count() → LazyFrame[source]#

+LazyGroupBy.count() → LazyFrame[source]#
 
Count the number of values in each group.

 

 
Warning

diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.first.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.first.html
index 164720b383a8..8148b204089b 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.first.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.first.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.first#

 

 

-LazyGroupBy.first() → LazyFrame[source]#

+LazyGroupBy.first() → LazyFrame[source]#
 
Aggregate the first values in the group.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.head.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.head.html
index 8e5c58f83dea..d4f5c4adaf39 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.head.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.head.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.head#

 

 

-LazyGroupBy.head(n: int = 5) → LazyFrame[source]#

+LazyGroupBy.head(n: int = 5) → LazyFrame[source]#
 
Get the first n rows of each group.

 

 
Parameters:

diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.last.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.last.html
index 9e1a43610ac3..368ba8c75698 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.last.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.last.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.last#

 

 

-LazyGroupBy.last() → LazyFrame[source]#

+LazyGroupBy.last() → LazyFrame[source]#
 
Aggregate the last values in the group.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.max.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.max.html
index 7348464ef198..22ab69bb561d 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.max.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.max.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.max#

 

 

-LazyGroupBy.max() → LazyFrame[source]#

+LazyGroupBy.max() → LazyFrame[source]#
 
Reduce the groups to the maximal value.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.mean.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.mean.html
index 4d52fc9dab64..573a8750aa17 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.mean.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.mean.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.mean#

 

 

-LazyGroupBy.mean() → LazyFrame[source]#

+LazyGroupBy.mean() → LazyFrame[source]#
 
Reduce the groups to the mean values.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.median.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.median.html
index 0cd5f0e9ba03..c67364dadc0a 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.median.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.median.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.median#

 

 

-LazyGroupBy.median() → LazyFrame[source]#

+LazyGroupBy.median() → LazyFrame[source]#
 
Return the median per group.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.min.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.min.html
index e6d584040c94..74ddb8236992 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.min.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.min.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.min#

 

 

-LazyGroupBy.min() → LazyFrame[source]#

+LazyGroupBy.min() → LazyFrame[source]#
 
Reduce the groups to the minimal value.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.n_unique.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.n_unique.html
index dfc46f44852f..2cd47f043f6a 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.n_unique.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.n_unique.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.n_unique#

 

 

-LazyGroupBy.n_unique() → LazyFrame[source]#

+LazyGroupBy.n_unique() → LazyFrame[source]#
 
Count the unique values per group.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.quantile.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.quantile.html
index ec70838ce9f5..e97d0b91f3bb 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.quantile.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.quantile.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.quantile#

 

 

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

+LazyGroupBy.quantile(quantile: float, interpolation: RollingInterpolationMethod = 'nearest') → LazyFrame[source]#
 
Compute the quantile per group.

 

 
Parameters:

diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.sum.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.sum.html
index 4c3ad6ffec80..015a28603ee3 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.sum.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.sum.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.sum#

 

 

-LazyGroupBy.sum() → LazyFrame[source]#

+LazyGroupBy.sum() → LazyFrame[source]#
 
Reduce the groups to the sum.

 
Examples

 
>>> ldf = pl.DataFrame(
diff --git a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.tail.html b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.tail.html
index 73db58b6e082..004a9cde535d 100644
--- a/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.tail.html
+++ b/py-polars/dev/reference/lazyframe/api/polars.lazyframe.groupby.LazyGroupBy.tail.html
@@ -1626,7 +1626,7 @@
 
polars.lazyframe.groupby.LazyGroupBy.tail#

 

 

-LazyGroupBy.tail(n: int = 5) → LazyFrame[source]#

+LazyGroupBy.tail(n: int = 5) → LazyFrame[source]#
 
Get the last n rows of each group.

 

 
Parameters:

diff --git a/py-polars/dev/reference/lazyframe/index.html b/py-polars/dev/reference/lazyframe/index.html
index b30db5ab61fe..11f1e4f9d87b 100644
--- a/py-polars/dev/reference/lazyframe/index.html
+++ b/py-polars/dev/reference/lazyframe/index.html
@@ -1617,7 +1617,7 @@ 
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.

@@ -1973,7 +1973,7 @@ 
LazyFrame
 

-approx_unique() → Self[source]

+approx_unique() → Self[source]
 
Approx count unique values.

 
This is done using the HyperLogLog++ algorithm for cardinality estimation.

 
Examples

@@ -1998,7 +1998,7 @@ 
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.

 

@@ -2070,13 +2070,13 @@ 
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.

 

@@ -2128,7 +2128,7 @@ 
LazyFrame
 

-clone() → Self[source]

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

 

 
See also

@@ -2153,7 +2153,7 @@ 
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.

@@ -2211,7 +2211,7 @@ 
LazyFrame
 

-property columns: list[str][source]

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

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -2229,7 +2229,7 @@ 
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:

@@ -2295,7 +2295,7 @@ 
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.

 

@@ -2369,7 +2369,7 @@ 
LazyFrame
 

-property dtypes: list[PolarsDataType][source]

+property dtypes: list[PolarsDataType][source]
 
Get dtypes of columns in LazyFrame.

 

 
See also

@@ -2394,7 +2394,7 @@ 
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.

 

@@ -2441,7 +2441,7 @@ 
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:

@@ -2483,7 +2483,7 @@ 
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
@@ -2546,7 +2546,7 @@ 
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:

@@ -2586,7 +2586,7 @@ 
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:

@@ -2668,7 +2668,7 @@ 
LazyFrame
 

-filter(predicate: IntoExpr) → Self[source]

+filter(predicate: IntoExpr) → Self[source]
 
Filter the rows in the LazyFrame based on a predicate expression.

 

 
Parameters:

@@ -2730,7 +2730,7 @@ 
LazyFrame
 

-first() → Self[source]

+first() → Self[source]
 
Get the first row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -2754,7 +2754,7 @@ 
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:

@@ -2774,7 +2774,7 @@ 
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:

@@ -2866,7 +2866,7 @@ 
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
@@ -3181,7 +3181,7 @@ 
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
@@ -3313,7 +3313,7 @@ 
LazyFrame
 

-head(n: int = 5) → Self[source]

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

 

 
Parameters:

@@ -3363,7 +3363,7 @@ 
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.

@@ -3381,7 +3381,7 @@ 
LazyFrame
 

-interpolate() → Self[source]

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

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3409,7 +3409,7 @@ 
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:

@@ -3547,7 +3547,7 @@ 
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.

@@ -3669,7 +3669,7 @@ 
LazyFrame
 

-last() → Self[source]

+last() → Self[source]
 
Get the last row of the DataFrame.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3693,7 +3693,7 @@ 
LazyFrame
 

-lazy() → Self[source]

+lazy() → Self[source]
 
Return lazy representation, i.e. itself.

 
Useful for writing code that expects either a DataFrame or
 LazyFrame.

@@ -3720,7 +3720,7 @@ 
LazyFrame
 

-limit(n: int = 5) → Self[source]

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

 
Alias for LazyFrame.head().

 

@@ -3771,7 +3771,7 @@ 
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.

 

@@ -3834,7 +3834,7 @@ 
LazyFrame
 

-max() → Self[source]

+max() → Self[source]
 
Aggregate the columns in the LazyFrame to their maximum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3858,7 +3858,7 @@ 
LazyFrame
 

-mean() → Self[source]

+mean() → Self[source]
 
Aggregate the columns in the LazyFrame to their mean value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3882,7 +3882,7 @@ 
LazyFrame
 

-median() → Self[source]

+median() → Self[source]
 
Aggregate the columns in the LazyFrame to their median value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -3906,7 +3906,7 @@ 
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
@@ -3960,7 +3960,7 @@ 
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
@@ -4027,7 +4027,7 @@ 
LazyFrame
 

-min() → Self[source]

+min() → Self[source]
 
Aggregate the columns in the LazyFrame to their minimum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4051,7 +4051,7 @@ 
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(
@@ -4076,7 +4076,7 @@ 
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:

@@ -4147,7 +4147,7 @@ 
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
@@ -4221,7 +4221,7 @@ 
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:

@@ -4255,7 +4255,7 @@ 
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:

@@ -4275,7 +4275,7 @@ 
LazyFrame
 

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

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

 

 
Parameters:

@@ -4313,7 +4313,7 @@ 
LazyFrame
 

-reverse() → Self[source]

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

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4339,7 +4339,7 @@ 
LazyFrame
 

-property schema: SchemaDict[source]

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

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4357,7 +4357,7 @@ 
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:

@@ -4462,7 +4462,7 @@ 
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:

@@ -4480,7 +4480,7 @@ 
LazyFrame
 

-shift(periods: int) → Self[source]

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

 

 
Parameters:

@@ -4525,7 +4525,7 @@ 
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:

@@ -4572,7 +4572,7 @@ 
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:

@@ -4623,7 +4623,7 @@ 
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.

 

@@ -4666,7 +4666,7 @@ 
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.

 

@@ -4730,7 +4730,7 @@ 
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:

@@ -4767,7 +4767,7 @@ 
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:

@@ -4857,7 +4857,7 @@ 
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:

@@ -4900,7 +4900,7 @@ 
LazyFrame
 

-sum() → Self[source]

+sum() → Self[source]
 
Aggregate the columns in the LazyFrame to their sum value.

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -4924,7 +4924,7 @@ 
LazyFrame
 

-tail(n: int = 5) → Self[source]

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

 

 
Parameters:

@@ -4970,7 +4970,7 @@ 
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(
@@ -4995,7 +4995,7 @@ 
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.

 

@@ -5067,7 +5067,7 @@ 
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:

@@ -5151,7 +5151,7 @@ 
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.

@@ -5202,7 +5202,7 @@ 
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:

@@ -5279,7 +5279,7 @@ 
LazyFrame
 

-var(ddof: int = 1) → Self[source]

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

 

 
Parameters:

@@ -5322,7 +5322,7 @@ 
LazyFrame
 

-property width: int[source]

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

 
Examples

 
>>> lf = pl.LazyFrame(
@@ -5339,7 +5339,7 @@ 
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.

 

@@ -5484,7 +5484,7 @@ 
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.

@@ -5540,7 +5540,7 @@ 
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:

@@ -5581,7 +5581,7 @@ 
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/dev/reference/series/api/polars.Series.abs.html b/py-polars/dev/reference/series/api/polars.Series.abs.html
index 152b097499e2..a5afd5349bc4 100644
--- a/py-polars/dev/reference/series/api/polars.Series.abs.html
+++ b/py-polars/dev/reference/series/api/polars.Series.abs.html
@@ -1626,7 +1626,7 @@
 
polars.Series.abs#

 

 

-Series.abs() → Series[source]#

+Series.abs() → Series[source]#
 
Compute absolute values.

 
Same as abs(series).

 

diff --git a/py-polars/dev/reference/series/api/polars.Series.alias.html b/py-polars/dev/reference/series/api/polars.Series.alias.html
index 12034a27b7f4..4c3059cef5b7 100644
--- a/py-polars/dev/reference/series/api/polars.Series.alias.html
+++ b/py-polars/dev/reference/series/api/polars.Series.alias.html
@@ -1626,7 +1626,7 @@
 
polars.Series.alias#

 

 

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

+Series.alias(name: str) → Series[source]#
 
Rename the series.

 

 
Parameters:

diff --git a/py-polars/dev/reference/series/api/polars.Series.all.html b/py-polars/dev/reference/series/api/polars.Series.all.html
index 4c1f2c1881de..8c7916c7caf5 100644
--- a/py-polars/dev/reference/series/api/polars.Series.all.html
+++ b/py-polars/dev/reference/series/api/polars.Series.all.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.any.html b/py-polars/dev/reference/series/api/polars.Series.any.html
index bfb4c935877f..aa56cc0fc1f3 100644
--- a/py-polars/dev/reference/series/api/polars.Series.any.html
+++ b/py-polars/dev/reference/series/api/polars.Series.any.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.append.html b/py-polars/dev/reference/series/api/polars.Series.append.html
index 167ef083b58e..2c345edfcf3e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.append.html
+++ b/py-polars/dev/reference/series/api/polars.Series.append.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.apply.html b/py-polars/dev/reference/series/api/polars.Series.apply.html
index ec8aa6b925a4..03d46524899e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.apply.html
+++ b/py-polars/dev/reference/series/api/polars.Series.apply.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arccos.html b/py-polars/dev/reference/series/api/polars.Series.arccos.html
index 3978838874e8..755ecb89e98b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arccos.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arccos.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arccosh.html b/py-polars/dev/reference/series/api/polars.Series.arccosh.html
index 805f2fe448dc..65e110135802 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arccosh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arccosh.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arcsin.html b/py-polars/dev/reference/series/api/polars.Series.arcsin.html
index 27acd46a32d4..9998d999b293 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arcsin.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arcsin.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arcsinh.html b/py-polars/dev/reference/series/api/polars.Series.arcsinh.html
index 9991fd5dbcdb..2979e8768392 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arcsinh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arcsinh.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arctan.html b/py-polars/dev/reference/series/api/polars.Series.arctan.html
index bdbbb0f07898..cdd374e2540a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arctan.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arctan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arctanh.html b/py-polars/dev/reference/series/api/polars.Series.arctanh.html
index 6230e0011dc0..788a669ec0c8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arctanh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arctanh.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arg_max.html b/py-polars/dev/reference/series/api/polars.Series.arg_max.html
index 1cd02025b8c3..05cf752bcb38 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arg_max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arg_max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arg_min.html b/py-polars/dev/reference/series/api/polars.Series.arg_min.html
index 909d48576a0a..6d4f0c8ff978 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arg_min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arg_min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arg_sort.html b/py-polars/dev/reference/series/api/polars.Series.arg_sort.html
index 178811a207c8..9aec3c8c5b44 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arg_sort.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arg_sort.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arg_true.html b/py-polars/dev/reference/series/api/polars.Series.arg_true.html
index 4d670c69ac52..8538270832fd 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arg_true.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arg_true.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.arg_unique.html b/py-polars/dev/reference/series/api/polars.Series.arg_unique.html
index f78160b3968b..b958929b4480 100644
--- a/py-polars/dev/reference/series/api/polars.Series.arg_unique.html
+++ b/py-polars/dev/reference/series/api/polars.Series.arg_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.bottom_k.html b/py-polars/dev/reference/series/api/polars.Series.bottom_k.html
index 270f5f45e720..a4e826604fb7 100644
--- a/py-polars/dev/reference/series/api/polars.Series.bottom_k.html
+++ b/py-polars/dev/reference/series/api/polars.Series.bottom_k.html
@@ -1627,7 +1627,7 @@
 
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/dev/reference/series/api/polars.Series.cast.html b/py-polars/dev/reference/series/api/polars.Series.cast.html
index dc50e753461d..cb671f109f3d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cast.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cast.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cbrt.html b/py-polars/dev/reference/series/api/polars.Series.cbrt.html
index 67652d92d4c8..82e648d930cf 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cbrt.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cbrt.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.ceil.html b/py-polars/dev/reference/series/api/polars.Series.ceil.html
index 9b864161cd29..4276577f353b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.ceil.html
+++ b/py-polars/dev/reference/series/api/polars.Series.ceil.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.chunk_lengths.html b/py-polars/dev/reference/series/api/polars.Series.chunk_lengths.html
index fe17ebcf7651..df78e8535a3a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.chunk_lengths.html
+++ b/py-polars/dev/reference/series/api/polars.Series.chunk_lengths.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.clear.html b/py-polars/dev/reference/series/api/polars.Series.clear.html
index c12071a9f3d1..c73be60c69f2 100644
--- a/py-polars/dev/reference/series/api/polars.Series.clear.html
+++ b/py-polars/dev/reference/series/api/polars.Series.clear.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.clip.html b/py-polars/dev/reference/series/api/polars.Series.clip.html
index 0888ae44fafa..6c5e3d06fde6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.clip.html
+++ b/py-polars/dev/reference/series/api/polars.Series.clip.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.clip_max.html b/py-polars/dev/reference/series/api/polars.Series.clip_max.html
index f19347b42c2d..e181500af9be 100644
--- a/py-polars/dev/reference/series/api/polars.Series.clip_max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.clip_max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.clip_min.html b/py-polars/dev/reference/series/api/polars.Series.clip_min.html
index 2e1cbd3a275c..4fdea8932e49 100644
--- a/py-polars/dev/reference/series/api/polars.Series.clip_min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.clip_min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.clone.html b/py-polars/dev/reference/series/api/polars.Series.clone.html
index d3c4371f4c6a..9bf50ba85a81 100644
--- a/py-polars/dev/reference/series/api/polars.Series.clone.html
+++ b/py-polars/dev/reference/series/api/polars.Series.clone.html
@@ -1626,7 +1626,7 @@
 
polars.Series.clone#

 

 

-Series.clone() → Self[source]#

+Series.clone() → Self[source]#
 
Very cheap deepcopy/clone.

 

 
See also

diff --git a/py-polars/dev/reference/series/api/polars.Series.cos.html b/py-polars/dev/reference/series/api/polars.Series.cos.html
index 68ba6de2d499..9c7252a98c8c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cos.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cos.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cosh.html b/py-polars/dev/reference/series/api/polars.Series.cosh.html
index 70cd74ed4942..7a8b522c70b0 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cosh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cosh.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cummax.html b/py-polars/dev/reference/series/api/polars.Series.cummax.html
index 1ae976217ed8..c4851c4023e1 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cummax.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cummax.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cummin.html b/py-polars/dev/reference/series/api/polars.Series.cummin.html
index 398d156bea23..964add8f435c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cummin.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cummin.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cumprod.html b/py-polars/dev/reference/series/api/polars.Series.cumprod.html
index 2d58cdc19014..337d96c45fc9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cumprod.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cumprod.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cumsum.html b/py-polars/dev/reference/series/api/polars.Series.cumsum.html
index 0d3d5a3e02a0..adfb4d10549a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cumsum.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cumsum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cumulative_eval.html b/py-polars/dev/reference/series/api/polars.Series.cumulative_eval.html
index a64ee5c8f51d..f6452431f1f9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cumulative_eval.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cumulative_eval.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.cut.html b/py-polars/dev/reference/series/api/polars.Series.cut.html
index d00596d3205f..b2d7b8573b2a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.cut.html
+++ b/py-polars/dev/reference/series/api/polars.Series.cut.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.describe.html b/py-polars/dev/reference/series/api/polars.Series.describe.html
index 036585f04cea..4164d5ae18bc 100644
--- a/py-polars/dev/reference/series/api/polars.Series.describe.html
+++ b/py-polars/dev/reference/series/api/polars.Series.describe.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.diff.html b/py-polars/dev/reference/series/api/polars.Series.diff.html
index efdf255189dd..7e67dc6625a2 100644
--- a/py-polars/dev/reference/series/api/polars.Series.diff.html
+++ b/py-polars/dev/reference/series/api/polars.Series.diff.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.dot.html b/py-polars/dev/reference/series/api/polars.Series.dot.html
index efafc260c0ee..9e28a7269068 100644
--- a/py-polars/dev/reference/series/api/polars.Series.dot.html
+++ b/py-polars/dev/reference/series/api/polars.Series.dot.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.drop_nans.html b/py-polars/dev/reference/series/api/polars.Series.drop_nans.html
index 2304e8ccada3..338618420141 100644
--- a/py-polars/dev/reference/series/api/polars.Series.drop_nans.html
+++ b/py-polars/dev/reference/series/api/polars.Series.drop_nans.html
@@ -1626,7 +1626,7 @@
 
polars.Series.drop_nans#

 

 

-Series.drop_nans() → Series[source]#

+Series.drop_nans() → Series[source]#
 
Drop NaN values.

 

 
diff --git a/py-polars/dev/reference/series/api/polars.Series.drop_nulls.html b/py-polars/dev/reference/series/api/polars.Series.drop_nulls.html
index 446aa55cde81..1d61ca68207a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.drop_nulls.html
+++ b/py-polars/dev/reference/series/api/polars.Series.drop_nulls.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.entropy.html b/py-polars/dev/reference/series/api/polars.Series.entropy.html
index 78d728b3a51d..c66ce047a13d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.entropy.html
+++ b/py-polars/dev/reference/series/api/polars.Series.entropy.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.estimated_size.html b/py-polars/dev/reference/series/api/polars.Series.estimated_size.html
index 7f6f9d4dcaa8..059183738634 100644
--- a/py-polars/dev/reference/series/api/polars.Series.estimated_size.html
+++ b/py-polars/dev/reference/series/api/polars.Series.estimated_size.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.ewm_mean.html b/py-polars/dev/reference/series/api/polars.Series.ewm_mean.html
index 15e1043e171e..d76d352f2bee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.ewm_mean.html
+++ b/py-polars/dev/reference/series/api/polars.Series.ewm_mean.html
@@ -1627,7 +1627,7 @@
 
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/dev/reference/series/api/polars.Series.ewm_std.html b/py-polars/dev/reference/series/api/polars.Series.ewm_std.html
index e3c0b0534474..60dfa768f787 100644
--- a/py-polars/dev/reference/series/api/polars.Series.ewm_std.html
+++ b/py-polars/dev/reference/series/api/polars.Series.ewm_std.html
@@ -1627,7 +1627,7 @@
 
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/dev/reference/series/api/polars.Series.ewm_var.html b/py-polars/dev/reference/series/api/polars.Series.ewm_var.html
index 0178425a730f..040725be12aa 100644
--- a/py-polars/dev/reference/series/api/polars.Series.ewm_var.html
+++ b/py-polars/dev/reference/series/api/polars.Series.ewm_var.html
@@ -1627,7 +1627,7 @@
 
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/dev/reference/series/api/polars.Series.exp.html b/py-polars/dev/reference/series/api/polars.Series.exp.html
index 0eef1083b798..c6ef3b15e1ee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.exp.html
+++ b/py-polars/dev/reference/series/api/polars.Series.exp.html
@@ -1626,7 +1626,7 @@
 
polars.Series.exp#

 

 

-Series.exp() → Series[source]#

+Series.exp() → Series[source]#
 
Compute the exponential, element-wise.

 

 
diff --git a/py-polars/dev/reference/series/api/polars.Series.explode.html b/py-polars/dev/reference/series/api/polars.Series.explode.html
index 6f5b74507bbd..dbd45d54d891 100644
--- a/py-polars/dev/reference/series/api/polars.Series.explode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.explode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.extend.html b/py-polars/dev/reference/series/api/polars.Series.extend.html
index ab32741c3838..7c795d2522ee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.extend.html
+++ b/py-polars/dev/reference/series/api/polars.Series.extend.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.extend_constant.html b/py-polars/dev/reference/series/api/polars.Series.extend_constant.html
index 10778769ea7d..9692af60f191 100644
--- a/py-polars/dev/reference/series/api/polars.Series.extend_constant.html
+++ b/py-polars/dev/reference/series/api/polars.Series.extend_constant.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.fill_nan.html b/py-polars/dev/reference/series/api/polars.Series.fill_nan.html
index 34bbeb2a0737..ad3f1b708990 100644
--- a/py-polars/dev/reference/series/api/polars.Series.fill_nan.html
+++ b/py-polars/dev/reference/series/api/polars.Series.fill_nan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.fill_null.html b/py-polars/dev/reference/series/api/polars.Series.fill_null.html
index 6d138f99be0e..0d0868023710 100644
--- a/py-polars/dev/reference/series/api/polars.Series.fill_null.html
+++ b/py-polars/dev/reference/series/api/polars.Series.fill_null.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.filter.html b/py-polars/dev/reference/series/api/polars.Series.filter.html
index 8ac36f180c4b..b0d616edcd7f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.filter.html
+++ b/py-polars/dev/reference/series/api/polars.Series.filter.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.floor.html b/py-polars/dev/reference/series/api/polars.Series.floor.html
index dce52c31c0ff..b49cba6696ee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.floor.html
+++ b/py-polars/dev/reference/series/api/polars.Series.floor.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.get_chunks.html b/py-polars/dev/reference/series/api/polars.Series.get_chunks.html
index 088559467b54..0f3f8fc44aae 100644
--- a/py-polars/dev/reference/series/api/polars.Series.get_chunks.html
+++ b/py-polars/dev/reference/series/api/polars.Series.get_chunks.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.has_validity.html b/py-polars/dev/reference/series/api/polars.Series.has_validity.html
index 3b6add5b767f..35701193afa8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.has_validity.html
+++ b/py-polars/dev/reference/series/api/polars.Series.has_validity.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.hash.html b/py-polars/dev/reference/series/api/polars.Series.hash.html
index 72590fb2cb04..a65fac63e9d0 100644
--- a/py-polars/dev/reference/series/api/polars.Series.hash.html
+++ b/py-polars/dev/reference/series/api/polars.Series.hash.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.head.html b/py-polars/dev/reference/series/api/polars.Series.head.html
index 29e54d12c16e..fc95153739fc 100644
--- a/py-polars/dev/reference/series/api/polars.Series.head.html
+++ b/py-polars/dev/reference/series/api/polars.Series.head.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.hist.html b/py-polars/dev/reference/series/api/polars.Series.hist.html
index 27cfa1c23d95..9b3b551c594e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.hist.html
+++ b/py-polars/dev/reference/series/api/polars.Series.hist.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.implode.html b/py-polars/dev/reference/series/api/polars.Series.implode.html
index 38a1f8f04281..c526db240ec8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.implode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.implode.html
@@ -1626,7 +1626,7 @@
 
polars.Series.implode#

 

 

-Series.implode() → Self[source]#

+Series.implode() → Self[source]#
 
Aggregate values into a list.

 

 
diff --git a/py-polars/dev/reference/series/api/polars.Series.interpolate.html b/py-polars/dev/reference/series/api/polars.Series.interpolate.html
index 983c088d9f1f..36e8b1554d33 100644
--- a/py-polars/dev/reference/series/api/polars.Series.interpolate.html
+++ b/py-polars/dev/reference/series/api/polars.Series.interpolate.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_between.html b/py-polars/dev/reference/series/api/polars.Series.is_between.html
index 204fa24b45dd..a57857296be1 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_between.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_between.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_boolean.html b/py-polars/dev/reference/series/api/polars.Series.is_boolean.html
index e1ecfcbc7461..e6c955a3b618 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_boolean.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_boolean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_duplicated.html b/py-polars/dev/reference/series/api/polars.Series.is_duplicated.html
index 0f3ef69614ca..cdf0a379e3ef 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_duplicated.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_duplicated.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_empty.html b/py-polars/dev/reference/series/api/polars.Series.is_empty.html
index afaf232d734a..e5471394bff8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_empty.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_empty.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_finite.html b/py-polars/dev/reference/series/api/polars.Series.is_finite.html
index 1657972b1f3c..55a4578158ee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_finite.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_finite.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_first.html b/py-polars/dev/reference/series/api/polars.Series.is_first.html
index 759494ab69c1..5a552fc4a47a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_first.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_first.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_float.html b/py-polars/dev/reference/series/api/polars.Series.is_float.html
index 6960feb2d5a8..4f74ac71a5f1 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_float.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_float.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_in.html b/py-polars/dev/reference/series/api/polars.Series.is_in.html
index c5ae065968f5..a443ba19482d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_in.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_in.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_infinite.html b/py-polars/dev/reference/series/api/polars.Series.is_infinite.html
index fac1f75c8220..cf9e8412bce6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_infinite.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_infinite.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_integer.html b/py-polars/dev/reference/series/api/polars.Series.is_integer.html
index ff9fdfa5af1f..95999ff1a8c6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_integer.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_integer.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_nan.html b/py-polars/dev/reference/series/api/polars.Series.is_nan.html
index f988d8233836..742c390f68f9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_nan.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_nan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_not_nan.html b/py-polars/dev/reference/series/api/polars.Series.is_not_nan.html
index b684d7da155a..4a665c037b02 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_not_nan.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_not_nan.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_not_null.html b/py-polars/dev/reference/series/api/polars.Series.is_not_null.html
index 857d2cba6e62..e0bf94d71231 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_not_null.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_not_null.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_null.html b/py-polars/dev/reference/series/api/polars.Series.is_null.html
index eefb0ee555bf..06dcb85953e9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_null.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_null.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_numeric.html b/py-polars/dev/reference/series/api/polars.Series.is_numeric.html
index 346e582e3035..65e554d49523 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_numeric.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_numeric.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_sorted.html b/py-polars/dev/reference/series/api/polars.Series.is_sorted.html
index 4162050509bf..bc8d634e6aaa 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_sorted.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_temporal.html b/py-polars/dev/reference/series/api/polars.Series.is_temporal.html
index 9ba484a14860..b518a0846150 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_temporal.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_temporal.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_unique.html b/py-polars/dev/reference/series/api/polars.Series.is_unique.html
index 63f8d227516c..fd4957861121 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_unique.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.is_utf8.html b/py-polars/dev/reference/series/api/polars.Series.is_utf8.html
index 8137b8758021..cf323bb21166 100644
--- a/py-polars/dev/reference/series/api/polars.Series.is_utf8.html
+++ b/py-polars/dev/reference/series/api/polars.Series.is_utf8.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.item.html b/py-polars/dev/reference/series/api/polars.Series.item.html
index 05d0a9e175ef..e2753a34078a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.item.html
+++ b/py-polars/dev/reference/series/api/polars.Series.item.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.kurtosis.html b/py-polars/dev/reference/series/api/polars.Series.kurtosis.html
index a285f7168340..bc0e23581065 100644
--- a/py-polars/dev/reference/series/api/polars.Series.kurtosis.html
+++ b/py-polars/dev/reference/series/api/polars.Series.kurtosis.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.len.html b/py-polars/dev/reference/series/api/polars.Series.len.html
index 921068f95ce0..8fbefd2b587b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.len.html
+++ b/py-polars/dev/reference/series/api/polars.Series.len.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.limit.html b/py-polars/dev/reference/series/api/polars.Series.limit.html
index 6612cc95f3bc..f9ce548ab078 100644
--- a/py-polars/dev/reference/series/api/polars.Series.limit.html
+++ b/py-polars/dev/reference/series/api/polars.Series.limit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.log.html b/py-polars/dev/reference/series/api/polars.Series.log.html
index 7e8c0220d78b..56d6161cae6b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.log.html
+++ b/py-polars/dev/reference/series/api/polars.Series.log.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.log10.html b/py-polars/dev/reference/series/api/polars.Series.log10.html
index 97a0d970cf3e..7eab703d6f41 100644
--- a/py-polars/dev/reference/series/api/polars.Series.log10.html
+++ b/py-polars/dev/reference/series/api/polars.Series.log10.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.log1p.html b/py-polars/dev/reference/series/api/polars.Series.log1p.html
index acb57cf5b160..bdf4697e47a3 100644
--- a/py-polars/dev/reference/series/api/polars.Series.log1p.html
+++ b/py-polars/dev/reference/series/api/polars.Series.log1p.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.lower_bound.html b/py-polars/dev/reference/series/api/polars.Series.lower_bound.html
index af4c5c2b0153..792b47b8b842 100644
--- a/py-polars/dev/reference/series/api/polars.Series.lower_bound.html
+++ b/py-polars/dev/reference/series/api/polars.Series.lower_bound.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.map_dict.html b/py-polars/dev/reference/series/api/polars.Series.map_dict.html
index 6b5328fa90eb..b5d4bcc4af05 100644
--- a/py-polars/dev/reference/series/api/polars.Series.map_dict.html
+++ b/py-polars/dev/reference/series/api/polars.Series.map_dict.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.max.html b/py-polars/dev/reference/series/api/polars.Series.max.html
index 5017c83ab634..7353bf633b44 100644
--- a/py-polars/dev/reference/series/api/polars.Series.max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.mean.html b/py-polars/dev/reference/series/api/polars.Series.mean.html
index 3ae950ec575d..4ef517b6aac0 100644
--- a/py-polars/dev/reference/series/api/polars.Series.mean.html
+++ b/py-polars/dev/reference/series/api/polars.Series.mean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.median.html b/py-polars/dev/reference/series/api/polars.Series.median.html
index 4042ce6c1204..3a46dc5b9b4a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.median.html
+++ b/py-polars/dev/reference/series/api/polars.Series.median.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.min.html b/py-polars/dev/reference/series/api/polars.Series.min.html
index 978a3b65e752..a27a1b55ceee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.mode.html b/py-polars/dev/reference/series/api/polars.Series.mode.html
index e379409d20ee..ce0aa100f818 100644
--- a/py-polars/dev/reference/series/api/polars.Series.mode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.mode.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.n_chunks.html b/py-polars/dev/reference/series/api/polars.Series.n_chunks.html
index 49a4aa2381c0..874aa0db5b16 100644
--- a/py-polars/dev/reference/series/api/polars.Series.n_chunks.html
+++ b/py-polars/dev/reference/series/api/polars.Series.n_chunks.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.n_unique.html b/py-polars/dev/reference/series/api/polars.Series.n_unique.html
index 34aad8e4b9fa..fec8dcd64205 100644
--- a/py-polars/dev/reference/series/api/polars.Series.n_unique.html
+++ b/py-polars/dev/reference/series/api/polars.Series.n_unique.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.nan_max.html b/py-polars/dev/reference/series/api/polars.Series.nan_max.html
index fca32bcaa5c5..2db8b1572497 100644
--- a/py-polars/dev/reference/series/api/polars.Series.nan_max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.nan_max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.nan_min.html b/py-polars/dev/reference/series/api/polars.Series.nan_min.html
index bf6c8e9be2e1..02461ab8de64 100644
--- a/py-polars/dev/reference/series/api/polars.Series.nan_min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.nan_min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.new_from_index.html b/py-polars/dev/reference/series/api/polars.Series.new_from_index.html
index 275c21f2e110..211a74d60e49 100644
--- a/py-polars/dev/reference/series/api/polars.Series.new_from_index.html
+++ b/py-polars/dev/reference/series/api/polars.Series.new_from_index.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.null_count.html b/py-polars/dev/reference/series/api/polars.Series.null_count.html
index ac4555f23158..9fc2729a72c8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.null_count.html
+++ b/py-polars/dev/reference/series/api/polars.Series.null_count.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.pct_change.html b/py-polars/dev/reference/series/api/polars.Series.pct_change.html
index 55468005aff7..c12e872c7aee 100644
--- a/py-polars/dev/reference/series/api/polars.Series.pct_change.html
+++ b/py-polars/dev/reference/series/api/polars.Series.pct_change.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.peak_max.html b/py-polars/dev/reference/series/api/polars.Series.peak_max.html
index 23a0a9f4666a..6c4e4c40025f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.peak_max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.peak_max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.peak_min.html b/py-polars/dev/reference/series/api/polars.Series.peak_min.html
index b21c578f4003..482b4b007d76 100644
--- a/py-polars/dev/reference/series/api/polars.Series.peak_min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.peak_min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.product.html b/py-polars/dev/reference/series/api/polars.Series.product.html
index 8c338a206ed7..b65fab53122a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.product.html
+++ b/py-polars/dev/reference/series/api/polars.Series.product.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.qcut.html b/py-polars/dev/reference/series/api/polars.Series.qcut.html
index ee7000f43052..5fe0cd70f4cc 100644
--- a/py-polars/dev/reference/series/api/polars.Series.qcut.html
+++ b/py-polars/dev/reference/series/api/polars.Series.qcut.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.quantile.html b/py-polars/dev/reference/series/api/polars.Series.quantile.html
index 52a568174de7..2e0ff2abf23c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.quantile.html
+++ b/py-polars/dev/reference/series/api/polars.Series.quantile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rank.html b/py-polars/dev/reference/series/api/polars.Series.rank.html
index 45ebc6619652..07ab6e238ede 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rank.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rank.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rechunk.html b/py-polars/dev/reference/series/api/polars.Series.rechunk.html
index aba9eae6f39e..654dd0ce5d60 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rechunk.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rechunk.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.reinterpret.html b/py-polars/dev/reference/series/api/polars.Series.reinterpret.html
index 411b2d4ab677..7d2fbb6e46f9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.reinterpret.html
+++ b/py-polars/dev/reference/series/api/polars.Series.reinterpret.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rename.html b/py-polars/dev/reference/series/api/polars.Series.rename.html
index 39ceb4f77338..d59ae84487a6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rename.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rename.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.reshape.html b/py-polars/dev/reference/series/api/polars.Series.reshape.html
index f193e89b3e47..24a85bdebd68 100644
--- a/py-polars/dev/reference/series/api/polars.Series.reshape.html
+++ b/py-polars/dev/reference/series/api/polars.Series.reshape.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.reverse.html b/py-polars/dev/reference/series/api/polars.Series.reverse.html
index dff1996d5462..8589aaba9d7e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.reverse.html
+++ b/py-polars/dev/reference/series/api/polars.Series.reverse.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rle.html b/py-polars/dev/reference/series/api/polars.Series.rle.html
index 5094b005cdcf..4d0be3161c51 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rle.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rle.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rle_id.html b/py-polars/dev/reference/series/api/polars.Series.rle_id.html
index 7bd59114811e..6562ac5a6cdd 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rle_id.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rle_id.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_apply.html b/py-polars/dev/reference/series/api/polars.Series.rolling_apply.html
index 5dd9381ee30f..7080f7576086 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_apply.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_apply.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_max.html b/py-polars/dev/reference/series/api/polars.Series.rolling_max.html
index 63778217910b..f751dd6d3129 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_max.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_max.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_mean.html b/py-polars/dev/reference/series/api/polars.Series.rolling_mean.html
index 467ea2889e88..8cd30f45fb9c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_mean.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_mean.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_median.html b/py-polars/dev/reference/series/api/polars.Series.rolling_median.html
index 0b1eb0783771..b2f9657ffdce 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_median.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_median.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_min.html b/py-polars/dev/reference/series/api/polars.Series.rolling_min.html
index 06c5bbfb41d8..5c0092fbc443 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_min.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_min.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_quantile.html b/py-polars/dev/reference/series/api/polars.Series.rolling_quantile.html
index 04b400651a84..972e29637123 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_quantile.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_quantile.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_skew.html b/py-polars/dev/reference/series/api/polars.Series.rolling_skew.html
index 0e6e62b87dfe..498d98a410f5 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_skew.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_skew.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_std.html b/py-polars/dev/reference/series/api/polars.Series.rolling_std.html
index f2376df1abd4..f9c5ff546708 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_std.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_std.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_sum.html b/py-polars/dev/reference/series/api/polars.Series.rolling_sum.html
index fc98a537089f..d2b85e16ba29 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_sum.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_sum.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.rolling_var.html b/py-polars/dev/reference/series/api/polars.Series.rolling_var.html
index 7cbe258a78b6..549fba1742c8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.rolling_var.html
+++ b/py-polars/dev/reference/series/api/polars.Series.rolling_var.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.round.html b/py-polars/dev/reference/series/api/polars.Series.round.html
index 2467c6042d4d..cbd886c749d6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.round.html
+++ b/py-polars/dev/reference/series/api/polars.Series.round.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.sample.html b/py-polars/dev/reference/series/api/polars.Series.sample.html
index 4153a9efbefd..5f13c5b14554 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sample.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sample.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.search_sorted.html b/py-polars/dev/reference/series/api/polars.Series.search_sorted.html
index 81afdc7ae10b..dab035287d63 100644
--- a/py-polars/dev/reference/series/api/polars.Series.search_sorted.html
+++ b/py-polars/dev/reference/series/api/polars.Series.search_sorted.html
@@ -1627,7 +1627,7 @@
 
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/dev/reference/series/api/polars.Series.series_equal.html b/py-polars/dev/reference/series/api/polars.Series.series_equal.html
index b9156a076a0c..f28a1038b1b6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.series_equal.html
+++ b/py-polars/dev/reference/series/api/polars.Series.series_equal.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.set.html b/py-polars/dev/reference/series/api/polars.Series.set.html
index a3005fa50581..5b246f4982f4 100644
--- a/py-polars/dev/reference/series/api/polars.Series.set.html
+++ b/py-polars/dev/reference/series/api/polars.Series.set.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.set_at_idx.html b/py-polars/dev/reference/series/api/polars.Series.set_at_idx.html
index 1e6cad32b8cf..9be69629c48b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.set_at_idx.html
+++ b/py-polars/dev/reference/series/api/polars.Series.set_at_idx.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.set_sorted.html b/py-polars/dev/reference/series/api/polars.Series.set_sorted.html
index f5abe8b73457..25f528c0c524 100644
--- a/py-polars/dev/reference/series/api/polars.Series.set_sorted.html
+++ b/py-polars/dev/reference/series/api/polars.Series.set_sorted.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.shift.html b/py-polars/dev/reference/series/api/polars.Series.shift.html
index 8ddeb2647564..72d910b21a83 100644
--- a/py-polars/dev/reference/series/api/polars.Series.shift.html
+++ b/py-polars/dev/reference/series/api/polars.Series.shift.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.shift_and_fill.html b/py-polars/dev/reference/series/api/polars.Series.shift_and_fill.html
index fc7adeeabf1b..7f8bdd435a79 100644
--- a/py-polars/dev/reference/series/api/polars.Series.shift_and_fill.html
+++ b/py-polars/dev/reference/series/api/polars.Series.shift_and_fill.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.shrink_dtype.html b/py-polars/dev/reference/series/api/polars.Series.shrink_dtype.html
index 5bb41502814c..af2c3266a790 100644
--- a/py-polars/dev/reference/series/api/polars.Series.shrink_dtype.html
+++ b/py-polars/dev/reference/series/api/polars.Series.shrink_dtype.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.shrink_to_fit.html b/py-polars/dev/reference/series/api/polars.Series.shrink_to_fit.html
index 586a60d0c5d9..e8a36c3bca28 100644
--- a/py-polars/dev/reference/series/api/polars.Series.shrink_to_fit.html
+++ b/py-polars/dev/reference/series/api/polars.Series.shrink_to_fit.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.shuffle.html b/py-polars/dev/reference/series/api/polars.Series.shuffle.html
index 96bc3cf7b122..4f77cbb2efa6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.shuffle.html
+++ b/py-polars/dev/reference/series/api/polars.Series.shuffle.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.sign.html b/py-polars/dev/reference/series/api/polars.Series.sign.html
index b9f1ec5acc7b..e7451a53e4f5 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sign.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sign.html
@@ -1626,7 +1626,7 @@
 
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/dev/reference/series/api/polars.Series.sin.html b/py-polars/dev/reference/series/api/polars.Series.sin.html
index 02f433e8316c..b69128652bf4 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sin.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sin.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.sinh.html b/py-polars/dev/reference/series/api/polars.Series.sinh.html
index f75ec5af26b3..c88405560b79 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sinh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sinh.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.skew.html b/py-polars/dev/reference/series/api/polars.Series.skew.html
index c8ff5f259a5f..d484aec4f57e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.skew.html
+++ b/py-polars/dev/reference/series/api/polars.Series.skew.html
@@ -1627,7 +1627,7 @@
 
    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/dev/reference/series/api/polars.Series.slice.html b/py-polars/dev/reference/series/api/polars.Series.slice.html
index e5f176f67616..2bf8de1cebc4 100644
--- a/py-polars/dev/reference/series/api/polars.Series.slice.html
+++ b/py-polars/dev/reference/series/api/polars.Series.slice.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.sort.html b/py-polars/dev/reference/series/api/polars.Series.sort.html
index d944d1e5dfef..1aeb57914624 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sort.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sort.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.sqrt.html b/py-polars/dev/reference/series/api/polars.Series.sqrt.html
index 11326b610f74..53b371cf34be 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sqrt.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sqrt.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.std.html b/py-polars/dev/reference/series/api/polars.Series.std.html
index e23ce55fab45..89abc67da93c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.std.html
+++ b/py-polars/dev/reference/series/api/polars.Series.std.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.concat.html b/py-polars/dev/reference/series/api/polars.Series.str.concat.html
index 6649eebbd4c7..1dbee9e4bda2 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.concat.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.concat.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.contains.html b/py-polars/dev/reference/series/api/polars.Series.str.contains.html
index 9e29621d447d..815583cd7b49 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.contains.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.contains.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.count_match.html b/py-polars/dev/reference/series/api/polars.Series.str.count_match.html
index e45895870508..58cadba6feab 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.count_match.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.count_match.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.decode.html b/py-polars/dev/reference/series/api/polars.Series.str.decode.html
index 8b4b1238f464..23a85e846d1f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.decode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.decode.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.encode.html b/py-polars/dev/reference/series/api/polars.Series.str.encode.html
index aed939388411..5286197fd3ff 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.encode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.encode.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.ends_with.html b/py-polars/dev/reference/series/api/polars.Series.str.ends_with.html
index 7ec007e603c0..077ecc0898a8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.ends_with.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.ends_with.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.explode.html b/py-polars/dev/reference/series/api/polars.Series.str.explode.html
index e34b4d9d6345..d03ac966a41d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.explode.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.explode.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.extract.html b/py-polars/dev/reference/series/api/polars.Series.str.extract.html
index 12b58883ff65..b40eed12ed33 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.extract.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.extract.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.extract_all.html b/py-polars/dev/reference/series/api/polars.Series.str.extract_all.html
index 70de68e3eceb..83d772b23be0 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.extract_all.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.extract_all.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.json_extract.html b/py-polars/dev/reference/series/api/polars.Series.str.json_extract.html
index d85ba16d9ae4..be95d79c9a69 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.json_extract.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.json_extract.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.json_path_match.html b/py-polars/dev/reference/series/api/polars.Series.str.json_path_match.html
index 64311bf4bf87..72e7808c7d7a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.json_path_match.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.json_path_match.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.lengths.html b/py-polars/dev/reference/series/api/polars.Series.str.lengths.html
index 0cc40661dcb3..939ca21a426f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.lengths.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.lengths.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.ljust.html b/py-polars/dev/reference/series/api/polars.Series.str.ljust.html
index 26064ec87553..5e7604323c51 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.ljust.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.ljust.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.lstrip.html b/py-polars/dev/reference/series/api/polars.Series.str.lstrip.html
index 0e111b216ede..7e2e32638ebf 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.lstrip.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.lstrip.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.n_chars.html b/py-polars/dev/reference/series/api/polars.Series.str.n_chars.html
index 342b7d073701..211f8c7f7260 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.n_chars.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.n_chars.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.parse_int.html b/py-polars/dev/reference/series/api/polars.Series.str.parse_int.html
index 469cc8f65d6b..c4bf5a76a3f8 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.parse_int.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.parse_int.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.replace.html b/py-polars/dev/reference/series/api/polars.Series.str.replace.html
index 241f2baa244e..cd40fffc3b08 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.replace.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.replace.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.replace_all.html b/py-polars/dev/reference/series/api/polars.Series.str.replace_all.html
index ff7922e25543..d2d35d13f75f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.replace_all.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.replace_all.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.rjust.html b/py-polars/dev/reference/series/api/polars.Series.str.rjust.html
index 1a9e1dc8fa6f..1ba401edbc9f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.rjust.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.rjust.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.rstrip.html b/py-polars/dev/reference/series/api/polars.Series.str.rstrip.html
index bba45c09fb79..917821c10aa3 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.rstrip.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.rstrip.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.slice.html b/py-polars/dev/reference/series/api/polars.Series.str.slice.html
index 03a2d676819a..2203f06384c9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.slice.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.slice.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.split.html b/py-polars/dev/reference/series/api/polars.Series.str.split.html
index de8942be85b9..44d14f6ac4b9 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.split.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.split.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.split_exact.html b/py-polars/dev/reference/series/api/polars.Series.str.split_exact.html
index 6e307d31a2a4..ce525b78bfdf 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.split_exact.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.split_exact.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.splitn.html b/py-polars/dev/reference/series/api/polars.Series.str.splitn.html
index 99d1b3eb6230..a2789367724b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.splitn.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.splitn.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.starts_with.html b/py-polars/dev/reference/series/api/polars.Series.str.starts_with.html
index 2a0144e77ccf..37111211af99 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.starts_with.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.starts_with.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.strip.html b/py-polars/dev/reference/series/api/polars.Series.str.strip.html
index 6109e2e8a592..8b1910981544 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.strip.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.strip.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.strptime.html b/py-polars/dev/reference/series/api/polars.Series.str.strptime.html
index 8d3796cd757f..3cbcd1a74c1b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.strptime.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.strptime.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_date.html b/py-polars/dev/reference/series/api/polars.Series.str.to_date.html
index 9275c0039e49..6f83e6b71649 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_date.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_date.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_datetime.html b/py-polars/dev/reference/series/api/polars.Series.str.to_datetime.html
index 22b67f32ee9b..5686a680fba3 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_datetime.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_datetime.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_decimal.html b/py-polars/dev/reference/series/api/polars.Series.str.to_decimal.html
index 7efb6f9387e9..3e43d3a28865 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_decimal.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_decimal.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_lowercase.html b/py-polars/dev/reference/series/api/polars.Series.str.to_lowercase.html
index b300de22494e..e3d1e3fb6d59 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_lowercase.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_lowercase.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_time.html b/py-polars/dev/reference/series/api/polars.Series.str.to_time.html
index 366f7df82dda..e48db742131c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_time.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_time.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_titlecase.html b/py-polars/dev/reference/series/api/polars.Series.str.to_titlecase.html
index 334bd70e5218..fca16ab41190 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_titlecase.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_titlecase.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.to_uppercase.html b/py-polars/dev/reference/series/api/polars.Series.str.to_uppercase.html
index 36c124f2a5f1..5391a2a32fed 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.to_uppercase.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.to_uppercase.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.str.zfill.html b/py-polars/dev/reference/series/api/polars.Series.str.zfill.html
index cddb497cb36a..8fd5121d2afb 100644
--- a/py-polars/dev/reference/series/api/polars.Series.str.zfill.html
+++ b/py-polars/dev/reference/series/api/polars.Series.str.zfill.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.sum.html b/py-polars/dev/reference/series/api/polars.Series.sum.html
index 011d1738dee1..d7d90db7f8bb 100644
--- a/py-polars/dev/reference/series/api/polars.Series.sum.html
+++ b/py-polars/dev/reference/series/api/polars.Series.sum.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.tail.html b/py-polars/dev/reference/series/api/polars.Series.tail.html
index e47bf52a9dc2..8033bc57d95c 100644
--- a/py-polars/dev/reference/series/api/polars.Series.tail.html
+++ b/py-polars/dev/reference/series/api/polars.Series.tail.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.take.html b/py-polars/dev/reference/series/api/polars.Series.take.html
index f943644d618e..aa93b88a1c1d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.take.html
+++ b/py-polars/dev/reference/series/api/polars.Series.take.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.take_every.html b/py-polars/dev/reference/series/api/polars.Series.take_every.html
index 101b3bef9cf6..64d0633e5505 100644
--- a/py-polars/dev/reference/series/api/polars.Series.take_every.html
+++ b/py-polars/dev/reference/series/api/polars.Series.take_every.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.tan.html b/py-polars/dev/reference/series/api/polars.Series.tan.html
index 3f398079c27e..e4fef2ad83ac 100644
--- a/py-polars/dev/reference/series/api/polars.Series.tan.html
+++ b/py-polars/dev/reference/series/api/polars.Series.tan.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.tanh.html b/py-polars/dev/reference/series/api/polars.Series.tanh.html
index e9d5e63eb188..5d1682e9b953 100644
--- a/py-polars/dev/reference/series/api/polars.Series.tanh.html
+++ b/py-polars/dev/reference/series/api/polars.Series.tanh.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_arrow.html b/py-polars/dev/reference/series/api/polars.Series.to_arrow.html
index 4f955523b1ab..c8c01484d149 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_arrow.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_arrow.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_dummies.html b/py-polars/dev/reference/series/api/polars.Series.to_dummies.html
index a06c6bda47d6..f5c9ffd054a5 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_dummies.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_dummies.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_frame.html b/py-polars/dev/reference/series/api/polars.Series.to_frame.html
index 901c9b5b817f..6c911f4fefc6 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_frame.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_frame.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_init_repr.html b/py-polars/dev/reference/series/api/polars.Series.to_init_repr.html
index 94d0d948481e..924b3a0a3e2f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_init_repr.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_init_repr.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_list.html b/py-polars/dev/reference/series/api/polars.Series.to_list.html
index 403dec887e0d..05c8267a7a61 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_list.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_list.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_numpy.html b/py-polars/dev/reference/series/api/polars.Series.to_numpy.html
index 379dc133fbdd..bd84f18abc69 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_numpy.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_numpy.html
@@ -1626,7 +1626,7 @@
 
    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/dev/reference/series/api/polars.Series.to_pandas.html b/py-polars/dev/reference/series/api/polars.Series.to_pandas.html
index e7f95bcd2d7a..403268e4942e 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_pandas.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_pandas.html
@@ -1626,7 +1626,7 @@
 
      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/dev/reference/series/api/polars.Series.to_physical.html b/py-polars/dev/reference/series/api/polars.Series.to_physical.html
index e4cb386c9973..d4a23c46529f 100644
--- a/py-polars/dev/reference/series/api/polars.Series.to_physical.html
+++ b/py-polars/dev/reference/series/api/polars.Series.to_physical.html
@@ -1626,7 +1626,7 @@
 
      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/dev/reference/series/api/polars.Series.top_k.html b/py-polars/dev/reference/series/api/polars.Series.top_k.html
index e4d828196322..4287c40c52d7 100644
--- a/py-polars/dev/reference/series/api/polars.Series.top_k.html
+++ b/py-polars/dev/reference/series/api/polars.Series.top_k.html
@@ -1627,7 +1627,7 @@
 
        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/dev/reference/series/api/polars.Series.unique.html b/py-polars/dev/reference/series/api/polars.Series.unique.html
index ec7bfd84f726..4907ab2de170 100644
--- a/py-polars/dev/reference/series/api/polars.Series.unique.html
+++ b/py-polars/dev/reference/series/api/polars.Series.unique.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.unique_counts.html b/py-polars/dev/reference/series/api/polars.Series.unique_counts.html
index 3bbd7f4eca5b..127174a8d21d 100644
--- a/py-polars/dev/reference/series/api/polars.Series.unique_counts.html
+++ b/py-polars/dev/reference/series/api/polars.Series.unique_counts.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.upper_bound.html b/py-polars/dev/reference/series/api/polars.Series.upper_bound.html
index 575f6a12e80c..ca4871a0a50b 100644
--- a/py-polars/dev/reference/series/api/polars.Series.upper_bound.html
+++ b/py-polars/dev/reference/series/api/polars.Series.upper_bound.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.value_counts.html b/py-polars/dev/reference/series/api/polars.Series.value_counts.html
index f738a4a4cc6b..84dbf1a8106a 100644
--- a/py-polars/dev/reference/series/api/polars.Series.value_counts.html
+++ b/py-polars/dev/reference/series/api/polars.Series.value_counts.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.var.html b/py-polars/dev/reference/series/api/polars.Series.var.html
index fc7b99e42d6a..604c4414e2b2 100644
--- a/py-polars/dev/reference/series/api/polars.Series.var.html
+++ b/py-polars/dev/reference/series/api/polars.Series.var.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.view.html b/py-polars/dev/reference/series/api/polars.Series.view.html
index 803609860385..d33dc4eedbb5 100644
--- a/py-polars/dev/reference/series/api/polars.Series.view.html
+++ b/py-polars/dev/reference/series/api/polars.Series.view.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/api/polars.Series.zip_with.html b/py-polars/dev/reference/series/api/polars.Series.zip_with.html
index 1887034ce107..00d9b5daf9e2 100644
--- a/py-polars/dev/reference/series/api/polars.Series.zip_with.html
+++ b/py-polars/dev/reference/series/api/polars.Series.zip_with.html
@@ -1626,7 +1626,7 @@
 
        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/dev/reference/series/index.html b/py-polars/dev/reference/series/index.html
index 8cf6375cd26f..7c51eb0f7e9d 100644
--- a/py-polars/dev/reference/series/index.html
+++ b/py-polars/dev/reference/series/index.html
@@ -1618,7 +1618,7 @@ 
        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:
        
@@ -2216,14 +2216,14 @@ 
        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:
        
@@ -2249,7 +2249,7 @@ 
        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:
        
@@ -2263,7 +2263,7 @@ 
        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:
        
@@ -2277,7 +2277,7 @@ 
        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:
        
@@ -2345,7 +2345,7 @@ 
        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
        
@@ -2410,7 +2410,7 @@ 
        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])
@@ -2428,7 +2428,7 @@ 
        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])
@@ -2447,7 +2447,7 @@ 
        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])
@@ -2465,7 +2465,7 @@ 
        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])
@@ -2483,7 +2483,7 @@ 
        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])
@@ -2501,7 +2501,7 @@ 
        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])
@@ -2523,7 +2523,7 @@ 
        Series
 
        
-arg_max() → int | None[source]
        
+arg_max() → int | None[source]
 
        Get the index of the maximal value.
        
 
        
 
        Returns:
        
@@ -2542,7 +2542,7 @@ 
        Series
 
        
-arg_min() → int | None[source]
        
+arg_min() → int | None[source]
 
        Get the index of the minimal value.
        
 
        
 
        Returns:
        
@@ -2561,7 +2561,7 @@ 
        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:
        
@@ -2591,7 +2591,7 @@ 
        Series
 
        
-arg_true() → Series[source]
        
+arg_true() → Series[source]
 
        Get index values where Boolean Series evaluate True.
        
 
        
 
        Returns:
        
@@ -2615,7 +2615,7 @@ 
        Series
 
        
-arg_unique() → Series[source]
        
+arg_unique() → Series[source]
 
        Get unique index as Series.
        
 
        
 
        Returns:
        
@@ -2640,7 +2640,7 @@ 
        Series
 
        
-bottom_k(k: int = 5) → Series[source]
        
+bottom_k(k: int = 5) → Series[source]
 
        Return the k smallest elements.
        
 
        This has time complexity:
        
 
        
@@ -2675,7 +2675,7 @@ 
        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:
        
@@ -2713,7 +2713,7 @@ 
        Series
 
        
-cbrt() → Series[source]
        
+cbrt() → Series[source]
 
        Compute the cube root of the elements.
        
 
        Optimization for
        
 
        >>> pl.Series([1, 2]) ** (1.0 / 3)
@@ -2729,7 +2729,7 @@ 
        Series
 
        
-ceil() → Series[source]
        
+ceil() → Series[source]
 
        Rounds up to the nearest integer value.
        
 
        Only works on floating point Series.
        
 
        Examples
        
@@ -2748,7 +2748,7 @@ 
        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])
@@ -2769,7 +2769,7 @@ 
        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.
        
 
        
@@ -2809,7 +2809,7 @@ 
        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”
@@ -2841,7 +2841,7 @@ 
        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”
@@ -2858,7 +2858,7 @@ 
        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”
@@ -2875,7 +2875,7 @@ 
        Series
 
        
-clone() → Self[source]
        
+clone() → Self[source]
 
        Very cheap deepcopy/clone.
        
 
        
 
        See also
        
@@ -2900,7 +2900,7 @@ 
        Series
 
        
-cos() → Series[source]
        
+cos() → Series[source]
 
        Compute the element-wise value for the cosine.
        
 
        Examples
        
 
        >>> import math
@@ -2919,7 +2919,7 @@ 
        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])
@@ -2937,7 +2937,7 @@ 
        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:
        
@@ -2963,7 +2963,7 @@ 
        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:
        
@@ -2989,7 +2989,7 @@ 
        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:
        
@@ -3018,7 +3018,7 @@ 
        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:
        
@@ -3047,7 +3047,7 @@ 
        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:
        
@@ -3088,7 +3088,7 @@ 
        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:
        
@@ -3178,7 +3178,7 @@ 
        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.
        
@@ -3236,7 +3236,7 @@ 
        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:
        
@@ -3288,7 +3288,7 @@ 
        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:
        
@@ -3309,20 +3309,20 @@ 
        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.
        
 
        
@@ -3348,13 +3348,13 @@ 
        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`.
        
@@ -3371,7 +3371,7 @@ 
        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
@@ -3402,7 +3402,7 @@ 
        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:
        
@@ -3472,7 +3472,7 @@ 
        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:
        
@@ -3557,7 +3557,7 @@ 
        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:
        
@@ -3642,13 +3642,13 @@ 
        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.
        
 
        
@@ -3672,7 +3672,7 @@ 
        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
@@ -3729,7 +3729,7 @@ 
        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:
        
@@ -3760,7 +3760,7 @@ 
        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:
        
@@ -3787,7 +3787,7 @@ 
        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:
        
@@ -3837,7 +3837,7 @@ 
        Series
 
        
-filter(predicate: Series | list[bool]) → Self[source]
        
+filter(predicate: Series | list[bool]) → Self[source]
 
        Filter elements by a boolean mask.
        
 
        
 
        Parameters:
        
@@ -3863,7 +3863,7 @@ 
        Series
 
        
-floor() → Series[source]
        
+floor() → Series[source]
 
        Rounds down to the nearest integer value.
        
 
        Only works on floating point Series.
        
 
        Examples
        
@@ -3882,25 +3882,25 @@ 
        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.
        
@@ -3908,7 +3908,7 @@ 
        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.
        
 
        
@@ -3941,7 +3941,7 @@ 
        Series
 
        
-head(n: int = 10) → Series[source]
        
+head(n: int = 10) → Series[source]
 
        Get the first n elements.
        
 
        
 
        Parameters:
        
@@ -3984,7 +3984,7 @@ 
        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:
        
@@ -4029,13 +4029,13 @@ 
        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:
        
@@ -4063,7 +4063,7 @@ 
        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:
        
@@ -4124,7 +4124,7 @@ 
        Series
 
        
-is_boolean() → bool[source]
        
+is_boolean() → bool[source]
 
        Check if this Series is a Boolean.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [True, False, True])
@@ -4136,7 +4136,7 @@ 
        Series
 
        
-is_duplicated() → Series[source]
        
+is_duplicated() → Series[source]
 
        Get mask of all duplicated values.
        
 
        
 
        Returns:
        
@@ -4163,7 +4163,7 @@ 
        Series
 
        
-is_empty() → bool[source]
        
+is_empty() → bool[source]
 
        Check if the Series is empty.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [], dtype=pl.Float32)
@@ -4175,7 +4175,7 @@ 
        Series
 
        
-is_finite() → Series[source]
        
+is_finite() → Series[source]
 
        Returns a boolean Series indicating which values are finite.
        
 
        
 
        Returns:
        
@@ -4202,7 +4202,7 @@ 
        Series
 
        
-is_first() → Series[source]
        
+is_first() → Series[source]
 
        Get a mask of the first unique value.
        
 
        
 
        Returns:
        
@@ -4216,7 +4216,7 @@ 
        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])
@@ -4228,7 +4228,7 @@ 
        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:
        
@@ -4283,7 +4283,7 @@ 
        Series
 
        
-is_infinite() → Series[source]
        
+is_infinite() → Series[source]
 
        Returns a boolean Series indicating which values are infinite.
        
 
        
 
        Returns:
        
@@ -4310,7 +4310,7 @@ 
        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:
        
@@ -4338,7 +4338,7 @@ 
        Series
 
        
-is_nan() → Series[source]
        
+is_nan() → Series[source]
 
        Returns a boolean Series indicating which values are not NaN.
        
 
        
 
        Returns:
        
@@ -4366,7 +4366,7 @@ 
        Series
 
        
-is_not_nan() → Series[source]
        
+is_not_nan() → Series[source]
 
        Returns a boolean Series indicating which values are not NaN.
        
 
        
 
        Returns:
        
@@ -4394,7 +4394,7 @@ 
        Series
 
        
-is_not_null() → Series[source]
        
+is_not_null() → Series[source]
 
        Returns a boolean Series indicating which values are not null.
        
 
        
 
        Returns:
        
@@ -4421,7 +4421,7 @@ 
        Series
 
        
-is_null() → Series[source]
        
+is_null() → Series[source]
 
        Returns a boolean Series indicating which values are null.
        
 
        
 
        Returns:
        
@@ -4448,7 +4448,7 @@ 
        Series
 
        
-is_numeric() → bool[source]
        
+is_numeric() → bool[source]
 
        Check if this Series datatype is numeric.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4460,7 +4460,7 @@ 
        Series
 
        
-is_sorted(*, descending: bool = False) → bool[source]
        
+is_sorted(*, descending: bool = False) → bool[source]
 
        Check if the Series is sorted.
        
 
        
 
        Parameters:
        
@@ -4474,7 +4474,7 @@ 
        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:
        
@@ -4497,7 +4497,7 @@ 
        Series
 
        
-is_unique() → Series[source]
        
+is_unique() → Series[source]
 
        Get mask of all unique values.
        
 
        
 
        Returns:
        
@@ -4524,7 +4524,7 @@ 
        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"])
@@ -4536,7 +4536,7 @@ 
        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].
        
@@ -4553,7 +4553,7 @@ 
        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
@@ -4576,13 +4576,13 @@ 
        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])
@@ -4594,7 +4594,7 @@ 
        Series
 
        
-limit(n: int = 10) → Series[source]
        
+limit(n: int = 10) → Series[source]
 
        Get the first n elements.
        
 
        Alias for Series.head().
        
 
        
@@ -4616,25 +4616,25 @@ 
        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
        
@@ -4666,13 +4666,13 @@ 
        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:
        
@@ -4748,7 +4748,7 @@ 
        Series
 
        
-max() → PythonLiteral | None[source]
        
+max() → PythonLiteral | None[source]
 
        Get the maximum value in this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4760,7 +4760,7 @@ 
        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])
@@ -4772,7 +4772,7 @@ 
        Series
 
        
-median() → float | None[source]
        
+median() → float | None[source]
 
        Get the median of this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4784,7 +4784,7 @@ 
        Series
 
        
-min() → PythonLiteral | None[source]
        
+min() → PythonLiteral | None[source]
 
        Get the minimal value in this Series.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3])
@@ -4796,7 +4796,7 @@ 
        Series
 
        
-mode() → Series[source]
        
+mode() → Series[source]
 
        Compute the most occurring value(s).
        
 
        Can return multiple Values.
        
 
        Examples
        
@@ -4813,7 +4813,7 @@ 
        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])
@@ -4836,7 +4836,7 @@ 
        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])
@@ -4848,7 +4848,7 @@ 
        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.
        
@@ -4856,7 +4856,7 @@ 
        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.
        
@@ -4864,13 +4864,13 @@ 
        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`.
        
@@ -4887,19 +4887,19 @@ 
        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.
        
@@ -4951,7 +4951,7 @@ 
        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])
@@ -4971,7 +4971,7 @@ 
        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])
@@ -4991,7 +4991,7 @@ 
        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:
        
@@ -5018,13 +5018,13 @@ 
        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:
        
@@ -5128,7 +5128,7 @@ 
        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:
        
@@ -5150,7 +5150,7 @@ 
        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:
        
@@ -5214,7 +5214,7 @@ 
        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:
        
@@ -5228,7 +5228,7 @@ 
        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.
        
@@ -5244,7 +5244,7 @@ 
        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:
        
@@ -5272,7 +5272,7 @@ 
        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:
        
@@ -5315,7 +5315,7 @@ 
        Series
 
        
-reverse() → Series[source]
        
+reverse() → Series[source]
 
        Return Series in reverse order.
        
 
        Examples
        
 
        >>> s = pl.Series("a", [1, 2, 3], dtype=pl.Int8)
@@ -5333,7 +5333,7 @@ 
        Series
 
        
-rle() → Series[source]
        
+rle() → Series[source]
 
        Get the lengths of runs of identical values.
        
 
        
 
        Returns:
        
@@ -5365,7 +5365,7 @@ 
        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
@@ -5404,7 +5404,7 @@ 
        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:
        
 
        
@@ -5454,7 +5454,7 @@ 
        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
@@ -5495,7 +5495,7 @@ 
        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
@@ -5536,7 +5536,7 @@ 
        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:
        
@@ -5575,7 +5575,7 @@ 
        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
@@ -5616,7 +5616,7 @@ 
        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.
        
@@ -5670,7 +5670,7 @@ 
        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.
        
@@ -5705,7 +5705,7 @@ 
        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
@@ -5749,7 +5749,7 @@ 
        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
@@ -5790,7 +5790,7 @@ 
        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
@@ -5834,7 +5834,7 @@ 
        Series
 
        
-round(decimals: int = 0) → Series[source]
        
+round(decimals: int = 0) → Series[source]
 
        Round underlying floating point data by decimals digits.
        
 
        
 
        Parameters:
        
@@ -5860,7 +5860,7 @@ 
        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:
        
@@ -5895,7 +5895,7 @@ 
        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.
        
@@ -5917,7 +5917,7 @@ 
        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:
        
@@ -5945,7 +5945,7 @@ 
        Series
 
        
-set(filter: Series, value: int | float | str) → Series[source]
        
+set(filter: Series, value: int | float | str) → Series[source]
 
        Set masked values.
        
 
        
 
        Parameters:
        
@@ -5993,7 +5993,7 @@ 
        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:
        
@@ -6047,7 +6047,7 @@ 
        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.
        
 
        
@@ -6073,7 +6073,7 @@ 
        Series
 
        
-shift(periods: int = 1) → Series[source]
        
+shift(periods: int = 1) → Series[source]
 
        Shift the values by a given period.
        
 
        
 
        Parameters:
        
@@ -6107,7 +6107,7 @@ 
        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:
        
@@ -6123,7 +6123,7 @@ 
        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.
        
@@ -6131,7 +6131,7 @@ 
        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).
        
@@ -6139,7 +6139,7 @@ 
        Series
 
        
-shuffle(seed: int | None = None) → Series[source]
        
+shuffle(seed: int | None = None) → Series[source]
 
        Shuffle the contents of this Series.
        
 
        
 
        Parameters:
        
@@ -6166,7 +6166,7 @@ 
        Series
 
        
-sign() → Series[source]
        
+sign() → Series[source]
 
        Compute the element-wise indication of the sign.
        
 
        The returned values can be -1, 0, or 1:
        
 
          
@@ -6193,7 +6193,7 @@ 
          Series
 
          
-sin() → Series[source]
          
+sin() → Series[source]
 
          Compute the element-wise value for the sine.
          
 
          Examples
          
 
          >>> import math
@@ -6212,7 +6212,7 @@ 
          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])
@@ -6230,7 +6230,7 @@ 
          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
@@ -6265,7 +6265,7 @@ 
          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:
          
@@ -6293,7 +6293,7 @@ 
          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:
          
@@ -6331,7 +6331,7 @@ 
          Series
 
          
-sqrt() → Series[source]
          
+sqrt() → Series[source]
 
          Compute the square root of the elements.
          
 
          Syntactic sugar for
          
 
          >>> pl.Series([1, 2]) ** 0.5
@@ -6347,7 +6347,7 @@ 
          Series
 
          
-std(ddof: int = 1) → float | None[source]
          
+std(ddof: int = 1) → float | None[source]
 
          Get the standard deviation of this Series.
          
 
          
 
          Parameters:
          
@@ -6369,7 +6369,7 @@ 
          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
@@ -6384,7 +6384,7 @@ 
          Series
 
          
-tail(n: int = 10) → Series[source]
          
+tail(n: int = 10) → Series[source]
 
          Get the last n elements.
          
 
          
 
          Parameters:
          
@@ -6427,7 +6427,7 @@ 
          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:
          
@@ -6452,7 +6452,7 @@ 
          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])
@@ -6469,7 +6469,7 @@ 
          Series
 
          
-tan() → Series[source]
          
+tan() → Series[source]
 
          Compute the element-wise value for the tangent.
          
 
          Examples
          
 
          >>> import math
@@ -6488,7 +6488,7 @@ 
          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])
@@ -6506,7 +6506,7 @@ 
          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
          
@@ -6525,7 +6525,7 @@ 
          Series
 
          
-to_dummies(separator: str = '_') → DataFrame[source]
          
+to_dummies(separator: str = '_') → DataFrame[source]
 
          Get dummy/indicator variables.
          
 
          
 
          Parameters:
          
@@ -6554,7 +6554,7 @@ 
          Series
 
          
-to_frame(name: str | None = None) → DataFrame[source]
          
+to_frame(name: str | None = None) → DataFrame[source]
 
          Cast this Series to a DataFrame.
          
 
          
 
          Parameters:
          
@@ -6596,7 +6596,7 @@ 
          Series
 
          
-to_init_repr(n: int = 1000) → str[source]
          
+to_init_repr(n: int = 1000) → str[source]
 
          Convert Series to instantiatable string representation.
          
 
          
 
          Parameters:
          
@@ -6633,7 +6633,7 @@ 
          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:
          
@@ -6655,7 +6655,7 @@ 
          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:
          
 
            
@@ -6699,7 +6699,7 @@ 
            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.
            
@@ -6750,7 +6750,7 @@ 
            Series
 
            
-to_physical() → Series[source]
            
+to_physical() → Series[source]
 
            Cast to physical representation of the logical dtype.
            
 
              
 
            • polars.datatypes.Date() -> polars.datatypes.Int32()
              • 
@@ -6781,7 +6781,7 @@ 
              Series
 
              
-top_k(k: int = 5) → Series[source]
              
+top_k(k: int = 5) → Series[source]
 
              Return the k largest elements.
              
 
              This has time complexity:
              
 
              
@@ -6816,7 +6816,7 @@ 
              Series
 
              
-unique(*, maintain_order: bool = False) → Series[source]
              
+unique(*, maintain_order: bool = False) → Series[source]
 
              Get unique elements in series.
              
 
              
 
              Parameters:
              
@@ -6842,7 +6842,7 @@ 
              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"])
@@ -6860,7 +6860,7 @@ 
              Series
 
              
-upper_bound() → Self[source]
              
+upper_bound() → Self[source]
 
              Return the upper bound of this Series’ dtype as a unit Series.
              
 
              
 
              See also
              
@@ -6892,7 +6892,7 @@ 
              Series
 
              
-value_counts(*, sort: bool = False) → DataFrame[source]
              
+value_counts(*, sort: bool = False) → DataFrame[source]
 
              Count the unique values in a Series.
              
 
              
 
              Parameters:
              
@@ -6921,7 +6921,7 @@ 
              Series
 
              
-var(ddof: int = 1) → float | None[source]
              
+var(ddof: int = 1) → float | None[source]
 
              Get variance of this Series.
              
 
              
 
              Parameters:
              
@@ -6943,7 +6943,7 @@ 
              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.
              
@@ -6966,7 +6966,7 @@ 
              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/dev/searchindex.js b/py-polars/dev/searchindex.js
index a515a456c135..41b613ec8ebe 100644
--- a/py-polars/dev/searchindex.js
+++ b/py-polars/dev/searchindex.js
@@ -1 +1 @@
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"polars.read_ipc", "polars.read_ipc_schema", "polars.read_json", "polars.read_ndjson", "polars.read_parquet", "polars.read_parquet_schema", "polars.scan_csv", "polars.scan_delta", "polars.scan_ipc", "polars.scan_ndjson", "polars.scan_parquet", "polars.scan_pyarrow_dataset", "polars.show_versions", "polars.testing.assert_frame_equal", "polars.testing.assert_series_equal", "polars.testing.parametric.column", "polars.testing.parametric.columns", "polars.testing.parametric.create_list_strategy", "polars.testing.parametric.dataframes", "polars.testing.parametric.load_profile", "polars.testing.parametric.series", "polars.testing.parametric.set_profile", "polars.threadpool_size", "polars.using_string_cache", "Config", "Aggregation", "polars.DataFrame.__dataframe__", "polars.DataFrame.apply", "polars.DataFrame.bottom_k", "polars.DataFrame.clear", "polars.DataFrame.clone", "polars.DataFrame.columns", "polars.DataFrame.corr", "polars.DataFrame.describe", "polars.DataFrame.drop", "polars.DataFrame.drop_in_place", "polars.DataFrame.drop_nulls", "polars.DataFrame.dtypes", "polars.DataFrame.estimated_size", "polars.DataFrame.explode", "polars.DataFrame.extend", "polars.DataFrame.fill_nan", "polars.DataFrame.fill_null", "polars.DataFrame.filter", "polars.DataFrame.find_idx_by_name", "polars.DataFrame.flags", "polars.DataFrame.fold", "polars.DataFrame.frame_equal", "polars.DataFrame.get_column", "polars.DataFrame.get_columns", "polars.DataFrame.glimpse", "polars.DataFrame.groupby", "polars.DataFrame.groupby_dynamic", "polars.DataFrame.groupby_rolling", "polars.DataFrame.hash_rows", "polars.DataFrame.head", "polars.DataFrame.height", "polars.DataFrame.hstack", "polars.DataFrame.insert_at_idx", "polars.DataFrame.interpolate", "polars.DataFrame.is_duplicated", "polars.DataFrame.is_empty", "polars.DataFrame.is_unique", "polars.DataFrame.item", "polars.DataFrame.iter_rows", "polars.DataFrame.iter_slices", "polars.DataFrame.join", "polars.DataFrame.join_asof", "polars.DataFrame.lazy", "polars.DataFrame.limit", "polars.DataFrame.max", "polars.DataFrame.mean", "polars.DataFrame.median", "polars.DataFrame.melt", "polars.DataFrame.merge_sorted", "polars.DataFrame.min", "polars.DataFrame.n_chunks", "polars.DataFrame.n_unique", "polars.DataFrame.null_count", "polars.DataFrame.partition_by", "polars.DataFrame.pipe", "polars.DataFrame.pivot", "polars.DataFrame.product", "polars.DataFrame.quantile", "polars.DataFrame.rechunk", "polars.DataFrame.rename", "polars.DataFrame.replace", "polars.DataFrame.replace_at_idx", "polars.DataFrame.reverse", "polars.DataFrame.row", "polars.DataFrame.rows", "polars.DataFrame.rows_by_key", "polars.DataFrame.sample", "polars.DataFrame.schema", "polars.DataFrame.select", "polars.DataFrame.set_sorted", "polars.DataFrame.shape", "polars.DataFrame.shift", "polars.DataFrame.shift_and_fill", "polars.DataFrame.shrink_to_fit", "polars.DataFrame.slice", "polars.DataFrame.sort", "polars.DataFrame.std", "polars.DataFrame.sum", "polars.DataFrame.tail", "polars.DataFrame.take_every", "polars.DataFrame.to_arrow", "polars.DataFrame.to_dict", "polars.DataFrame.to_dicts", "polars.DataFrame.to_dummies", "polars.DataFrame.to_init_repr", "polars.DataFrame.to_numpy", "polars.DataFrame.to_pandas", "polars.DataFrame.to_series", "polars.DataFrame.to_struct", "polars.DataFrame.top_k", "polars.DataFrame.transpose", "polars.DataFrame.unique", "polars.DataFrame.unnest", "polars.DataFrame.unstack", "polars.DataFrame.update", "polars.DataFrame.upsample", "polars.DataFrame.var", "polars.DataFrame.vstack", "polars.DataFrame.width", "polars.DataFrame.with_columns", "polars.DataFrame.with_row_count", "polars.dataframe.groupby.GroupBy.__iter__", "polars.dataframe.groupby.GroupBy.agg", "polars.dataframe.groupby.GroupBy.all", "polars.dataframe.groupby.GroupBy.apply", "polars.dataframe.groupby.GroupBy.count", "polars.dataframe.groupby.GroupBy.first", "polars.dataframe.groupby.GroupBy.head", "polars.dataframe.groupby.GroupBy.last", "polars.dataframe.groupby.GroupBy.max", "polars.dataframe.groupby.GroupBy.mean", "polars.dataframe.groupby.GroupBy.median", "polars.dataframe.groupby.GroupBy.min", "polars.dataframe.groupby.GroupBy.n_unique", "polars.dataframe.groupby.GroupBy.quantile", "polars.dataframe.groupby.GroupBy.sum", "polars.dataframe.groupby.GroupBy.tail", "Attributes", "Computation", "Descriptive", "Export", "GroupBy", "DataFrame", "Miscellaneous", "Manipulation/selection", "Data types", "Exceptions", "Aggregation", "polars.Expr.abs", "polars.Expr.add", "polars.Expr.agg_groups", "polars.Expr.alias", "polars.Expr.all", "polars.Expr.and_", "polars.Expr.any", "polars.Expr.append", "polars.Expr.apply", "polars.Expr.approx_unique", "polars.Expr.arccos", "polars.Expr.arccosh", "polars.Expr.arcsin", "polars.Expr.arcsinh", "polars.Expr.arctan", "polars.Expr.arctanh", "polars.Expr.arg_max", "polars.Expr.arg_min", "polars.Expr.arg_sort", "polars.Expr.arg_true", "polars.Expr.arg_unique", "polars.Expr.arr.max", "polars.Expr.arr.min", "polars.Expr.arr.sum", "polars.Expr.arr.unique", "polars.Expr.backward_fill", "polars.Expr.bin.contains", "polars.Expr.bin.decode", "polars.Expr.bin.encode", "polars.Expr.bin.ends_with", "polars.Expr.bin.starts_with", "polars.Expr.bottom_k", "polars.Expr.cache", "polars.Expr.cast", "polars.Expr.cat.get_categories", "polars.Expr.cat.set_ordering", "polars.Expr.cbrt", "polars.Expr.ceil", "polars.Expr.clip", "polars.Expr.clip_max", "polars.Expr.clip_min", "polars.Expr.cos", "polars.Expr.cosh", "polars.Expr.count", "polars.Expr.cumcount", "polars.Expr.cummax", "polars.Expr.cummin", "polars.Expr.cumprod", "polars.Expr.cumsum", "polars.Expr.cumulative_eval", "polars.Expr.cut", "polars.Expr.degrees", "polars.Expr.diff", "polars.Expr.dot", "polars.Expr.drop_nans", "polars.Expr.drop_nulls", "polars.Expr.dt.base_utc_offset", "polars.Expr.dt.cast_time_unit", "polars.Expr.dt.combine", "polars.Expr.dt.convert_time_zone", "polars.Expr.dt.date", "polars.Expr.dt.datetime", "polars.Expr.dt.day", "polars.Expr.dt.days", "polars.Expr.dt.dst_offset", "polars.Expr.dt.epoch", "polars.Expr.dt.hour", "polars.Expr.dt.hours", "polars.Expr.dt.is_leap_year", "polars.Expr.dt.iso_year", "polars.Expr.dt.microsecond", "polars.Expr.dt.microseconds", "polars.Expr.dt.millisecond", "polars.Expr.dt.milliseconds", "polars.Expr.dt.minute", "polars.Expr.dt.minutes", "polars.Expr.dt.month", "polars.Expr.dt.month_end", "polars.Expr.dt.month_start", "polars.Expr.dt.nanosecond", "polars.Expr.dt.nanoseconds", "polars.Expr.dt.offset_by", "polars.Expr.dt.ordinal_day", "polars.Expr.dt.quarter", "polars.Expr.dt.replace_time_zone", "polars.Expr.dt.round", "polars.Expr.dt.second", "polars.Expr.dt.seconds", "polars.Expr.dt.strftime", "polars.Expr.dt.time", "polars.Expr.dt.timestamp", "polars.Expr.dt.to_string", "polars.Expr.dt.truncate", "polars.Expr.dt.week", "polars.Expr.dt.weekday", "polars.Expr.dt.with_time_unit", "polars.Expr.dt.year", "polars.Expr.entropy", "polars.Expr.eq", "polars.Expr.eq_missing", "polars.Expr.ewm_mean", "polars.Expr.ewm_std", "polars.Expr.ewm_var", "polars.Expr.exclude", "polars.Expr.exp", "polars.Expr.explode", "polars.Expr.extend_constant", "polars.Expr.fill_nan", "polars.Expr.fill_null", "polars.Expr.filter", "polars.Expr.first", "polars.Expr.flatten", "polars.Expr.floor", "polars.Expr.floordiv", "polars.Expr.forward_fill", "polars.Expr.from_json", "polars.Expr.ge", "polars.Expr.gt", "polars.Expr.hash", "polars.Expr.head", "polars.Expr.implode", "polars.Expr.inspect", "polars.Expr.interpolate", "polars.Expr.is_between", "polars.Expr.is_duplicated", "polars.Expr.is_finite", "polars.Expr.is_first", "polars.Expr.is_in", "polars.Expr.is_infinite", "polars.Expr.is_nan", "polars.Expr.is_not", "polars.Expr.is_not_nan", "polars.Expr.is_not_null", "polars.Expr.is_null", "polars.Expr.is_unique", "polars.Expr.keep_name", "polars.Expr.kurtosis", "polars.Expr.last", "polars.Expr.le", "polars.Expr.len", "polars.Expr.limit", "polars.Expr.list.all", "polars.Expr.list.any", "polars.Expr.list.arg_max", "polars.Expr.list.arg_min", "polars.Expr.list.concat", "polars.Expr.list.contains", "polars.Expr.list.count_match", "polars.Expr.list.diff", "polars.Expr.list.difference", "polars.Expr.list.eval", "polars.Expr.list.explode", "polars.Expr.list.first", "polars.Expr.list.get", "polars.Expr.list.head", "polars.Expr.list.intersection", "polars.Expr.list.join", "polars.Expr.list.last", "polars.Expr.list.lengths", "polars.Expr.list.max", "polars.Expr.list.mean", "polars.Expr.list.min", "polars.Expr.list.reverse", "polars.Expr.list.shift", "polars.Expr.list.slice", "polars.Expr.list.sort", "polars.Expr.list.sum", "polars.Expr.list.tail", "polars.Expr.list.take", "polars.Expr.list.to_struct", "polars.Expr.list.union", "polars.Expr.list.unique", "polars.Expr.log", "polars.Expr.log10", "polars.Expr.log1p", "polars.Expr.lower_bound", "polars.Expr.lt", "polars.Expr.map", "polars.Expr.map_alias", "polars.Expr.map_dict", "polars.Expr.max", "polars.Expr.mean", "polars.Expr.median", "polars.Expr.meta.eq", "polars.Expr.meta.has_multiple_outputs", "polars.Expr.meta.is_regex_projection", "polars.Expr.meta.ne", "polars.Expr.meta.output_name", "polars.Expr.meta.pop", "polars.Expr.meta.root_names", "polars.Expr.meta.tree_format", "polars.Expr.meta.undo_aliases", "polars.Expr.meta.write_json", "polars.Expr.min", "polars.Expr.mod", "polars.Expr.mode", "polars.Expr.mul", "polars.Expr.n_unique", "polars.Expr.nan_max", "polars.Expr.nan_min", "polars.Expr.ne", "polars.Expr.ne_missing", "polars.Expr.null_count", "polars.Expr.or_", "polars.Expr.over", "polars.Expr.pct_change", "polars.Expr.pipe", "polars.Expr.pow", "polars.Expr.prefix", "polars.Expr.product", "polars.Expr.qcut", "polars.Expr.quantile", "polars.Expr.radians", "polars.Expr.rank", "polars.Expr.rechunk", "polars.Expr.reinterpret", "polars.Expr.repeat_by", "polars.Expr.reshape", "polars.Expr.reverse", "polars.Expr.rle", "polars.Expr.rle_id", "polars.Expr.rolling_apply", "polars.Expr.rolling_max", "polars.Expr.rolling_mean", "polars.Expr.rolling_median", "polars.Expr.rolling_min", "polars.Expr.rolling_quantile", "polars.Expr.rolling_skew", "polars.Expr.rolling_std", "polars.Expr.rolling_sum", "polars.Expr.rolling_var", "polars.Expr.round", "polars.Expr.sample", "polars.Expr.search_sorted", "polars.Expr.set_sorted", "polars.Expr.shift", "polars.Expr.shift_and_fill", "polars.Expr.shrink_dtype", "polars.Expr.shuffle", "polars.Expr.sign", "polars.Expr.sin", "polars.Expr.sinh", "polars.Expr.skew", "polars.Expr.slice", "polars.Expr.sort", "polars.Expr.sort_by", "polars.Expr.sqrt", "polars.Expr.std", "polars.Expr.str.concat", "polars.Expr.str.contains", "polars.Expr.str.count_match", "polars.Expr.str.decode", "polars.Expr.str.encode", "polars.Expr.str.ends_with", "polars.Expr.str.explode", "polars.Expr.str.extract", "polars.Expr.str.extract_all", "polars.Expr.str.json_extract", "polars.Expr.str.json_path_match", "polars.Expr.str.lengths", "polars.Expr.str.ljust", "polars.Expr.str.lstrip", "polars.Expr.str.n_chars", "polars.Expr.str.parse_int", "polars.Expr.str.replace", "polars.Expr.str.replace_all", "polars.Expr.str.rjust", "polars.Expr.str.rstrip", "polars.Expr.str.slice", "polars.Expr.str.split", "polars.Expr.str.split_exact", "polars.Expr.str.splitn", "polars.Expr.str.starts_with", "polars.Expr.str.strip", "polars.Expr.str.strptime", "polars.Expr.str.to_date", "polars.Expr.str.to_datetime", "polars.Expr.str.to_decimal", "polars.Expr.str.to_lowercase", "polars.Expr.str.to_time", "polars.Expr.str.to_titlecase", "polars.Expr.str.to_uppercase", "polars.Expr.str.zfill", "polars.Expr.struct.field", "polars.Expr.struct.rename_fields", "polars.Expr.sub", "polars.Expr.suffix", "polars.Expr.sum", "polars.Expr.tail", "polars.Expr.take", "polars.Expr.take_every", "polars.Expr.tan", "polars.Expr.tanh", "polars.Expr.to_physical", "polars.Expr.top_k", "polars.Expr.truediv", "polars.Expr.unique", "polars.Expr.unique_counts", "polars.Expr.upper_bound", "polars.Expr.value_counts", "polars.Expr.var", "polars.Expr.where", "polars.Expr.xor", "polars.all", "polars.all_horizontal", "polars.any", "polars.any_horizontal", "polars.apply", "polars.approx_unique", "polars.arange", "polars.arctan2", "polars.arctan2d", "polars.arg_sort_by", "polars.arg_where", "polars.avg", "polars.coalesce", "polars.col", "polars.concat_list", "polars.concat_str", "polars.corr", "polars.count", "polars.cov", "polars.cumfold", "polars.cumreduce", "polars.cumsum", "polars.cumsum_horizontal", "polars.date", "polars.date_range", "polars.date_ranges", "polars.datetime", "polars.duration", "polars.element", "polars.exclude", "polars.first", "polars.fold", "polars.format", "polars.from_epoch", "polars.groups", "polars.head", "polars.implode", "polars.int_range", "polars.int_ranges", "polars.last", "polars.lit", "polars.map", "polars.max", "polars.max_horizontal", "polars.mean", "polars.median", "polars.min", "polars.min_horizontal", "polars.n_unique", "polars.ones", "polars.quantile", "polars.reduce", "polars.repeat", "polars.rolling_corr", "polars.rolling_cov", "polars.select", "polars.sql_expr", "polars.std", "polars.struct", "polars.sum", "polars.sum_horizontal", "polars.tail", "polars.time", "polars.time_range", "polars.time_ranges", "polars.var", "polars.when", "polars.zeros", "Array", "Binary", "Boolean", "Categories", "Columns / names", "Computation", "Functions", "Expressions", "List", "Meta", "Miscellaneous", "Manipulation/selection", "Operators", "String", "Struct", "Temporal", "Window", "Functions", "API reference", "Input/output", "Aggregation", "polars.LazyFrame.bottom_k", "polars.LazyFrame.cache", "polars.LazyFrame.clear", "polars.LazyFrame.clone", "polars.LazyFrame.collect", "polars.LazyFrame.columns", "polars.LazyFrame.drop", "polars.LazyFrame.drop_nulls", "polars.LazyFrame.dtypes", "polars.LazyFrame.explain", "polars.LazyFrame.explode", "polars.LazyFrame.fetch", "polars.LazyFrame.fill_nan", "polars.LazyFrame.fill_null", "polars.LazyFrame.filter", "polars.LazyFrame.first", "polars.LazyFrame.from_json", "polars.LazyFrame.groupby", "polars.LazyFrame.groupby_dynamic", "polars.LazyFrame.groupby_rolling", "polars.LazyFrame.head", "polars.LazyFrame.inspect", "polars.LazyFrame.interpolate", "polars.LazyFrame.join", "polars.LazyFrame.join_asof", "polars.LazyFrame.last", "polars.LazyFrame.lazy", "polars.LazyFrame.limit", "polars.LazyFrame.map", "polars.LazyFrame.max", "polars.LazyFrame.mean", "polars.LazyFrame.median", "polars.LazyFrame.melt", "polars.LazyFrame.merge_sorted", "polars.LazyFrame.min", "polars.LazyFrame.null_count", "polars.LazyFrame.pipe", "polars.LazyFrame.profile", "polars.LazyFrame.quantile", "polars.LazyFrame.read_json", "polars.LazyFrame.rename", "polars.LazyFrame.reverse", "polars.LazyFrame.schema", "polars.LazyFrame.select", "polars.LazyFrame.set_sorted", "polars.LazyFrame.shift", "polars.LazyFrame.shift_and_fill", "polars.LazyFrame.show_graph", "polars.LazyFrame.slice", "polars.LazyFrame.sort", "polars.LazyFrame.std", "polars.LazyFrame.sum", "polars.LazyFrame.tail", "polars.LazyFrame.take_every", "polars.LazyFrame.top_k", "polars.LazyFrame.unique", "polars.LazyFrame.unnest", "polars.LazyFrame.update", "polars.LazyFrame.var", "polars.LazyFrame.width", "polars.LazyFrame.with_columns", "polars.LazyFrame.with_context", "polars.LazyFrame.with_row_count", "polars.LazyFrame.write_json", "polars.lazyframe.groupby.LazyGroupBy.agg", "polars.lazyframe.groupby.LazyGroupBy.all", "polars.lazyframe.groupby.LazyGroupBy.apply", "polars.lazyframe.groupby.LazyGroupBy.count", "polars.lazyframe.groupby.LazyGroupBy.first", "polars.lazyframe.groupby.LazyGroupBy.head", "polars.lazyframe.groupby.LazyGroupBy.last", "polars.lazyframe.groupby.LazyGroupBy.max", "polars.lazyframe.groupby.LazyGroupBy.mean", "polars.lazyframe.groupby.LazyGroupBy.median", "polars.lazyframe.groupby.LazyGroupBy.min", "polars.lazyframe.groupby.LazyGroupBy.n_unique", "polars.lazyframe.groupby.LazyGroupBy.quantile", "polars.lazyframe.groupby.LazyGroupBy.sum", "polars.lazyframe.groupby.LazyGroupBy.tail", "Attributes", "Descriptive", "GroupBy", "LazyFrame", "Miscellaneous", "Manipulation/selection", "Selectors", "Aggregation", "polars.Series.abs", "polars.Series.alias", "polars.Series.all", "polars.Series.any", "polars.Series.append", "polars.Series.apply", "polars.Series.arccos", "polars.Series.arccosh", "polars.Series.arcsin", "polars.Series.arcsinh", "polars.Series.arctan", "polars.Series.arctanh", "polars.Series.arg_max", "polars.Series.arg_min", "polars.Series.arg_sort", "polars.Series.arg_true", "polars.Series.arg_unique", "polars.Series.arr.max", "polars.Series.arr.min", "polars.Series.arr.sum", "polars.Series.arr.unique", "polars.Series.bin.contains", "polars.Series.bin.decode", "polars.Series.bin.encode", "polars.Series.bin.ends_with", "polars.Series.bin.starts_with", "polars.Series.bottom_k", 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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, 428, 435, 437, 439, 475, 476, 482, 483, 484, 485, 486, 488, 489, 490, 497, 517, 534, 547, 549, 553, 558, 567, 569, 580, 592, 596, 600, 601, 603, 604, 612, 615, 621, 630, 638, 670, 671, 676, 734, 737, 756, 757, 766, 768, 773, 774, 775, 776, 782, 783, 786, 787, 835, 839, 863, 869, 876, 888, 911, 918, 919, 943, 959, 966, 987, 1004, 1018, 1028, 1030, 1031, 1032, 1036, 1049, 1057], "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, 436, 454, 456, 460, 461, 463, 467, 468, 471, 473, 482, 483, 484, 485, 486, 488, 489, 490, 537, 545, 546, 555, 557, 562, 567, 579, 587, 613, 615, 626, 631, 632, 634, 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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, 355, 378, 387, 409, 415, 424, 428, 429, 430, 439, 466, 470, 481, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 502, 503, 521, 527, 528, 533, 534, 535, 536, 539, 569, 573, 587, 588, 600, 601, 612, 615, 616, 617, 621, 626, 627, 629, 630, 638, 649, 659, 661, 669, 670, 671, 675, 676, 680, 684, 695, 700, 707, 712, 715, 718, 734, 743, 744, 835, 854, 867, 892, 898, 907, 911, 912, 913, 919, 946, 947, 948, 949, 950, 951, 953, 954, 955, 957, 961, 968, 972, 973, 991, 997, 998, 1003, 1004, 1005, 1006, 1009, 1030, 1049, 1057], "string": [5, 7, 9, 12, 13, 14, 18, 28, 29, 31, 33, 34, 38, 52, 58, 66, 75, 97, 101, 102, 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, 416, 439, 450, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 508, 509, 512, 513, 514, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 542, 563, 564, 565, 566, 572, 575, 577, 578, 584, 585, 587, 588, 595, 605, 606, 609, 610, 618, 621, 622, 623, 626, 627, 629, 638, 652, 661, 668, 669, 670, 671, 676, 695, 701, 706, 712, 715, 716, 734, 737, 763, 769, 821, 825, 828, 831, 832, 844, 860, 899, 977, 978, 979, 983, 984, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1008, 1010, 1011, 1012, 1028, 1049, 1057], "classmethod": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 375, 638, 668, 691, 734], "activ": [6, 10, 16, 17, 19, 20, 21, 22, 25, 292, 410, 516, 638, 893, 986], "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, 410, 425, 428, 431, 436, 437, 443, 444, 445, 446, 450, 460, 461, 463, 470, 473, 475, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 492, 494, 497, 498, 502, 504, 505, 509, 511, 513, 523, 524, 525, 529, 530, 532, 534, 535, 536, 539, 544, 556, 559, 562, 563, 564, 565, 566, 567, 569, 572, 573, 579, 582, 587, 588, 600, 601, 603, 612, 615, 621, 626, 627, 630, 638, 652, 654, 656, 661, 663, 665, 669, 670, 671, 675, 676, 680, 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117, 215, 225, 226, 254, 268, 309, 345, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 591, 638, 709, 734, 784, 825, 856, 936, 1049, 1057], "consid": [6, 101, 102, 112, 117, 133, 142, 153, 179, 196, 223, 225, 226, 254, 268, 298, 299, 300, 309, 345, 437, 482, 483, 484, 485, 486, 488, 489, 490, 582, 594, 638, 659, 672, 679, 684, 707, 709, 718, 734, 744, 774, 775, 776, 784, 825, 856, 869, 936, 959, 960, 961, 1049], "break": [6, 117, 225, 226, 254, 268, 309, 310, 345, 470, 482, 483, 484, 485, 486, 488, 489, 490, 638, 709, 734, 784, 785, 825, 856, 936, 1049], "chang": [6, 26, 67, 101, 102, 117, 225, 226, 227, 254, 268, 309, 312, 345, 465, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 638, 709, 734, 743, 784, 825, 856, 932, 936, 967, 1049], "current": [6, 9, 26, 54, 91, 97, 103, 129, 132, 135, 136, 172, 254, 324, 345, 429, 465, 638, 649, 654, 655, 675, 734, 737, 773, 777, 792, 800, 825, 932, 1049, 1056], "alpha": [6, 73, 268, 360, 361, 362, 638, 656, 661, 663, 689, 699, 734, 840, 841, 842, 1049], "state": [6, 8, 73, 83, 129, 582, 649, 656, 661, 663, 689, 699, 734], "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, 452, 494, 518, 638, 691, 699, 715, 734, 962, 988, 1049], "previous": 7, "save": [7, 158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 649, 670, 671, 676, 734, 800, 821, 825, 832], "share": [7, 58, 144, 254, 839, 1049], "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, 481, 482, 483, 484, 485, 486, 488, 489, 490, 502, 528, 603, 621, 629, 638, 649, 675, 676, 684, 734, 737, 876, 880, 946, 947, 948, 949, 950, 951, 953, 954, 955, 972, 998, 1027, 1049], "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, 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1005, 1006, 1009, 1037, 1040, 1049, 1056, 1057], "json": [7, 9, 33, 34, 108, 109, 115, 254, 375, 452, 517, 518, 638, 649, 668, 691, 715, 734, 987, 988], "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, 254, 452, 663, 691, 715, 734, 743, 845, 1049], "produc": [7, 123, 124, 126, 407, 447, 587, 588, 604, 680, 734, 890], "filepath": 7, "same": [7, 18, 30, 31, 56, 58, 67, 73, 74, 75, 106, 112, 122, 130, 133, 140, 157, 197, 207, 223, 231, 234, 236, 254, 260, 267, 443, 446, 464, 481, 482, 483, 484, 485, 486, 488, 489, 490, 505, 544, 549, 576, 587, 638, 658, 669, 680, 701, 707, 712, 716, 734, 739, 743, 744, 794, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1040, 1049], "reset": [8, 130], "default": [8, 13, 26, 27, 28, 30, 31, 32, 33, 34, 35, 48, 67, 74, 90, 92, 93, 94, 95, 96, 97, 101, 102, 105, 108, 109, 110, 112, 121, 122, 123, 124, 125, 126, 142, 144, 157, 159, 160, 170, 172, 173, 179, 185, 187, 195, 196, 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675, 680, 734, 880, 1049], "esp": 13, "when": [13, 15, 17, 21, 31, 35, 38, 48, 57, 58, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 101, 102, 106, 110, 112, 113, 114, 115, 124, 126, 133, 134, 143, 144, 146, 148, 152, 158, 159, 172, 186, 195, 197, 200, 207, 215, 217, 221, 223, 254, 298, 299, 300, 344, 352, 360, 361, 362, 368, 429, 437, 439, 448, 466, 480, 504, 505, 521, 527, 533, 534, 572, 582, 587, 594, 638, 652, 665, 670, 671, 675, 680, 695, 701, 706, 707, 734, 743, 774, 775, 776, 824, 832, 839, 840, 841, 842, 845, 848, 919, 945, 960, 961, 991, 997, 1003, 1004, 1026, 1049], "have": [13, 26, 31, 38, 67, 75, 91, 101, 102, 103, 105, 124, 126, 146, 159, 170, 190, 196, 197, 214, 254, 309, 360, 361, 362, 448, 470, 473, 534, 536, 587, 638, 671, 684, 699, 714, 734, 737, 743, 784, 840, 841, 842, 845, 853, 936, 938, 1004, 1006, 1049, 1057], "larg": [13, 67, 158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], 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182, 185, 195, 196, 200, 209, 213, 226, 231, 254, 263, 286, 318, 358, 359, 366, 373, 376, 377, 383, 398, 436, 439, 454, 456, 460, 461, 467, 509, 524, 525, 545, 555, 563, 564, 565, 566, 569, 573, 575, 577, 578, 584, 585, 586, 587, 588, 589, 600, 601, 603, 605, 606, 609, 610, 612, 615, 618, 621, 622, 623, 625, 626, 627, 630, 638, 665, 695, 709, 712, 734, 737, 760, 794, 846, 860, 960, 961, 979, 994, 995, 1049, 1056], "left": [14, 31, 54, 67, 119, 120, 158, 159, 172, 173, 226, 254, 360, 361, 362, 382, 383, 482, 483, 484, 485, 486, 488, 489, 490, 493, 520, 542, 553, 575, 582, 583, 587, 588, 594, 614, 626, 627, 629, 638, 670, 671, 675, 676, 709, 734, 840, 841, 842, 860, 958, 990, 1012, 1032, 1049], "center": [14, 31, 254, 360, 361, 362, 481, 482, 483, 484, 485, 486, 488, 489, 490, 638, 840, 841, 842, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1049], "right": [14, 16, 31, 101, 102, 119, 120, 158, 159, 172, 173, 254, 310, 360, 361, 362, 383, 409, 415, 430, 470, 482, 483, 484, 485, 486, 488, 489, 490, 493, 502, 526, 575, 587, 588, 626, 627, 638, 670, 671, 675, 676, 734, 785, 840, 841, 842, 860, 892, 898, 913, 936, 958, 972, 996, 1049], "cell": [14, 31, 254], "align": [14, 31, 67, 74, 254, 542, 1012], "keyerror": [14, 18], "recognis": [14, 18, 121], "column_abc": 14, "column_xyz": 14, "visibl": [15, 144, 254, 839, 1049], "eg": [15, 23, 31, 103, 254, 345, 534, 536, 555, 638, 1004, 1006], "low": [15, 128], "rang": [15, 31, 103, 139, 144, 158, 171, 254, 310, 311, 322, 336, 342, 343, 345, 352, 353, 382, 470, 569, 577, 586, 587, 588, 589, 600, 601, 625, 626, 627, 638, 670, 734, 785, 786, 798, 816, 822, 823, 825, 832, 833, 839, 932, 936, 1049], "100": [15, 31, 93, 96, 101, 102, 112, 115, 254, 517, 537, 542, 734, 947, 948, 950, 987, 1007, 1049, 1057], "98": [15, 164, 254, 291, 504, 537, 549, 554, 638, 1007], "99": [15, 31, 147, 148, 164, 167, 254, 262, 291, 366, 368, 504, 549, 554, 638, 664, 665, 734, 838, 846, 1049], "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, 414, 423, 424, 427, 433, 465, 466, 470, 503, 542, 548, 562, 575, 582, 587, 591, 594, 596, 598, 603, 604, 624, 627, 638, 656, 670, 671, 672, 674, 679, 688, 689, 695, 703, 704, 712, 716, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 737, 744, 774, 785, 787, 792, 794, 800, 824, 832, 835, 838, 855, 867, 882, 891, 897, 906, 907, 910, 916, 932, 960, 961, 1019, 1049, 1057], "95": [15, 262, 638], "96": [15, 262, 638], "97": [15, 164, 254, 262, 638], "move": [16, 197, 254, 360, 361, 362, 482, 483, 485, 489, 638, 840, 841, 842, 947, 948, 950, 954, 1049], "inlin": [16, 197, 254, 509, 515, 516, 524, 979, 985, 986, 994], "parenthes": 16, "below": [17, 31, 104, 113, 142, 254, 368, 587, 588, 629, 638, 659, 734], "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, 510, 515, 518, 638, 980, 985, 988], "rounded_corn": 18, "style": [18, 31, 187, 254], "border": 18, "line": [18, 31, 101, 102, 105, 112, 156, 166, 168, 254, 515, 985], "includ": [18, 26, 28, 30, 31, 72, 104, 113, 124, 134, 139, 144, 158, 185, 197, 221, 222, 225, 254, 310, 346, 383, 470, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 527, 529, 530, 533, 582, 616, 617, 638, 652, 670, 706, 734, 785, 786, 826, 839, 860, 936, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 999, 1000, 1003, 1039, 1049], "divid": [18, 345, 352, 360, 361, 362, 396, 638, 825, 832, 840, 841, 842, 880, 1049], "dens": [18, 156, 254, 473, 638, 938, 1049], "space": [18, 158, 254, 470, 638, 670, 734, 936, 1049], "horizont": [18, 74, 152, 163, 225, 254, 563, 564, 565, 566, 577, 578, 582, 583, 584, 585, 591, 594, 605, 606, 609, 610, 614, 622, 623], "markdown": 18, "compat": [18, 31, 35, 48, 254, 509, 510, 515, 516, 524, 525, 734, 737, 979, 980, 985, 986, 994, 995], "No": [18, 540, 1010], "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, 437, 466, 481, 482, 483, 485, 489, 524, 534, 535, 536, 539, 562, 582, 583, 594, 604, 614, 629, 638, 672, 679, 680, 688, 734, 796, 797, 798, 802, 804, 805, 809, 811, 814, 816, 819, 822, 823, 826, 829, 833, 834, 836, 841, 842, 946, 947, 948, 950, 954, 994, 1004, 1005, 1006, 1009, 1049], "round": [18, 31, 69, 97, 254, 297, 372, 551, 638, 771, 851, 1049], "corner": [18, 31, 97, 254], "op": [18, 126, 254, 292, 476, 534, 536, 638, 734, 1004, 1006, 1049], "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, 429, 434, 481, 504, 516, 549, 587, 619, 622, 629, 638, 665, 666, 669, 670, 671, 684, 696, 713, 717, 734, 743, 848, 876, 912, 917, 946, 986, 1013, 1049], "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, 431, 434, 437, 463, 492, 502, 516, 534, 535, 536, 556, 619, 622, 629, 638, 684, 707, 734, 737, 744, 759, 774, 775, 776, 786, 876, 880, 914, 957, 972, 986, 1004, 1005, 1006, 1034, 1049, 1057], "semigraph": 18, "box": [18, 133, 254], "draw": [18, 23, 24, 123, 492, 498, 638, 1057], "found": [18, 28, 54, 77, 86, 88, 93, 97, 143, 226, 254, 493, 518, 534, 536, 638, 709, 734, 958, 988, 1004, 1006, 1049, 1056], "unicod": 18, "block": [18, 157, 223, 254, 669, 692, 707, 714, 718, 734, 960, 961, 1049], "http": [18, 31, 91, 103, 132, 138, 254, 515, 985], "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, 737, 960, 961, 1049], "inform": [21, 72, 104, 113, 138, 254, 298, 299, 300, 396, 502, 509, 515, 516, 524, 587, 588, 638, 689, 734, 774, 775, 776, 880, 972, 979, 985, 986, 994, 1049], "separ": [22, 28, 99, 101, 102, 112, 185, 187, 215, 222, 224, 254, 268, 411, 416, 514, 578, 582, 583, 638, 708, 734, 894, 899, 984, 1017, 1026, 1049], "between": [22, 74, 121, 122, 124, 126, 138, 189, 246, 254, 293, 313, 383, 409, 415, 416, 430, 465, 470, 471, 486, 492, 498, 508, 570, 571, 579, 581, 613, 616, 617, 638, 690, 728, 734, 766, 788, 860, 892, 898, 899, 913, 932, 936, 937, 951, 978, 1049], "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, 429, 464, 473, 482, 494, 531, 606, 619, 638, 665, 669, 670, 671, 734, 774, 775, 780, 786, 848, 912, 938, 947, 962, 1001, 1049], "both": [23, 28, 58, 158, 159, 172, 173, 180, 195, 254, 267, 383, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 648, 670, 671, 675, 676, 685, 734, 860, 869, 1049], "tbl_row": 23, "char": [24, 58, 75, 516, 522, 986, 992], "enabl": [25, 75, 129, 200, 231, 254, 494, 638, 695, 712, 734, 962, 1049], "addit": [25, 30, 31, 93, 104, 113, 122, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 261, 324, 363, 366, 464, 505, 509, 515, 516, 524, 563, 565, 572, 575, 576, 577, 578, 584, 592, 605, 609, 618, 621, 622, 638, 658, 662, 669, 695, 696, 701, 708, 712, 716, 734, 792, 800, 846, 979, 985, 986, 994, 1049], "verbos": [25, 130, 516, 986], "debug": [25, 656, 663, 680, 734, 1057], "log": [25, 69, 291, 357, 434, 456, 467, 554, 638, 765, 838, 1033, 1049], "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, 439, 470, 480, 638, 692, 694, 734, 919, 1049], "show": [26, 31, 56, 142, 156, 174, 184, 254, 659, 689, 699, 734], "variabl": [26, 49, 54, 125, 128, 179, 215, 254, 684, 734, 1026, 1049, 1056], "restrict": [26, 531, 587, 588, 1001], "dictionari": [26, 31, 90, 92, 93, 94, 96, 101, 102, 107, 108, 109, 111, 112, 170, 185, 195, 196, 197, 213, 214, 254, 439, 638, 734, 919, 1049], "those": [26, 31, 101, 197, 254, 473, 515, 638, 737, 938, 985, 1049], "been": [26, 31, 254, 473, 482, 483, 484, 485, 486, 488, 489, 490, 569, 638, 938, 1049], "set_fmt_float": 26, "directli": [26, 54, 124, 126, 130, 197, 254, 360, 361, 362, 615, 638, 734, 840, 841, 842, 1049, 1057], "via": [26, 101, 102, 105, 112, 114, 115, 116, 170, 196, 254, 268, 638], "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, 734], "avrocompress": [27, 254], "uncompress": [27, 32, 35, 48, 106, 114, 254, 734], "write": [27, 28, 29, 30, 31, 32, 33, 35, 48, 102, 106, 130, 254, 298, 299, 300, 452, 638, 678, 699, 715, 734, 774, 775, 776, 1049], "apach": [27, 35, 100, 103, 254], "avro": [27, 100, 254, 649], "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, 410, 470, 481, 482, 483, 484, 485, 486, 488, 489, 490, 493, 502, 576, 592, 599, 603, 616, 617, 629, 638, 652, 658, 670, 671, 675, 676, 680, 699, 701, 706, 708, 715, 734, 737, 744, 769, 784, 785, 821, 825, 832, 835, 893, 936, 946, 947, 948, 949, 950, 951, 953, 954, 955, 958, 972, 1049], "snappi": [27, 35, 48, 254, 734], "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, 482, 483, 485, 488, 489, 490, 570, 571, 587, 588, 590, 626, 627, 638, 649, 670, 676, 680, 734, 778, 792, 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, 864, 868, 870, 871, 876, 946, 970, 1022, 1049, 1057], "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, 412, 413, 417, 418, 477, 495, 496, 508, 510, 512, 514, 516, 521, 527, 529, 531, 533, 538, 541, 548, 550, 568, 574, 576, 579, 580, 581, 593, 597, 598, 602, 605, 607, 608, 609, 611, 613, 618, 620, 624, 628, 629, 638, 657, 658, 659, 660, 666, 673, 674, 675, 687, 692, 694, 695, 707, 708, 711, 713, 715, 734, 737, 768, 774, 821, 899, 942, 980, 982, 984, 986, 1001, 1008, 1011, 1049], "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, 418, 502, 512, 514, 529, 531, 568, 574, 576, 579, 580, 581, 593, 598, 602, 605, 607, 608, 609, 611, 618, 620, 624, 628, 629, 638, 657, 658, 659, 660, 666, 673, 674, 675, 687, 692, 694, 695, 707, 708, 711, 715, 734, 737, 768, 899, 972, 982, 984, 1001, 1049], "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, 576, 657, 658, 659, 660, 666, 675, 687, 692, 694, 695, 707, 713, 734, 768], "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, 466, 497, 510, 516, 530, 534, 535, 536, 575, 596, 638, 670, 675, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 801, 828, 831, 860, 980, 986, 1000, 1004, 1005, 1006, 1049], "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, 432, 439, 482, 483, 484, 485, 486, 488, 489, 490, 497, 502, 592, 629, 638, 670, 671, 676, 678, 692, 734, 737, 821, 825, 832, 838, 860, 959, 972, 1030, 1049], "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, 734], "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, 649, 734], "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, 429, 437, 464, 470, 477, 481, 482, 483, 484, 485, 486, 488, 489, 490, 494, 496, 529, 530, 536, 555, 569, 582, 583, 587, 588, 604, 612, 615, 616, 617, 630, 638, 653, 670, 671, 675, 680, 689, 698, 715, 716, 734, 743, 744, 828, 831, 840, 841, 842, 845, 880, 936, 942, 946, 947, 948, 949, 950, 951, 953, 954, 955, 962, 965, 999, 1000, 1006, 1030, 1049, 1056, 1057], "If": [28, 29, 30, 31, 32, 33, 34, 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, 424, 429, 437, 439, 450, 464, 470, 473, 475, 477, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 492, 493, 498, 502, 503, 517, 519, 521, 522, 527, 528, 529, 530, 531, 533, 534, 535, 536, 539, 563, 565, 567, 569, 573, 579, 580, 582, 584, 587, 588, 594, 600, 601, 603, 605, 609, 612, 615, 616, 617, 621, 622, 626, 627, 629, 630, 638, 652, 659, 661, 670, 671, 675, 676, 680, 684, 692, 700, 706, 707, 709, 715, 718, 734, 737, 743, 744, 774, 775, 776, 785, 794, 817, 818, 832, 845, 853, 855, 856, 879, 880, 882, 907, 912, 936, 938, 940, 942, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 957, 958, 962, 968, 972, 973, 987, 989, 991, 992, 997, 998, 999, 1000, 1001, 1003, 1004, 1005, 1006, 1009, 1019, 1025, 1030, 1039, 1049, 1056], "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, 470, 482, 483, 484, 485, 486, 488, 489, 490, 519, 521, 527, 533, 563, 565, 569, 573, 584, 587, 588, 600, 601, 605, 609, 612, 615, 621, 622, 626, 627, 630, 638, 664, 670, 671, 676, 695, 712, 715, 734, 737, 753, 785, 821, 825, 832, 936, 960, 961, 989, 991, 997, 1003, 1031, 1049, 1056], "whether": [28, 94, 96, 126, 134, 201, 221, 254, 310, 328, 346, 401, 402, 444, 445, 470, 494, 638, 652, 680, 696, 701, 706, 734, 737, 785, 804, 826, 884, 885, 936, 1049, 1056], "header": [28, 31, 35, 48, 97, 101, 102, 105, 112, 143, 187, 222, 254, 734], "field": [28, 59, 86, 88, 93, 217, 224, 254, 429, 439, 479, 516, 517, 530, 531, 544, 582, 583, 604, 621, 638, 708, 734, 785, 912, 936, 944, 998, 1000, 1001, 1015, 1017, 1049], "symbol": [28, 254], "byte": [28, 48, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 144, 254, 286, 289, 290, 519, 522, 734, 760, 763, 764, 839, 989, 992, 1049], "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, 439, 464, 476, 482, 483, 484, 485, 486, 488, 489, 490, 505, 520, 526, 563, 565, 572, 575, 576, 577, 578, 584, 587, 588, 592, 605, 609, 618, 621, 622, 626, 627, 638, 652, 658, 662, 665, 669, 670, 671, 675, 695, 696, 701, 706, 708, 712, 716, 734, 839, 840, 841, 842, 848, 990, 996, 1049], "defin": [28, 31, 38, 121, 122, 124, 133, 158, 159, 183, 186, 236, 254, 268, 383, 428, 466, 480, 482, 483, 484, 485, 486, 488, 489, 490, 567, 587, 588, 603, 621, 626, 627, 638, 670, 671, 688, 718, 734, 737, 744, 860, 911, 945, 1049], "chrono": [28, 254, 348, 351, 534, 535, 536, 539, 828, 831, 1004, 1005, 1006, 1009], "rust": [28, 35, 83, 106, 110, 133, 236, 254, 744, 1049], "crate": [28, 254, 509, 510, 515, 516, 524, 525, 534, 535, 536, 539, 737, 979, 980, 985, 986, 994, 995, 1004, 1005, 1006, 1009], "fraction": [28, 119, 120, 198, 254, 346, 465, 492, 534, 536, 638, 826, 932, 957, 1004, 1006, 1049], "second": [28, 123, 158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 534, 536, 589, 590, 625, 629, 638, 670, 671, 676, 734, 737, 821, 825, 832, 1004, 1006, 1057], "precis": [28, 31, 38, 39, 170, 196, 197, 214, 254, 317, 537, 737, 793, 1007], "infer": [28, 90, 92, 93, 94, 95, 96, 101, 102, 105, 108, 109, 112, 115, 133, 254, 477, 517, 534, 535, 536, 537, 539, 615, 638, 734, 942, 987, 1004, 1005, 1006, 1007, 1009, 1049], "maximum": [28, 101, 102, 112, 122, 123, 124, 126, 176, 254, 403, 440, 458, 473, 605, 606, 638, 681, 734, 774, 806, 886, 920, 928, 933, 938, 1049], "timeunit": [28, 38, 40, 254, 317, 318, 350, 355, 536, 587, 588, 737, 793, 794, 830, 835, 1006], "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, 654, 685, 688, 734, 737, 773, 1049, 1056, 1057], "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, 482, 483, 484, 485, 486, 488, 489, 490, 534, 536, 553, 587, 588, 590, 596, 603, 626, 627, 638, 670, 671, 676, 734, 737, 791, 792, 793, 794, 795, 796, 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, 876, 889, 890, 928, 929, 961, 1004, 1006, 1032, 1049], "place": [28, 134, 141, 146, 163, 164, 187, 192, 197, 203, 204, 207, 221, 229, 254, 278, 416, 423, 495, 496, 504, 638, 652, 697, 698, 701, 706, 734, 743, 753, 845, 899, 906, 939, 941, 964, 965, 974, 1049], "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, 472, 500, 501, 551, 552, 576, 592, 612, 615, 630, 638, 660, 694, 712, 734, 737, 826, 1031, 1036, 1049], "repres": [28, 50, 65, 90, 92, 94, 95, 96, 208, 228, 233, 254, 389, 391, 507, 560, 563, 576, 579, 592, 603, 616, 617, 620, 628, 638, 702, 710, 734, 961, 976, 1038, 1049], "empti": [28, 81, 93, 101, 102, 105, 112, 135, 136, 158, 167, 179, 254, 603, 618, 654, 655, 670, 684, 734, 737, 773, 777, 863, 1049], "table_nam": [29, 31, 254], "connect": [29, 101, 103, 106, 110, 114, 116, 117, 254, 650], "if_exist": [29, 254], "dbwritemod": [29, 254], "fail": [29, 30, 91, 104, 106, 109, 113, 132, 223, 254, 279, 349, 429, 534, 535, 536, 539, 638, 707, 734, 744, 1004, 1005, 1006, 1009, 1049], "dbwriteengin": [29, 254], "sqlalchemi": [29, 254], "databas": [29, 103, 254, 649], "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, 429, 474, 482, 483, 485, 488, 489, 490, 528, 559, 577, 586, 587, 588, 589, 590, 625, 626, 627, 638, 654, 655, 661, 670, 671, 712, 734, 773, 777, 790, 794, 825, 832, 930, 939, 998, 1030, 1049, 1056, 1057], "append": [29, 30, 124, 146, 172, 173, 254, 310, 470, 474, 587, 588, 629, 638, 675, 676, 734, 845, 1049], "your": [29, 31, 67, 101, 102, 119, 120, 133, 170, 196, 197, 200, 214, 231, 234, 236, 254, 268, 534, 535, 536, 567, 638, 656, 672, 679, 680, 695, 712, 716, 718, 734, 744, 1004, 1005, 1006, 1049, 1057], "special": [29, 101, 102, 112, 254, 516, 744, 986, 1049], "uri": [29, 30, 103, 104, 113, 254], "postgresql": [29, 103, 254, 464, 638], "user": [29, 103, 133, 186, 236, 254, 268, 437, 466, 494, 567, 587, 638, 688, 718, 734, 744, 962, 1049], "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, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 521, 527, 533, 563, 565, 572, 576, 584, 587, 596, 605, 609, 621, 622, 638, 652, 658, 669, 670, 671, 673, 680, 688, 695, 699, 701, 706, 712, 716, 718, 734, 744, 801, 824, 846, 855, 882, 991, 997, 1003, 1019, 1049], "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, 439, 525, 569, 638, 664, 712, 734, 794, 824, 919, 960, 961, 995, 1049], "insert": [29, 101, 102, 106, 110, 112, 114, 115, 116, 164, 192, 222, 224, 254, 493, 508, 542, 638, 708, 734, 958, 978, 1012, 1049], "mode": [29, 30, 52, 254, 516, 612, 615, 630, 638, 734, 986, 1049, 1056], "new": [29, 30, 31, 112, 130, 133, 142, 163, 164, 183, 184, 191, 192, 211, 222, 224, 225, 231, 254, 263, 318, 365, 382, 438, 524, 525, 530, 531, 543, 544, 550, 569, 638, 649, 659, 692, 705, 708, 712, 718, 734, 740, 790, 794, 844, 930, 941, 994, 995, 1000, 1001, 1013, 1015, 1021, 1027, 1049, 1056], "alreadi": [29, 30, 254, 309, 410, 638, 784, 893, 1049], "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, 458, 459, 638, 787, 824, 840, 841, 842, 891, 928, 929, 1049], "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, 488, 490, 507, 560, 579, 616, 617, 620, 628, 638, 649, 702, 710, 734, 953, 955, 976, 1038, 1049], "like": [30, 91, 100, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 166, 168, 172, 217, 254, 316, 410, 451, 473, 515, 587, 588, 622, 626, 627, 638, 663, 691, 734, 743, 792, 893, 938, 985, 1049], "categor": [30, 58, 75, 172, 215, 216, 254, 294, 295, 439, 553, 638, 737, 767, 768, 769, 785, 936, 1032, 1049], "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, 587, 588, 603, 626, 627, 649, 670, 671, 691, 734, 737, 767, 791, 883, 977, 1025, 1049], "handl": [30, 74, 97, 101, 102, 112, 117, 254, 312, 408, 542, 638, 787, 891, 1012, 1049], "throw": [30, 91, 254, 293, 517, 518, 638, 766, 987, 988, 1049], "add": [30, 31, 102, 133, 146, 158, 231, 232, 254, 468, 546, 590, 594, 629, 638, 670, 675, 712, 713, 714, 734, 743, 845, 1049], "anyth": [30, 195, 254, 292, 516, 638, 986], "updat": [30, 254, 734], "extra": [30, 35, 48, 101, 104, 105, 106, 110, 113, 114, 116, 146, 158, 254, 670, 734, 743, 845, 1049], "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, 437, 502, 509, 515, 516, 524, 587, 588, 614, 638, 737, 774, 775, 776, 880, 972, 979, 985, 986, 994, 1049, 1057], "here": [30, 31, 90, 92, 93, 94, 96, 103, 104, 108, 109, 113, 122, 124, 126, 254, 518, 734, 988], "gc": [30, 104, 113, 254], "azur": [30, 104, 113, 254], "keyword": [30, 55, 104, 110, 113, 138, 186, 195, 200, 231, 234, 254, 466, 618, 621, 638, 688, 695, 712, 716, 734, 1049], "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, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 521, 527, 533, 563, 565, 570, 571, 572, 575, 576, 577, 578, 584, 587, 592, 605, 609, 615, 618, 621, 622, 626, 638, 658, 662, 669, 670, 671, 688, 695, 696, 701, 708, 712, 716, 734, 743, 825, 832, 860, 991, 997, 1003, 1031, 1049], "while": [30, 102, 104, 105, 113, 124, 126, 170, 179, 222, 254, 684, 734], "lake": [30, 104, 113, 254, 649], "instanti": [30, 31, 200, 231, 254, 695, 712, 734], "basic": [30, 31, 254, 1057], "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, 429, 439, 557, 587, 593, 602, 638, 663, 675, 680, 707, 712, 734, 743, 835, 845, 853, 919, 967, 1039, 1049], "match": [30, 31, 38, 74, 84, 90, 92, 93, 94, 96, 108, 109, 119, 120, 148, 173, 195, 254, 445, 487, 509, 510, 513, 515, 516, 517, 518, 524, 525, 532, 534, 535, 536, 576, 638, 665, 676, 734, 737, 869, 876, 952, 979, 980, 983, 985, 986, 987, 988, 994, 995, 1002, 1004, 1005, 1006, 1049], "version": [30, 72, 104, 113, 118, 254, 292, 337, 338, 534, 536, 569, 587, 614, 615, 626, 638, 743, 817, 818, 1004, 1006, 1049], "old": [30, 191, 254, 692, 734], "existing_table_path": [30, 254], "store": [30, 101, 110, 146, 170, 196, 254, 294, 743, 768, 845, 1049], "bucket": [30, 104, 113, 254, 345, 352, 825, 832, 856, 1049], "prefix": [30, 130, 254, 263, 290, 438, 532, 542, 546, 638, 737, 764, 1002, 1012], "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, 464, 470, 505, 523, 563, 565, 570, 571, 572, 575, 576, 577, 578, 584, 592, 605, 609, 618, 621, 622, 638, 658, 662, 669, 695, 696, 701, 708, 712, 716, 734, 840, 841, 842, 936, 993, 1049], "tupl": [31, 103, 133, 170, 195, 196, 197, 202, 233, 254, 477, 638, 689, 699, 734, 737, 942, 1049], "a1": [31, 68, 70, 254], "table_styl": [31, 254], "column_format": [31, 254], "dtype_format": [31, 254], "oneormoredatatyp": [31, 122, 254, 876, 1049], "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, 429, 437, 466, 505, 544, 567, 572, 582, 583, 594, 596, 604, 614, 619, 638, 652, 662, 675, 676, 688, 701, 706, 707, 708, 709, 734, 743, 786, 788, 845, 912, 961, 1015, 1049], "formula": [31, 254, 357, 638, 838, 1049], "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, 649], "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, 309, 448, 482, 483, 484, 485, 486, 488, 489, 490, 554, 567, 569, 638, 670, 671, 718, 734, 765, 773, 784, 786, 853, 866, 1033, 1049], "close": [31, 158, 159, 254, 383, 434, 482, 483, 484, 485, 486, 488, 489, 490, 502, 587, 588, 626, 627, 638, 670, 671, 734, 860, 972, 1049], "xlsx": [31, 105, 254], "work": [31, 39, 102, 105, 192, 254, 268, 284, 297, 298, 299, 300, 363, 372, 410, 431, 464, 480, 522, 556, 638, 759, 771, 774, 775, 776, 851, 893, 914, 992, 1034, 1049], "directori": [31, 110, 254], "sheet1": [31, 254], "valid": [31, 38, 52, 106, 110, 126, 130, 144, 172, 254, 309, 509, 510, 515, 516, 518, 524, 525, 587, 588, 638, 675, 734, 737, 784, 839, 853, 979, 980, 985, 986, 988, 994, 995, 1049], "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, 463, 470, 475, 482, 483, 484, 485, 486, 487, 488, 489, 490, 523, 562, 569, 596, 600, 601, 615, 638, 670, 671, 734, 737, 771, 826, 851, 869, 936, 940, 952, 961, 993, 1049, 1056, 1057], "medium": [31, 254], "kei": [31, 67, 72, 74, 158, 170, 172, 173, 180, 185, 187, 191, 194, 196, 197, 254, 621, 670, 675, 676, 685, 692, 693, 734], "follow": [31, 72, 101, 102, 104, 112, 113, 133, 158, 159, 173, 186, 227, 254, 268, 341, 345, 352, 466, 473, 482, 483, 484, 485, 486, 487, 488, 489, 490, 544, 555, 567, 587, 629, 631, 632, 634, 638, 639, 640, 644, 645, 646, 670, 671, 676, 688, 734, 821, 825, 832, 938, 960, 961, 1041, 1043, 1045, 1049, 1050, 1053, 1054, 1055, 1057], "first_column": [31, 254], "last_column": [31, 254], "banded_column": [31, 254], "banded_row": [31, 254], "sheet": [31, 105, 254], "chart": [31, 254, 689, 734], "subsequ": [31, 57, 190, 218, 254, 429, 629, 661, 734], "colnam": [31, 112, 124, 143, 254, 660, 734], "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, 423, 429, 432, 464, 466, 470, 473, 476, 477, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 493, 495, 496, 516, 534, 536, 544, 567, 592, 604, 615, 616, 617, 638, 652, 662, 670, 671, 680, 688, 697, 698, 701, 706, 709, 718, 734, 737, 744, 785, 792, 795, 828, 830, 831, 838, 839, 856, 860, 879, 889, 906, 912, 915, 918, 930, 936, 938, 942, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 958, 964, 965, 986, 1004, 1006, 1036, 1040, 1049, 1056, 1057], "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, 482, 483, 485, 488, 489, 490, 534, 536, 539, 587, 590, 626, 627, 638, 670, 676, 734, 737, 792, 793, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 829, 830, 832, 833, 834, 835, 836, 1004, 1006, 1009], "overridden": [31, 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485, 489, 561, 563, 582, 584, 585, 594, 614, 623, 638, 656, 661, 663, 669, 670, 671, 687, 689, 699, 716, 734, 737, 782, 783, 838, 839, 947, 948, 950, 953, 954, 955, 1049], "must": [31, 91, 92, 104, 113, 139, 145, 158, 159, 173, 180, 195, 254, 310, 429, 437, 470, 482, 483, 484, 485, 486, 488, 489, 490, 638, 662, 670, 671, 676, 680, 685, 734, 785, 786, 936, 1049], "funcnam": [31, 254], "averag": [31, 254, 360, 361, 362, 473, 638, 840, 841, 842, 938, 1049], "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, 457, 462, 482, 483, 484, 485, 486, 488, 489, 490, 510, 557, 559, 568, 611, 638, 670, 671, 676, 687, 709, 714, 718, 727, 734, 786, 821, 856, 890, 926, 931, 980, 1035, 1037, 1049], "std_dev": [31, 254], "var": [31, 127, 254, 490, 638, 734, 1049], "pixel": [31, 254], "unit": [31, 38, 40, 124, 126, 144, 254, 317, 318, 325, 350, 355, 435, 448, 534, 536, 558, 587, 588, 596, 638, 689, 734, 737, 793, 794, 801, 830, 835, 839, 918, 1004, 1006, 1036, 1049, 1057], "hand": [31, 101, 102, 112, 254, 409, 415, 430, 892, 898, 913], "side": [31, 158, 159, 254, 383, 409, 415, 430, 482, 483, 484, 485, 486, 488, 489, 490, 493, 587, 588, 626, 627, 638, 670, 671, 734, 860, 892, 898, 913, 958, 1049], "call": [31, 56, 102, 124, 126, 130, 133, 157, 158, 159, 174, 253, 254, 268, 305, 308, 395, 468, 546, 605, 609, 638, 650, 669, 670, 671, 733, 734, 737, 744, 1049], "ad": [31, 93, 132, 158, 222, 231, 254, 267, 366, 582, 583, 638, 670, 712, 734, 846, 1049], "end": [31, 101, 102, 110, 112, 158, 254, 286, 289, 290, 316, 341, 345, 346, 363, 383, 424, 509, 513, 516, 528, 532, 569, 576, 587, 588, 592, 600, 601, 626, 627, 629, 638, 670, 672, 679, 689, 734, 737, 763, 792, 821, 825, 860, 907, 983, 986, 998, 1002, 1049], "wise": [31, 67, 152, 254, 270, 271, 272, 273, 274, 275, 301, 302, 364, 433, 499, 500, 501, 551, 552, 582, 583, 594, 605, 609, 614, 638, 745, 746, 747, 748, 749, 750, 778, 779, 843, 916, 917, 969, 970, 971, 1022, 1023, 1049], "particip": [31, 254], "distinct": [31, 126, 185, 254, 284, 431, 473, 590, 638, 759, 914, 938, 1049, 1057], "referenc": [31, 254, 544], "differ": [31, 101, 117, 119, 146, 158, 159, 170, 196, 197, 214, 222, 254, 312, 322, 341, 342, 344, 353, 359, 408, 439, 458, 459, 461, 492, 498, 534, 557, 587, 593, 596, 602, 638, 661, 670, 671, 734, 737, 743, 744, 787, 798, 821, 822, 824, 833, 845, 891, 928, 929, 959, 1004, 1049], "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, 429, 437, 511, 512, 515, 517, 518, 596, 621, 638, 649, 734, 744, 761, 762, 856, 879, 981, 982, 985, 987, 988, 1049, 1056, 1057], "intersect": [31, 254, 737], "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, 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640, 643, 644, 645, 646, 649, 650, 663, 733, 734, 737, 938, 1041, 1043, 1045, 1049, 1050, 1053, 1054, 1055], "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, 481, 638, 737, 946, 1049], "strongli": [31, 128, 133, 195, 236, 254, 268, 638, 744, 1049], "advis": [31, 174, 254], "structur": [31, 81, 85, 87, 146, 186, 197, 217, 254, 466, 638, 688, 734, 743, 845, 1049], "wherev": [31, 133, 236, 254, 268, 638, 744, 1049], "possibl": [31, 101, 133, 134, 157, 170, 196, 221, 223, 236, 254, 268, 435, 447, 531, 558, 638, 652, 669, 701, 706, 707, 734, 744, 1001, 1049], "simpl": [31, 126, 183, 254], "colx": [31, 57, 254, 737, 1057], "coli": [31, 254, 737, 1057], "after": [31, 57, 74, 93, 100, 101, 102, 106, 110, 112, 114, 115, 116, 146, 224, 253, 254, 363, 439, 464, 473, 542, 638, 708, 734, 743, 845, 919, 938, 1012, 1049], "befor": [31, 101, 112, 128, 130, 146, 158, 173, 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1057], "titl": [31, 52, 254], "explicit": [31, 112, 122, 254, 614], "integr": [31, 254, 1057], "multi_fram": [31, 254], "wb": [31, 254], "coordin": [31, 254], "advanc": [31, 254, 429, 912, 1057], "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, 1057], "white": [31, 254], "w": [31, 54, 55, 158, 197, 254, 515, 564, 566, 670, 734, 737, 985, 997], "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, 465, 472, 489, 497, 537, 583, 586, 587, 589, 604, 638, 670, 671, 672, 676, 679, 699, 704, 734, 737, 744, 785, 792, 794, 816, 825, 832, 956, 1007, 1049, 1057], "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, 480, 524, 525, 557, 559, 638, 718, 945, 1035, 1049, 1057], "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, 466, 482, 483, 485, 596, 603, 626, 638, 685, 688, 734, 737, 787, 832, 1049], "35": [31, 118, 254, 312, 638, 787, 1049], "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, 497, 587, 603, 626, 638, 670, 688, 712, 734, 737, 785, 787, 794, 799, 817, 824, 825, 832, 1049, 1057], "15": [31, 118, 123, 133, 158, 159, 164, 254, 309, 312, 338, 345, 352, 466, 489, 583, 615, 626, 638, 670, 671, 734, 737, 784, 787, 825, 832, 1049], "60": [31, 146, 254, 346, 347, 489, 534, 638, 822, 826, 827, 1004], "q3": [31, 254], "40": [31, 146, 186, 254, 345, 352, 378, 537, 638, 688, 734, 805, 832, 1007], "80": [31, 254], "q4": [31, 254], "75": [31, 139, 254, 265, 463, 482, 483, 484, 485, 488, 489, 490, 638, 786, 856, 936, 1049, 1057], "account": [31, 97, 103, 254, 341, 360, 361, 362, 638, 821, 840, 841, 842, 1049], "flavour": [31, 254], "integer_dtyp": [31, 200, 254, 695, 734, 737], "0_": [31, 254], "just": [31, 112, 179, 254, 684, 734], "unifi": [31, 254, 737], "multi": [31, 101, 102, 254, 363, 515, 605, 609, 638, 985], "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, 438, 463, 468, 476, 516, 534, 536, 546, 564, 566, 585, 606, 610, 615, 623, 638, 675, 684, 700, 734, 737, 769, 848, 986, 1004, 1006, 1049, 1056], "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, 489, 510, 516, 570, 571, 603, 626, 638, 671, 734, 737, 794, 825, 832, 980, 986, 1057], "85": [31, 254, 1057], "font": [31, 254], "consola": [31, 254], "standard": [31, 118, 208, 217, 254, 329, 361, 488, 502, 507, 518, 620, 638, 702, 734, 805, 841, 972, 976, 988, 1049, 1057], "stdev": [31, 254], "ipccompress": [32, 254], "arrow": [32, 47, 76, 90, 103, 106, 114, 170, 196, 197, 212, 214, 254, 734, 1025, 1030, 1049], "ipc": [32, 106, 107, 114, 117, 254, 649], "binari": [32, 254, 286, 288, 289, 290, 760, 763, 764], "feather": [32, 106, 114, 254, 649], "lz4": [32, 35, 47, 48, 254, 734], "zstd": [32, 35, 47, 48, 254, 734], "pretti": [33, 254], "row_ori": [33, 254], "iobas": [33, 34, 108, 109, 254, 452, 691, 715, 734], "serial": [33, 34, 254], "represent": [33, 34, 216, 254, 295, 322, 326, 329, 330, 332, 334, 336, 339, 342, 343, 346, 353, 354, 356, 553, 638, 661, 678, 734, 769, 798, 802, 804, 805, 809, 811, 814, 816, 819, 822, 823, 826, 833, 834, 836, 1028, 1032, 1049], "orient": [33, 68, 70, 94, 96, 254, 734], "slower": [33, 94, 96, 133, 157, 185, 227, 236, 254, 268, 567, 638, 669, 718, 734, 744, 1049], "common": [33, 67, 73, 74, 254, 438, 587, 588, 638, 643, 656, 661, 663, 689, 699, 734], "write_ndjson": [33, 254], "newlin": [34, 109, 115, 254], "delimit": [34, 101, 102, 109, 112, 115, 187, 215, 254, 508, 978, 1026, 1049], "parquetcompress": [35, 254], "compression_level": [35, 48, 254, 734], "statist": [35, 48, 101, 102, 110, 116, 139, 254, 361, 362, 396, 482, 483, 484, 485, 486, 487, 488, 489, 490, 502, 638, 734, 786, 841, 842, 880, 952, 972, 1049], "row_group_s": [35, 48, 254, 734], "use_pyarrow": [35, 48, 101, 106, 110, 254, 734, 1029, 1030, 1049], "pyarrow_opt": [35, 104, 110, 113, 254], "parquet": [35, 48, 110, 111, 116, 254, 649, 734], "gzip": [35, 48, 254, 734], "lzo": [35, 48, 254, 734], "brotli": [35, 48, 254, 734], "choos": [35, 47, 48, 187, 254, 734], "good": [35, 47, 48, 170, 254, 734], "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, 464, 534, 535, 536, 638, 652, 670, 671, 676, 701, 706, 714, 734, 744, 1004, 1005, 1006, 1049], "fast": [35, 47, 48, 125, 127, 254, 366, 494, 638, 734, 846, 962, 1049, 1057], "decompress": [35, 47, 48, 254, 734], "backward": [35, 48, 148, 173, 254, 285, 338, 368, 638, 665, 676, 734, 818, 848, 1049], "guarante": [35, 48, 91, 101, 102, 223, 254, 663, 707, 734], "deal": [35, 48, 170, 254, 344, 352, 473, 534, 638, 734, 824, 832, 938, 1004, 1049], "older": [35, 48, 254, 734], "reader": [35, 48, 99, 101, 102, 106, 110, 254, 650, 734], "higher": [35, 48, 189, 246, 254, 471, 486, 613, 638, 690, 728, 734, 937, 951, 1049], "mean": [35, 48, 101, 102, 106, 110, 112, 139, 148, 157, 158, 159, 173, 187, 227, 234, 254, 341, 345, 352, 365, 368, 482, 483, 484, 485, 486, 488, 489, 490, 502, 515, 569, 574, 587, 638, 665, 669, 670, 671, 676, 680, 716, 734, 786, 821, 825, 832, 844, 848, 853, 948, 972, 985, 1049], "smaller": [35, 48, 144, 254, 663, 734, 839, 1049], "disk": [35, 47, 48, 106, 254, 699, 734], "11": [35, 48, 118, 124, 159, 254, 263, 314, 315, 329, 337, 338, 341, 345, 352, 382, 465, 473, 489, 503, 542, 562, 576, 622, 627, 638, 656, 671, 672, 679, 689, 704, 734, 737, 744, 821, 825, 832, 946, 1049], "22": [35, 48, 123, 254, 322, 342, 345, 352, 354, 355, 482, 483, 485, 488, 489, 490, 534, 576, 638, 734, 737, 825, 832, 835, 1004, 1057], "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, 409, 415, 419, 420, 421, 430, 432, 433, 434, 455, 464, 465, 469, 470, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 499, 500, 501, 502, 506, 551, 552, 563, 564, 565, 566, 570, 571, 572, 579, 581, 584, 591, 605, 609, 616, 617, 622, 638, 669, 673, 675, 676, 707, 713, 716, 728, 734, 739, 745, 746, 747, 748, 749, 750, 756, 757, 758, 770, 778, 779, 780, 781, 782, 783, 788, 838, 839, 843, 880, 892, 898, 902, 903, 904, 913, 915, 916, 917, 924, 932, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 969, 970, 971, 972, 975, 1022, 1023, 1031, 1049], "512": [35, 254, 467, 638, 932, 1049], "implement": [35, 91, 132, 133, 236, 254, 268, 395, 468, 546, 567, 638, 718, 744, 960, 961, 1049], "v": [35, 54, 55, 144, 254, 493, 638, 785, 958, 1049], "At": [35, 254], "moment": [35, 138, 254, 396, 502, 638, 880, 972, 1049], "pyarrow": [35, 90, 95, 101, 103, 104, 106, 110, 113, 117, 118, 171, 212, 217, 218, 254, 650, 1025, 1029, 1030, 1031, 1049], "write_t": [35, 254], "calendar": [37, 38, 158, 159, 173, 227, 254, 329, 341, 345, 352, 356, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 805, 821, 825, 832, 836], "time_unit": [38, 40, 97, 317, 318, 325, 350, 355, 534, 536, 587, 588, 596, 737, 793, 794, 801, 830, 835, 1004, 1006], "time_zon": [38, 97, 319, 344, 352, 536, 587, 588, 737, 792, 795, 800, 824, 832, 1006], "timezon": [38, 737], "m": [38, 40, 55, 172, 254, 316, 317, 318, 324, 325, 348, 350, 351, 355, 515, 534, 535, 536, 539, 587, 588, 596, 675, 734, 737, 792, 793, 794, 800, 801, 828, 830, 831, 835, 985, 1004, 1005, 1006, 1009], "zone": [38, 316, 319, 344, 534, 536, 587, 588, 737, 792, 795, 824, 1004, 1006], "zoneinfo": [38, 737], "run": [38, 47, 48, 73, 125, 127, 133, 157, 174, 187, 223, 236, 254, 268, 309, 410, 479, 480, 618, 638, 656, 661, 663, 669, 680, 684, 689, 699, 707, 734, 737, 744, 784, 893, 944, 945, 1049, 1056, 1057], "available_timezon": [38, 737], "check": [38, 101, 102, 112, 119, 120, 153, 158, 159, 167, 169, 172, 254, 264, 266, 286, 289, 290, 383, 387, 406, 509, 513, 532, 638, 670, 671, 675, 680, 734, 741, 742, 760, 763, 764, 861, 863, 866, 867, 869, 874, 875, 876, 878, 879, 889, 959, 979, 983, 1002, 1049], "128": [39, 69, 932, 1049], "bit": [39, 41, 42, 43, 44, 45, 46, 61, 62, 63, 64, 475, 509, 638, 940, 979, 1049], "neg": [39, 158, 159, 161, 175, 203, 204, 206, 210, 254, 423, 424, 466, 495, 496, 503, 528, 638, 670, 671, 697, 698, 700, 714, 734, 855, 882, 906, 907, 964, 965, 973, 998, 1019, 1049], "scale": [39, 144, 254, 466, 537, 638, 839, 1007, 1049], "experiment": [39, 117, 200, 225, 226, 231, 254, 309, 345, 482, 483, 484, 485, 486, 488, 489, 490, 638, 695, 709, 712, 734, 784, 825, 856, 936, 1049], "progress": 39, "expect": [39, 82, 84, 89, 268, 567, 603, 638, 678, 680, 734], "32": [41, 44, 62, 69, 159, 169, 254, 456, 497, 638, 671, 734, 788, 822, 932, 951, 1049], "sign": [43, 44, 45, 46, 341, 475, 542, 638, 821, 869, 940, 1012, 1049], "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, 431, 549, 556, 580, 638, 652, 656, 661, 663, 669, 689, 699, 701, 706, 707, 717, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 759, 914, 1034, 1049], "type_coercion": [47, 48, 73, 656, 661, 663, 689, 699, 734], "predicate_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "projection_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "simplify_express": [47, 48, 73, 656, 661, 663, 689, 699, 734], "no_optim": [47, 48, 73, 656, 663, 680, 689, 734], "slice_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "persist": [47, 48, 57, 734], "larger": [47, 48, 579, 734], "ram": [47, 48, 734], "maintain": [47, 48, 134, 221, 254, 284, 431, 493, 556, 638, 652, 701, 706, 734, 759, 914, 958, 1034, 1049], "slightli": [47, 48, 734], "faster": [47, 48, 146, 217, 225, 254, 268, 481, 522, 557, 638, 734, 743, 744, 845, 946, 992, 1049], "coercion": [47, 48, 73, 476, 638, 656, 661, 663, 689, 699, 734], "optim": [47, 48, 73, 110, 112, 114, 115, 116, 170, 174, 186, 190, 196, 223, 254, 656, 661, 663, 680, 689, 699, 707, 714, 718, 734, 770, 1049], "predic": [47, 48, 73, 112, 114, 115, 116, 117, 149, 169, 195, 254, 369, 561, 594, 638, 656, 661, 663, 666, 680, 689, 692, 699, 714, 734, 849, 960, 961, 1049], "pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 692, 699, 714, 734, 960, 961, 1049], "project": [47, 48, 73, 112, 114, 115, 116, 268, 504, 505, 638, 656, 661, 663, 680, 689, 692, 699, 715, 734], "turn": [47, 48, 73, 101, 102, 112, 540, 559, 638, 656, 661, 663, 680, 689, 734, 1010], "off": [47, 48, 73, 101, 102, 112, 559, 638, 656, 661, 663, 680, 689, 734], "certain": [47, 48, 80, 104, 113, 164, 227, 254, 576, 656, 689, 734, 1049], "slice": [47, 48, 68, 73, 144, 161, 171, 210, 254, 414, 427, 481, 482, 483, 484, 485, 486, 488, 489, 490, 638, 656, 661, 663, 680, 689, 699, 734, 839, 855, 897, 910, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1019, 1049], "lf": [47, 48, 652, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 669, 670, 672, 673, 674, 675, 677, 678, 679, 680, 681, 682, 683, 684, 686, 687, 688, 689, 690, 692, 693, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 710, 711, 712, 713, 714, 715, 734, 1056, 1057], "scan_csv": [47, 48, 101, 102, 734], "my_larger_than_ram_fil": [47, 48, 734], "data_pagesize_limit": [48, 734], "reduc": [48, 101, 102, 110, 112, 114, 115, 116, 241, 242, 244, 247, 497, 594, 638, 723, 724, 726, 729, 734, 921, 935, 966, 1018, 1049], "pressur": [48, 110, 115, 116, 497, 638, 734, 966, 1049], "improv": [48, 106, 114, 734], "speed": [48, 268, 638, 734], "minimum": [48, 122, 123, 124, 126, 152, 181, 254, 360, 361, 362, 453, 459, 473, 609, 610, 638, 686, 734, 774, 813, 840, 841, 842, 929, 934, 938, 1049, 1057], "limit": [48, 103, 112, 148, 254, 285, 298, 299, 300, 368, 374, 638, 665, 672, 734, 774, 775, 776, 848, 1049], "page": [48, 106, 110, 114, 116, 254, 638, 649, 734, 1049], "arrai": [49, 90, 94, 101, 102, 110, 144, 212, 217, 218, 254, 281, 282, 283, 284, 298, 299, 300, 304, 305, 306, 307, 308, 405, 418, 419, 420, 421, 422, 425, 426, 433, 482, 483, 485, 489, 494, 591, 638, 734, 756, 757, 758, 759, 774, 775, 776, 780, 781, 782, 783, 788, 839, 888, 901, 902, 903, 904, 905, 908, 909, 916, 917, 947, 948, 950, 953, 954, 955, 962, 967, 1025, 1030, 1031, 1039, 1049], "integer_list": 49, "float_list": 49, "wrap": [51, 97, 383, 638], "arbitrari": [51, 124, 737], "python": [51, 101, 102, 118, 133, 170, 196, 197, 214, 236, 254, 268, 318, 437, 567, 587, 588, 626, 627, 629, 638, 643, 718, 744, 794, 806, 807, 808, 813, 1029, 1049], "queri": [52, 54, 73, 103, 106, 112, 114, 115, 116, 146, 174, 186, 254, 268, 437, 439, 518, 638, 656, 661, 663, 672, 679, 689, 699, 714, 718, 734, 743, 744, 845, 988, 1049, 1056], "eager": [52, 53, 54, 55, 56, 57, 158, 225, 254, 310, 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, 470, 482, 483, 485, 488, 489, 490, 569, 573, 587, 588, 596, 600, 601, 612, 615, 621, 626, 627, 630, 637, 638, 670, 734, 792, 793, 795, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 830, 832, 833, 834, 835, 836, 1056], "pars": [52, 101, 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456, 460, 461, 463, 467, 468, 470, 476, 480, 499, 502, 530, 545, 546, 553, 555, 562, 564, 566, 567, 570, 571, 582, 583, 585, 594, 603, 604, 606, 610, 614, 623, 638, 675, 680, 684, 700, 734, 737, 744, 848, 878, 969, 972, 1000, 1032, 1049, 1056], "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, 292, 309, 316, 324, 344, 357, 360, 361, 362, 410, 464, 466, 470, 515, 521, 534, 536, 567, 629, 638, 670, 688, 707, 734, 737, 744, 784, 824, 838, 840, 841, 842, 893, 936, 959, 985, 991, 997, 1004, 1006, 1030, 1039, 1049], "join": [54, 58, 67, 73, 74, 75, 173, 226, 254, 382, 464, 638, 656, 661, 663, 676, 689, 699, 709, 713, 734], "named_fram": [55, 1056], "lf1": [55, 57], "o": [55, 106, 114, 291, 309, 514, 554, 638, 765, 784, 984, 1003, 1033, 1049], "lf2": [55, 57, 734], "p": [55, 69, 186, 254, 466, 621, 638, 688, 734], "q": [55, 197, 254, 310, 470, 621, 638, 936, 1049], "r": [55, 466, 510, 514, 515, 516, 524, 638, 737, 980, 984, 985, 986, 994], "lf3": [55, 734], "lf4": [55, 734], "either": [55, 124, 159, 169, 174, 177, 185, 195, 209, 217, 254, 470, 534, 563, 572, 621, 638, 671, 678, 734, 737, 936, 1004, 1049], "tbl1": [55, 57], "tbl2": [55, 57], "tbl3": 55, "tbl4": 55, "statement": [56, 629], "hello_world": 56, "baz": [56, 164, 165, 187, 224, 254, 529, 531, 674, 708, 734, 737, 1001], "hello_data": 56, "foo_bar": [56, 603], "registr": [57, 649], "lifetim": [57, 130, 648], "context": [57, 58, 128, 183, 237, 254, 262, 268, 303, 369, 410, 447, 504, 505, 559, 567, 580, 593, 596, 602, 615, 618, 637, 638, 648, 649, 713, 719, 734, 893, 1056], "manag": [57, 58, 648, 649, 1056], "often": [57, 130, 158, 159, 254, 407, 476, 638, 670, 671, 734, 890], "want": [57, 93, 133, 146, 183, 254, 268, 298, 299, 300, 352, 369, 437, 439, 480, 482, 483, 484, 485, 486, 488, 489, 490, 582, 594, 612, 615, 630, 638, 656, 672, 679, 734, 737, 743, 744, 774, 775, 776, 832, 845, 945, 1030, 1049], "df0": [57, 180, 254, 685, 734], "exit": [57, 58, 130, 1056], "construct": [57, 90, 92, 93, 94, 95, 96, 254, 375, 439, 612, 615, 630, 638, 668, 691, 734, 1049], "through": [57, 737, 1049], "tbl0": 57, "remain": [57, 101, 102, 112, 144, 254, 530, 531, 569, 680, 734, 839, 1000, 1001, 1049], "text": [57, 522, 524, 525, 619, 992, 1057], "misc": 57, "testing1234": 57, "test1": 57, "test2": 57, "test3": 57, "temporarili": [58, 128, 130, 158, 159, 254, 670, 671, 734], "cach": [58, 73, 75, 106, 112, 114, 116, 129, 439, 482, 483, 484, 485, 486, 488, 489, 490, 534, 535, 536, 539, 638, 648, 656, 661, 663, 689, 699, 734, 1004, 1005, 1006, 1009], "categori": [58, 75, 215, 254, 294, 295, 310, 470, 638, 768, 769, 785, 856, 936, 1049], "until": [58, 174, 254, 587], "finish": [58, 78, 146, 254, 743, 845, 1049], "invalid": [58, 101, 102, 112, 517, 518, 523, 555, 587, 588, 638, 987, 988, 993], "outermost": 58, "color": [58, 75, 236, 286, 288, 289, 290, 718], "red": [58, 75, 236, 718], "green": [58, 75, 236, 718], "blue": [58, 75, 286, 288, 289, 290], "orang": [58, 75, 137, 237, 238, 240, 241, 242, 244, 246, 247, 254, 719, 720, 722, 723, 724, 726, 728, 729], "uint8": [58, 75, 121, 123, 216, 217, 254, 307, 308, 439, 547, 562, 638, 737, 782, 783, 1018, 1049, 1057], "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, 470, 509, 538, 541, 553, 578, 634, 638, 737, 785, 856, 936, 979, 1008, 1011, 1045, 1049], "u8": [58, 75, 215, 216, 254, 439, 562, 638, 737, 1026, 1049, 1057], "composit": [59, 123, 1057], "schemadict": [59, 90, 92, 93, 94, 95, 96, 112, 199, 254, 621, 680, 694, 718, 734], "struct_seri": [59, 718], "dai": [60, 158, 159, 171, 173, 227, 254, 325, 329, 336, 337, 338, 341, 342, 343, 345, 350, 352, 353, 354, 356, 482, 483, 484, 485, 486, 488, 489, 490, 586, 587, 589, 590, 638, 670, 671, 676, 734, 737, 817, 818, 821, 822, 825, 832, 834], "unsign": [61, 62, 63, 64, 475, 638, 869, 940, 1049], "could": [65, 78, 142, 158, 254, 293, 582, 594, 638, 659, 670, 734, 766, 1049], "static": [65, 718], "utf": 66, "frametyp": [67, 1056], "joinstrategi": [67, 172, 254, 675, 734], "outer": [67, 74, 172, 254, 675, 734], "descend": [67, 134, 201, 207, 221, 254, 278, 425, 473, 494, 504, 505, 572, 638, 652, 696, 701, 706, 734, 753, 875, 908, 938, 962, 974, 1049], "fill": [67, 74, 135, 147, 148, 204, 225, 254, 285, 305, 308, 367, 368, 374, 382, 482, 483, 485, 489, 496, 520, 526, 542, 595, 612, 615, 630, 638, 664, 665, 698, 713, 734, 847, 848, 859, 930, 947, 948, 950, 953, 954, 955, 965, 990, 996, 1012, 1049], "sort": [67, 68, 119, 123, 134, 158, 159, 173, 180, 186, 187, 201, 221, 227, 239, 248, 254, 278, 295, 369, 464, 494, 505, 559, 561, 572, 638, 652, 661, 670, 671, 676, 685, 688, 689, 696, 699, 706, 721, 730, 734, 737, 753, 769, 875, 962, 1034, 1037, 1049, 1057], "origin": [67, 101, 102, 223, 254, 344, 395, 439, 464, 475, 476, 477, 510, 515, 516, 518, 520, 526, 542, 570, 571, 638, 707, 734, 785, 824, 919, 936, 942, 980, 985, 986, 988, 990, 996, 1012, 1049], "In": [67, 104, 113, 116, 124, 126, 130, 133, 144, 146, 158, 159, 183, 217, 254, 268, 587, 638, 670, 671, 734, 743, 839, 845, 939, 1049], "duplic": [67, 79, 166, 172, 173, 223, 254, 263, 384, 395, 470, 638, 675, 676, 707, 734, 862, 936, 1049], "behaviour": [67, 74, 509, 515, 516, 524, 555, 638, 979, 985, 986, 994], "strategi": [67, 74, 101, 121, 122, 123, 124, 126, 148, 158, 172, 173, 182, 254, 268, 368, 429, 638, 665, 670, 675, 676, 734, 848, 912, 1049], "suitabl": [67, 74, 122, 133, 254, 268, 493, 638, 744, 958, 1049, 1057], "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, 412, 417, 418, 431, 440, 441, 442, 447, 449, 453, 458, 459, 471, 479, 503, 507, 519, 522, 547, 548, 556, 560, 587, 593, 598, 602, 605, 606, 607, 608, 609, 610, 620, 624, 628, 638, 652, 657, 660, 667, 670, 671, 672, 677, 679, 694, 700, 704, 706, 711, 721, 730, 734, 751, 752, 753, 754, 755, 759, 768, 772, 780, 781, 782, 783, 801, 821, 852, 855, 860, 862, 865, 877, 882, 895, 900, 901, 914, 920, 922, 923, 925, 928, 929, 933, 934, 937, 944, 973, 976, 989, 992, 1019, 1025, 1026, 1034, 1038, 1039, 1049], "speedup": [67, 133, 170, 254, 268, 638, 744, 1049], "receiv": [67, 112, 133, 186, 254, 466, 638, 688, 734, 1057], "now": [67, 159, 254, 470, 534, 536, 638, 671, 734, 1004, 1006], "One": [67, 139, 183, 187, 254, 265, 463, 619, 638, 737, 786, 1049], "whose": [67, 173, 187, 254, 363, 592, 638, 676, 734], "uniqu": [67, 121, 122, 123, 126, 168, 172, 183, 197, 245, 254, 269, 280, 310, 386, 394, 457, 470, 534, 535, 536, 539, 557, 559, 568, 611, 638, 675, 727, 734, 755, 785, 786, 865, 877, 926, 936, 1004, 1005, 1006, 1009, 1035, 1037, 1049], "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, 463, 505, 517, 561, 562, 572, 573, 621, 629, 638, 652, 655, 666, 678, 701, 706, 734, 737, 741, 742, 754, 760, 762, 804, 849, 860, 861, 862, 864, 865, 867, 868, 870, 871, 872, 873, 877, 884, 885, 889, 933, 934, 960, 979, 1030, 1040, 1049], "know": [67, 429, 522, 992, 1030, 1039, 1049], "first": [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, 410, 413, 414, 429, 439, 466, 493, 505, 515, 517, 518, 524, 525, 575, 582, 587, 594, 598, 629, 638, 656, 670, 672, 676, 679, 688, 707, 713, 721, 734, 737, 753, 784, 786, 818, 821, 825, 855, 865, 882, 893, 896, 897, 912, 919, 958, 985, 987, 988, 994, 995, 1019, 1028, 1049, 1057], "2022": [67, 139, 156, 158, 159, 173, 227, 254, 318, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 534, 587, 588, 590, 596, 638, 670, 671, 676, 734, 737, 794, 805, 821, 825, 832, 1004], "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, 416, 436, 438, 460, 461, 463, 468, 476, 480, 534, 535, 536, 546, 555, 562, 564, 566, 570, 571, 585, 603, 606, 610, 623, 638, 675, 684, 700, 734, 737, 828, 831, 1004, 1005, 1006], "df3": [67, 254], "set_tbl_format": 67, "09": [67, 124, 159, 254, 318, 329, 337, 338, 537, 627, 671, 734, 798, 1007], "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, 482, 483, 485, 488, 489, 490, 534, 535, 536, 539, 587, 588, 590, 638, 670, 671, 734, 737, 793, 794, 795, 796, 797, 798, 799, 801, 802, 803, 804, 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, 1004, 1005, 1006, 1009], "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, 482, 483, 484, 485, 486, 488, 489, 490, 491, 534, 535, 536, 539, 587, 588, 590, 638, 670, 671, 676, 734, 737, 793, 794, 796, 797, 801, 802, 803, 806, 807, 808, 809, 811, 813, 814, 815, 816, 817, 818, 819, 821, 822, 823, 824, 825, 826, 827, 829, 830, 832, 833, 834, 835, 912, 1004, 1005, 1006, 1009], "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, 482, 483, 485, 488, 489, 490, 535, 539, 587, 588, 638, 670, 671, 734, 793, 794, 795, 798, 799, 801, 802, 803, 806, 807, 808, 809, 811, 813, 815, 816, 817, 818, 819, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 1005, 1009], "af1": 67, "af2": 67, "af3": 67, "keep": [67, 101, 102, 112, 223, 226, 227, 254, 395, 439, 575, 638, 707, 709, 734, 919, 1049], "easili": [67, 200, 231, 234, 254, 576, 621, 695, 712, 716, 734], "dot": [67, 638, 699, 734, 1049], "product": [67, 138, 254, 307, 313, 567, 638, 782, 788, 1049], "fill_nul": [67, 147, 254, 638, 664, 713, 734, 1049], "sum_horizont": [67, 622], "167": 67, "47": 67, "callabl": [68, 69, 70, 71, 112, 133, 152, 186, 236, 254, 268, 429, 437, 438, 466, 481, 567, 582, 583, 594, 604, 614, 638, 680, 688, 718, 734, 744, 912, 946, 1049], "decor": [68, 69, 70, 71, 124, 126, 133, 254, 268, 638, 648, 649, 744, 1049], "under": [68, 69, 70, 71, 631, 632, 634, 639, 640, 644, 645, 646, 1041, 1043, 1045, 1050, 1053, 1054, 1055], "access": [68, 69, 70, 71, 170, 195, 196, 254, 439, 638, 713, 734, 1057], "by_first_letter_of_column_nam": 68, "f": [68, 102, 139, 156, 171, 212, 222, 225, 254, 261, 268, 429, 497, 514, 534, 536, 567, 577, 604, 638, 912, 984, 1004, 1006], "fromkei": [68, 70], "by_first_letter_of_column_valu": 68, "starts_with": [68, 286, 289, 509, 513, 737, 983], "to_seri": [68, 154, 254, 534, 573, 615, 1004], "xx": [68, 70, 123, 126, 737], 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1057], "42": [71, 159, 160, 180, 254, 671, 685, 734, 854, 1049], "961": 71, "1764": 71, "4160": 71, "build": [72, 91, 737, 1057], "wa": 72, "compil": [72, 403, 404, 886, 887], "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, 429, 466, 475, 509, 511, 563, 565, 567, 584, 605, 609, 622, 629, 638, 685, 695, 718, 734, 744, 761, 774, 775, 776, 824, 832, 840, 841, 842, 912, 940, 960, 961, 979, 981, 1049], "depend": [72, 118, 268, 403, 404, 447, 470, 473, 567, 593, 596, 602, 638, 886, 887, 936, 938, 1049, 1057], "host": [72, 101, 106, 110, 114, 116], "git": 72, "lazy_fram": 73, "comm_subplan_elim": [73, 656, 661, 663, 689, 699, 734], "comm_subexpr_elim": [73, 292, 638, 656, 661, 663, 689, 699, 734], "graph": [73, 174, 254, 673, 713, 734], "parallel": [73, 74, 99, 103, 110, 116, 158, 173, 174, 186, 254, 309, 410, 638, 649, 670, 675, 676, 718, 734, 784, 893, 1049], "threadpool": [73, 128], "Will": [73, 656, 661, 663, 689, 699, 734, 1049], "try": [73, 85, 87, 101, 102, 105, 106, 110, 112, 114, 116, 656, 661, 663, 689, 699, 734], "branch": [73, 656, 661, 663, 689, 699, 734], "subplan": [73, 656, 661, 663, 689, 699, 734], "union": [73, 74, 656, 661, 663, 689, 699, 734, 737], "subexpress": [73, 656, 661, 663, 689, 699, 734], "reus": [73, 656, 661, 663, 689, 699, 734], "part": [73, 90, 124, 516, 530, 531, 656, 661, 663, 689, 699, 713, 734, 986, 1000, 1001], "fashion": [73, 172, 254, 656, 661, 663, 689, 699, 734], "item": [74, 102, 195, 198, 254, 365, 406, 413, 416, 492, 531, 638, 844, 889, 896, 899, 957, 1001, 1049], "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, 464, 505, 563, 564, 565, 566, 572, 575, 576, 577, 578, 584, 585, 592, 605, 606, 609, 610, 618, 621, 622, 623, 638, 652, 669, 670, 671, 695, 696, 701, 706, 712, 716, 734, 784, 1049, 1057], "polarstyp": 74, "concatmethod": 74, "vertic": [74, 146, 225, 229, 254, 508, 978], "rechunk": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 146, 254, 638, 743, 772, 845, 925, 1049], "combin": [74, 85, 87, 158, 159, 160, 173, 227, 254, 265, 279, 341, 352, 463, 521, 527, 533, 587, 638, 670, 671, 676, 699, 734, 821, 825, 832, 991, 997, 1003], "concaten": [74, 152, 186, 254, 466, 577, 578, 638, 688, 734, 772, 925, 1049], "diagon": [74, 222, 254], "vstack": [74, 146, 254], "vertical_relax": 74, "coerc": [74, 476, 638], "equal": [74, 75, 101, 102, 112, 119, 120, 134, 153, 158, 173, 180, 221, 254, 292, 358, 359, 376, 398, 429, 461, 476, 481, 482, 483, 484, 485, 486, 488, 489, 490, 519, 520, 526, 542, 616, 617, 638, 652, 670, 676, 685, 701, 706, 734, 912, 946, 947, 948, 949, 950, 951, 953, 954, 955, 959, 989, 990, 996, 1012, 1049, 1057], "supertyp": [74, 148, 254, 267, 638, 665, 734], "find": [74, 150, 254, 493, 638, 958, 1049], "miss": [74, 101, 102, 112, 147, 254, 285, 360, 361, 362, 374, 389, 391, 638, 664, 734, 840, 841, 842, 1039, 1049], "stack": [74, 163, 229, 254], "don": [74, 133, 223, 225, 254, 268, 292, 309, 410, 464, 470, 567, 638, 707, 734, 737, 744, 784, 893, 936, 959, 1039, 1049], "auto": [74, 90, 92, 93, 94, 96, 108, 109, 110, 116, 222, 254, 734, 1057], "logic": [74, 133, 236, 254, 268, 553, 567, 638, 668, 675, 691, 715, 718, 734, 744, 1032, 1049], "align_fram": 74, "pattern": [74, 101, 102, 112, 114, 115, 116, 170, 254, 445, 509, 510, 515, 516, 524, 525, 718, 737, 960, 961, 979, 980, 985, 986, 994, 995, 1049], "collis": 74, "need": [74, 97, 101, 102, 103, 105, 119, 120, 158, 159, 197, 205, 217, 254, 429, 439, 497, 519, 537, 612, 615, 630, 638, 670, 671, 734, 966, 989, 1007, 1049], "sure": [74, 90, 95, 101, 102, 106, 110, 158, 159, 190, 254, 670, 671, 734], "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, 458, 459, 517, 518, 579, 582, 594, 638, 670, 734, 928, 929, 987, 988, 1049], "least": [82, 124, 465, 559, 638, 932, 1037, 1049], "unexpect": [83, 254, 268, 437, 638, 744, 1049], "caus": [83, 91, 101, 102, 112, 132, 146, 254, 743, 845, 1049], "panic": 83, "mismatch": [85, 109], "incompat": 87, "pa": [90, 117], "chunkedarrai": [90, 182, 254, 788, 1049], "recordbatch": [90, 171, 254], "schemadefinit": [90, 92, 93, 94, 96, 108, 109, 254, 734], "schema_overrid": [90, 92, 93, 94, 95, 96, 108, 109, 171, 217, 254, 284, 734, 737, 759], "copi": [90, 91, 132, 135, 136, 171, 212, 217, 218, 231, 254, 366, 542, 638, 654, 655, 712, 734, 773, 777, 790, 846, 1012, 1025, 1030, 1031, 1049], "closest": 90, "pair": [90, 92, 93, 94, 96, 108, 109, 123, 191, 254, 692, 734, 1057], "sever": [90, 92, 93, 94, 96, 108, 109, 254, 734, 1057], "wai": [90, 92, 93, 94, 96, 108, 109, 140, 157, 171, 186, 207, 234, 254, 464, 466, 505, 515, 576, 638, 658, 669, 688, 701, 716, 718, 734, 985], 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"typic": [93, 133, 254, 324, 737, 744, 800, 1049], "clearer": 93, "load": [93, 95, 104, 113, 125, 127, 254, 649, 672, 679, 734, 1057], "_partial_": [93, 254, 734], "omit": [93, 97, 122, 124, 126, 130, 183, 197, 254, 626, 627, 737], "mani": [93, 96, 103, 146, 254, 517, 743, 845, 987, 1049], "scan": [93, 96, 101, 102, 110, 112, 113, 114, 115, 116, 117, 158, 159, 254, 663, 670, 671, 672, 679, 734], "slow": [93, 96, 101, 102, 112, 268, 309, 638, 718, 784, 1049], "partial": 93, "present": [93, 119, 124, 387, 638, 1039, 1049], "np": [94, 149, 217, 254, 549, 638, 734, 864, 868, 870, 871, 946, 1020, 1049], "ndarrai": [94, 149, 217, 254, 549, 638, 734, 788, 958, 961, 1020, 1030, 1049], "numpi": [94, 118, 138, 170, 196, 197, 214, 217, 218, 254, 458, 459, 638, 734, 864, 868, 870, 871, 928, 929, 946, 1030, 1031, 1039, 1049], "columnar": [94, 96, 170, 196, 254], "interpret": [94, 96, 101, 102, 112, 254, 734], "yield": [94, 96, 101, 102, 112, 144, 146, 222, 254, 464, 638, 734, 743, 839, 845, 1049], "conclus": [94, 96, 254, 734], "nan_to_nul": [95, 254, 734, 1049], "include_index": 95, "pd": [95, 105, 553, 638, 1031, 1032, 1049], "panda": [95, 105, 118, 158, 218, 254, 337, 338, 553, 638, 670, 734, 817, 818, 1031, 1032, 1049], "instal": [95, 101, 102, 103, 106, 110, 118, 138, 217, 218, 254, 699, 734, 1031, 1049], "nan": [95, 119, 120, 124, 132, 147, 218, 254, 314, 315, 358, 359, 367, 376, 377, 382, 389, 391, 392, 393, 398, 436, 440, 453, 458, 459, 460, 461, 555, 579, 638, 664, 734, 746, 750, 789, 847, 870, 871, 928, 929, 946, 1030, 1031, 1049, 1057], "convert": [95, 104, 105, 113, 132, 213, 214, 215, 216, 217, 220, 254, 311, 319, 348, 351, 429, 438, 472, 523, 534, 535, 536, 537, 539, 638, 734, 795, 828, 831, 912, 993, 1004, 1005, 1006, 1007, 1009, 1017, 1028, 1029, 1030, 1031, 1039, 1049], "pd_df": 95, "pd_seri": 95, "tbl": [97, 99, 102], "reconstruct": 97, "repr": [97, 124, 126], "trim": 97, "whitespac": [97, 521, 527, 533, 991, 997, 1003], "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, 466, 516, 517, 518, 638, 796, 797, 798, 799, 802, 803, 805, 809, 810, 811, 812, 814, 815, 816, 819, 820, 822, 823, 826, 827, 829, 833, 834, 836, 986, 987, 988], "to_init_repr": [97, 254, 1049], "truncat": [97, 158, 170, 196, 197, 214, 254, 341, 670, 689, 734, 821], "identifi": [97, 179, 185, 223, 254, 684, 707, 734], "compound": [97, 197, 254, 737], "struct": [97, 183, 200, 220, 224, 231, 254, 310, 429, 439, 470, 479, 480, 517, 530, 531, 559, 582, 583, 585, 604, 638, 695, 708, 712, 718, 734, 785, 912, 936, 944, 987, 998, 1000, 1001, 1049], "neither": [97, 105, 198, 254, 429, 912], "source_ac": 97, "source_cha": 97, "ident": [97, 135, 136, 254, 348, 479, 480, 638, 654, 655, 734, 773, 777, 828, 944, 945, 1049], "timestamp": [97, 344, 596, 824], "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, 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654, 656, 672, 679, 734], "ideal": 99, "read_csv_batch": [99, 650], "tpch": [99, 102], "tables_scale_100": [99, 102], "lineitem": [99, 102, 103], "try_parse_d": [99, 101, 102, 112], "n_row": [100, 101, 102, 106, 110, 112, 114, 115, 116, 171, 254, 663, 734], "accept": [100, 101, 102, 104, 106, 110, 134, 145, 157, 200, 207, 221, 231, 234, 254, 261, 363, 383, 439, 464, 505, 562, 563, 564, 565, 566, 572, 575, 576, 577, 578, 584, 585, 592, 605, 606, 609, 610, 618, 621, 622, 623, 629, 638, 652, 662, 669, 695, 701, 706, 712, 716, 734, 860, 1049], "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, 482, 483, 485, 488, 489, 490, 638, 793, 795, 798, 801, 802, 804, 809, 811, 814, 816, 819, 822, 823, 825, 826, 830, 832, 833, 834, 835, 836], "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], "we": [101, 102, 105, 112, 158, 159, 173, 227, 254, 268, 341, 345, 352, 410, 437, 482, 483, 484, 485, 486, 488, 489, 490, 523, 603, 629, 638, 670, 671, 676, 680, 734, 744, 821, 825, 832, 856, 893, 993, 1049], "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, 470, 482, 483, 484, 485, 486, 488, 489, 490, 497, 592, 638, 670, 671, 676, 692, 734, 821, 825, 832, 959, 1030, 1049], "builtin": [101, 102, 105], "stringio": [101, 102], "fsspec": [101, 102, 106, 110, 113, 114, 116, 118], "remot": 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429, 437, 438, 481, 567, 582, 583, 594, 604, 614, 638, 680, 688, 718, 734, 744, 840, 841, 842, 912, 1049], "lower": [112, 158, 189, 246, 254, 298, 300, 383, 435, 438, 471, 475, 486, 569, 587, 588, 600, 601, 613, 626, 627, 638, 663, 670, 690, 728, 734, 776, 860, 918, 937, 940, 951, 1036, 1049], "simpli": [112, 464, 582, 594, 638], "idx": [112, 158, 171, 254, 429, 670, 734, 912, 961, 1049], "uint16": [112, 307, 308, 547, 638, 737, 782, 783, 1018, 1049], "u16": [112, 124, 1057], "eu": 113, "central": [113, 396, 502, 638, 880, 972, 1049], "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, 587, 676, 734, 737, 794, 795, 796, 797, 798, 799, 824, 825, 828, 829, 831, 832, 834], "04": [117, 158, 227, 254, 318, 319, 323, 327, 334, 335, 336, 337, 338, 344, 345, 346, 347, 348, 351, 353, 355, 534, 587, 590, 670, 734, 737, 795, 799, 803, 809, 811, 814, 815, 816, 817, 818, 819, 823, 824, 826, 827, 828, 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238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 478, 513, 532, 638, 675, 692, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 983, 1002], "banana": [137, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 478, 638, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729], "pairwis": [138, 254], "pearson": [138, 254, 396, 502, 579, 638, 880, 972, 1049], "correl": [138, 254, 579, 616], "coeffici": [138, 254, 502, 638, 972, 1049], "corrcoef": [138, 254], "percentil": [139, 254, 786, 1049], "summari": [139, 254, 786, 1049], "glimps": [139, 161, 254], "usd": [139, 156, 254, 1057], "2020": [139, 156, 159, 254, 319, 323, 324, 327, 330, 331, 333, 335, 340, 344, 347, 348, 351, 352, 534, 535, 536, 671, 734, 737, 795, 799, 800, 803, 810, 812, 815, 820, 824, 827, 828, 831, 832, 1004, 1005, 1006], "null_count": [139, 142, 254, 309, 638, 734, 784, 786, 1049], "266667": [139, 254], "666667": [139, 177, 228, 242, 254, 360, 638, 710, 724, 734], "std": [139, 254, 481, 488, 638, 734, 786, 953, 1049], "101514": [139, 254], "707107": [139, 254, 361, 488, 638, 841, 1049], "57735": [139, 254], "median": [139, 187, 254, 368, 484, 638, 713, 734, 786, 949, 1049], "more_column": [140, 145, 201, 224, 254, 363, 592, 638, 658, 662, 696, 708, 734], "Or": [140, 157, 158, 159, 173, 207, 227, 234, 254, 464, 505, 576, 629, 638, 658, 669, 670, 671, 676, 701, 716, 734], "subset": [142, 183, 223, 254, 659, 707, 734], "snippet": [142, 254, 659, 734], "all_horizont": [142, 254, 563, 659, 734], "is_nul": [142, 254, 638, 659, 734, 1049], "sizeunit": [144, 254, 839, 1049], "heap": [144, 254, 839, 1049], "its": [144, 254, 318, 345, 352, 505, 638, 794, 825, 832, 839, 1049], "bitmap": [144, 254, 839, 1049], "therefor": [144, 254, 629, 839, 1049], "structarrai": [144, 254, 839, 1049], "constant": [144, 159, 254, 316, 366, 638, 671, 734, 792, 839, 846, 1049], "unchang": [144, 254, 553, 638, 680, 718, 734, 839, 1032, 1049], "capac": [144, 205, 254, 839, 967, 1049], "ffi": [144, 254, 839, 1049], "kb": [144, 254, 839, 1049], "mb": [144, 254, 839, 1049], "gb": [144, 254, 839, 1049], "tb": [144, 254, 839, 1049], "revers": [144, 254, 304, 305, 306, 307, 308, 438, 468, 546, 638, 734, 780, 781, 782, 783, 1049], "1_000_000": [144, 254, 839, 1049], "25888898": [144, 254], "689577102661133": [144, 254], "long": [145, 179, 225, 254, 662, 684, 734], "letter": [145, 239, 248, 254, 363, 516, 592, 638, 662, 721, 730, 734, 737, 986], "onlin": [146, 254, 743, 845, 1049], "rerun": [146, 254, 743, 845, 1049], "conveni": [146, 254, 743, 845, 1049], "evalu": [147, 149, 173, 254, 265, 279, 309, 381, 401, 402, 429, 439, 463, 563, 565, 569, 573, 587, 588, 591, 600, 601, 612, 615, 621, 626, 627, 629, 630, 638, 666, 673, 675, 676, 734, 754, 784, 884, 885, 1040, 1049], "Not": [147, 254, 389, 391, 439, 638, 664, 734], "To": [147, 254, 314, 315, 341, 368, 509, 515, 516, 524, 540, 622, 638, 664, 734, 821, 979, 985, 986, 994, 1010, 1030, 1049], "fillnullstrategi": [148, 254, 368, 638, 665, 734, 848, 1049], "matches_supertyp": [148, 254, 665, 734], "forward": [148, 173, 254, 337, 368, 374, 638, 665, 676, 734, 817, 848, 1049], "consecut": [148, 254, 285, 368, 374, 508, 638, 665, 734, 848, 978, 1049], "fill_nan": [148, 254, 638, 734, 1049], "OR": [149, 254, 565, 566, 666, 734, 737], "reduct": [152, 254], "supercast": [152, 254], "parent": [152, 254], "rule": [152, 254], "arithmet": [152, 254], "zip_with": [152, 254, 1049], "foo11": [152, 254], "bar22": [152, 254], "null_equ": [153, 254, 959, 1049], "retriev": [154, 254, 403, 404, 543, 886, 887, 1013], "return_as_str": [156, 254, 450], "preview": [156, 254], "wide": [156, 179, 225, 254, 684, 734], "nice": [156, 254], "few": [156, 254], "rather": [156, 173, 254, 450, 480, 542, 638, 676, 734, 945, 1012, 1049], "head": [156, 175, 210, 254, 267, 400, 638, 679, 734, 882, 1019, 1049], "tail": [156, 161, 254, 267, 502, 638, 734, 855, 972, 1049], "more_bi": [157, 185, 207, 254, 505, 638, 669, 701, 734], "consist": [157, 185, 254, 534, 669, 734, 743, 845, 1004, 1049], "regardless": [157, 254, 518, 988], "agg": [157, 158, 159, 254, 262, 268, 369, 371, 504, 505, 549, 561, 580, 638, 656, 661, 663, 669, 670, 671, 689, 699, 734, 737], "index_column": [158, 159, 254, 670, 671, 734], "timedelta": [158, 159, 227, 254, 322, 325, 326, 329, 334, 336, 341, 342, 343, 345, 346, 350, 352, 353, 354, 356, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 737, 806, 808, 813, 821, 825, 832, 928, 929, 1049], "period": [158, 159, 203, 204, 254, 345, 352, 360, 361, 362, 423, 465, 495, 496, 587, 588, 626, 627, 638, 670, 671, 697, 698, 734, 825, 832, 840, 841, 842, 906, 932, 964, 965, 1049], "include_boundari": [158, 254, 670, 734], "closedinterv": [158, 159, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 860, 1049], "start_bi": [158, 254, 670, 734], "startbi": [158, 254, 670, 734], "window": [158, 159, 254, 309, 345, 352, 360, 361, 362, 464, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 577, 616, 617, 638, 670, 671, 734, 784, 825, 832, 840, 841, 842, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 1049], "check_sort": [158, 159, 254, 670, 671, 734], "dynamicgroupbi": [158, 254], "groupbi": [158, 159, 183, 254, 262, 268, 309, 369, 371, 410, 464, 504, 505, 549, 561, 567, 580, 638, 656, 661, 663, 670, 671, 689, 699, 734, 737, 784, 893, 1049], "member": [158, 254, 670, 734, 867, 1049], "seen": [158, 254, 285, 374, 638, 670, 734], "roll": [158, 159, 254, 337, 338, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 577, 616, 617, 638, 670, 671, 734, 817, 818, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 1049], "slot": [158, 254, 309, 312, 408, 638, 670, 734, 784, 787, 891, 1049], "interv": [158, 159, 227, 254, 310, 328, 345, 346, 352, 383, 470, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 785, 798, 801, 802, 804, 809, 811, 814, 816, 819, 822, 823, 825, 826, 830, 832, 833, 834, 836, 860, 936, 1049], "1n": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "nanosecond": [158, 159, 173, 227, 254, 341, 345, 346, 352, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 821, 825, 826, 832], "1u": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "microsecond": [158, 159, 170, 173, 196, 197, 214, 227, 254, 341, 345, 346, 352, 482, 483, 484, 485, 486, 488, 489, 490, 589, 590, 625, 638, 670, 671, 676, 689, 734, 737, 821, 825, 832], "1m": [158, 159, 173, 227, 254, 330, 331, 333, 340, 341, 345, 347, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 810, 812, 820, 821, 825, 827, 832], "millisecond": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 737, 821, 825, 832], "minut": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 589, 590, 625, 626, 638, 670, 671, 676, 734, 737, 821, 825, 832], "1h": [158, 159, 173, 227, 254, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 626, 627, 638, 670, 671, 676, 734, 800, 802, 821, 825, 832], "hour": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 589, 590, 625, 626, 638, 670, 671, 676, 734, 737, 821, 825, 832], "1d": [158, 159, 173, 227, 254, 317, 327, 335, 341, 345, 352, 355, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 603, 638, 670, 671, 676, 734, 737, 793, 801, 803, 806, 807, 808, 813, 815, 821, 825, 830, 832, 834, 835], "1w": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "week": [158, 159, 173, 227, 254, 341, 345, 352, 354, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 737, 821, 825, 832, 834], "1mo": [158, 159, 173, 227, 254, 319, 323, 337, 338, 341, 344, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 638, 670, 671, 676, 734, 795, 799, 816, 817, 818, 821, 822, 823, 824, 825, 832, 833], "month": [158, 159, 173, 227, 254, 322, 337, 338, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 586, 587, 588, 589, 638, 670, 671, 676, 734, 798, 817, 818, 821, 825, 832], "1q": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "quarter": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "1y": [158, 159, 173, 227, 254, 328, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 638, 670, 671, 676, 734, 804, 821, 825, 832, 836], "1i": [158, 159, 173, 227, 254, 341, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821], "3d12h4m25": [158, 159, 173, 227, 254, 345, 352, 670, 671, 676, 734, 825, 832], "suffix": [158, 159, 172, 173, 200, 227, 231, 234, 254, 263, 289, 341, 345, 352, 389, 391, 392, 393, 438, 464, 468, 478, 482, 483, 484, 485, 486, 488, 489, 490, 513, 638, 670, 671, 675, 676, 695, 712, 713, 716, 734, 737, 763, 821, 825, 832, 983], "_satur": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 638, 670, 671, 676, 734, 821, 825, 832], "satur": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "28": [158, 159, 173, 213, 227, 254, 261, 341, 344, 345, 352, 355, 482, 483, 484, 485, 486, 488, 489, 490, 587, 638, 670, 671, 676, 734, 821, 824, 825, 832, 835, 1057], "correspond": [158, 159, 173, 217, 227, 254, 329, 341, 345, 352, 473, 480, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 805, 821, 825, 832, 938, 945, 1049], "due": [158, 159, 173, 197, 227, 254, 263, 293, 324, 341, 345, 352, 395, 468, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 546, 638, 670, 671, 676, 734, 766, 800, 821, 825, 832, 1049], "daylight": [158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 800, 821, 825, 832], "10i": [158, 159, 254, 670, 671, 734], "ascend": [158, 159, 254, 670, 671, 734], "dynam": [158, 254, 429, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 734, 912], "matter": [158, 159, 170, 196, 197, 214, 254, 670, 671, 734], "_lower_bound": [158, 254, 670, 734], "_upper_bound": [158, 254, 670, 734], "harder": [158, 254, 670, 734], "tempor": [158, 159, 170, 196, 197, 214, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 638, 649, 670, 671, 734, 737, 860, 876, 1049], "inclus": [158, 159, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 529, 530, 569, 587, 588, 600, 601, 626, 627, 638, 670, 671, 734, 860, 999, 1000, 1049], "datapoint": [158, 254, 670, 734], "mondai": [158, 254, 352, 354, 670, 734, 832, 834], "tuesdai": [158, 254, 670, 734], "wednesdai": [158, 254, 670, 734], "thursdai": [158, 254, 670, 734], "fridai": [158, 254, 670, 734], "saturdai": [158, 254, 670, 734], "sundai": [158, 254, 354, 670, 734, 834], "weekli": [158, 254, 352, 670, 734, 832], "sorted": [158, 159, 254, 670, 671, 734], "metadata": [158, 159, 254, 670, 671, 734], "verifi": [158, 159, 254, 670, 671, 734], "incorrectli": [158, 159, 254, 429, 670, 671, 734], "incorrect": [158, 159, 254, 355, 494, 638, 670, 671, 718, 734, 835, 962, 1049], "re": [158, 217, 254, 337, 338, 670, 734, 817, 818, 1056], "come": [158, 254, 337, 338, 396, 638, 650, 670, 733, 734, 817, 818, 880, 1049], "set_index": [158, 254, 670, 734], "resampl": [158, 254, 670, 734], "reset_index": [158, 254, 670, 734], "though": [158, 254, 670, 734], "evenli": [158, 254, 470, 638, 670, 734, 936, 1049], "upsampl": [158, 254, 670, 734], "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, 482, 483, 485, 488, 489, 490, 638, 670, 734, 792, 793, 795, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 830, 832, 833, 834, 835, 836], "30m": [158, 254, 345, 352, 670, 734, 825, 832], "time_min": [158, 254, 670, 734], "time_max": [158, 254, 670, 734], "23": [158, 159, 254, 318, 322, 326, 342, 345, 354, 482, 483, 485, 488, 489, 490, 589, 625, 626, 627, 638, 670, 671, 734, 737, 794, 802, 1057], "boundari": [158, 254, 298, 299, 300, 352, 638, 670, 734, 774, 775, 776, 832, 856, 1049], "time_count": [158, 254, 670, 734], "_lower_boundari": [158, 254, 670, 734], "_upper_boundari": [158, 254, 670, 734], "lower_bound": [158, 254, 298, 300, 383, 638, 670, 734, 774, 776, 860, 1036, 1049], "upper_bound": [158, 254, 298, 299, 383, 429, 638, 670, 734, 774, 775, 860, 918, 1049], "time_agg_list": [158, 254, 670, 734], "int_rang": [158, 225, 236, 254, 569, 670, 718, 734], "2i": [158, 254, 670, 734], "3i": [158, 254, 670, 734], "a_agg_list": [158, 254, 670, 734], "rollinggroupbi": [159, 254], "dynamic_groupbi": [159, 254, 671, 734], "groupby_dynam": [159, 254, 671, 734], "t_0": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "t_1": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "t_n": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "19": [159, 173, 254, 345, 483, 485, 537, 638, 671, 676, 734, 825, 832, 1007], "43": [159, 254, 308, 489, 638, 671, 734], "strptime": [159, 254, 344, 671, 734, 824], "set_sort": [159, 173, 227, 254, 638, 671, 676, 734, 1049], "2d": [159, 217, 254, 603, 671, 734, 792, 798], "sum_a": [159, 254, 671, 734], "min_a": [159, 254, 671, 734], "max_a": [159, 254, 671, 734], "seed": [160, 198, 254, 378, 473, 492, 498, 638, 854, 938, 957, 968, 1049], "seed_1": [160, 254, 378, 638, 854, 1049], "seed_2": [160, 254, 378, 638, 854, 1049], "seed_3": [160, 254, 378, 638, 854, 1049], "hash": [160, 254, 638, 1049], "u64": [160, 254, 378, 475, 638, 689, 734, 854, 1049], "10783150408545073287": [160, 254], "1438741209321515184": [160, 254], "10047419486152048166": [160, 254], "2047317070637311557": [160, 254], "ab": [161, 175, 210, 231, 254, 543, 544, 638, 712, 734, 855, 882, 1019, 1049], "grown": [163, 254], "intermedi": [165, 254, 674, 734, 1057], "linear": [165, 189, 246, 254, 382, 405, 442, 471, 486, 577, 578, 613, 638, 674, 690, 728, 734, 859, 888, 937, 951, 1049], "mask": [166, 168, 254, 287, 384, 386, 394, 509, 511, 638, 761, 849, 860, 862, 865, 877, 933, 934, 960, 979, 981, 1040, 1049], "visual": [166, 168, 254], "buffer_s": [170, 254], "intern": [170, 254], "veri": [170, 222, 254, 268, 638, 655, 718, 734, 777, 1049], "fit": [170, 205, 225, 254, 497, 615, 638, 966, 967, 1049], "signific": [170, 254, 268, 638], "4x": [170, 254], "materialis": [170, 171, 195, 196, 197, 254], "rows_by_kei": [170, 196, 254], "iter_row": [171, 195, 196, 197, 254], "17_500": [171, 254], "klmnoopqrstuvwxyz": [171, 254], "__name__": [171, 254], "7500": [171, 254], "15_000": [171, 254], "record_batch": [171, 254], "to_arrow": [171, 254, 1049], "to_batch": [171, 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module polars.selectors)": [[737, "polars.selectors.starts_with"]], "string() (in module polars.selectors)": [[737, "polars.selectors.string"]], "temporal() (in module polars.selectors)": [[737, "polars.selectors.temporal"]], "abs() (polars.series method)": [[739, "polars.Series.abs"]], "alias() (polars.series method)": [[740, "polars.Series.alias"]], "all() (polars.series method)": [[741, "polars.Series.all"]], "any() (polars.series method)": [[742, "polars.Series.any"]], "append() (polars.series method)": [[743, "polars.Series.append"]], "apply() (polars.series method)": [[744, "polars.Series.apply"]], "arccos() (polars.series method)": [[745, "polars.Series.arccos"]], "arccosh() (polars.series method)": [[746, "polars.Series.arccosh"]], "arcsin() (polars.series method)": [[747, "polars.Series.arcsin"]], "arcsinh() (polars.series method)": [[748, "polars.Series.arcsinh"]], "arctan() (polars.series method)": [[749, "polars.Series.arctan"]], "arctanh() (polars.series method)": [[750, 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"polars.DataFrame.drop_in_place", "polars.DataFrame.drop_nulls", "polars.DataFrame.dtypes", "polars.DataFrame.estimated_size", "polars.DataFrame.explode", "polars.DataFrame.extend", "polars.DataFrame.fill_nan", "polars.DataFrame.fill_null", "polars.DataFrame.filter", "polars.DataFrame.find_idx_by_name", "polars.DataFrame.flags", "polars.DataFrame.fold", "polars.DataFrame.frame_equal", "polars.DataFrame.get_column", "polars.DataFrame.get_columns", "polars.DataFrame.glimpse", "polars.DataFrame.groupby", "polars.DataFrame.groupby_dynamic", "polars.DataFrame.groupby_rolling", "polars.DataFrame.hash_rows", "polars.DataFrame.head", "polars.DataFrame.height", "polars.DataFrame.hstack", "polars.DataFrame.insert_at_idx", "polars.DataFrame.interpolate", "polars.DataFrame.is_duplicated", "polars.DataFrame.is_empty", "polars.DataFrame.is_unique", "polars.DataFrame.item", "polars.DataFrame.iter_rows", "polars.DataFrame.iter_slices", "polars.DataFrame.join", "polars.DataFrame.join_asof", "polars.DataFrame.lazy", "polars.DataFrame.limit", "polars.DataFrame.max", "polars.DataFrame.mean", "polars.DataFrame.median", "polars.DataFrame.melt", "polars.DataFrame.merge_sorted", "polars.DataFrame.min", "polars.DataFrame.n_chunks", "polars.DataFrame.n_unique", "polars.DataFrame.null_count", "polars.DataFrame.partition_by", "polars.DataFrame.pipe", "polars.DataFrame.pivot", "polars.DataFrame.product", "polars.DataFrame.quantile", "polars.DataFrame.rechunk", "polars.DataFrame.rename", "polars.DataFrame.replace", "polars.DataFrame.replace_at_idx", "polars.DataFrame.reverse", "polars.DataFrame.row", "polars.DataFrame.rows", "polars.DataFrame.rows_by_key", "polars.DataFrame.sample", "polars.DataFrame.schema", "polars.DataFrame.select", "polars.DataFrame.set_sorted", "polars.DataFrame.shape", "polars.DataFrame.shift", "polars.DataFrame.shift_and_fill", "polars.DataFrame.shrink_to_fit", "polars.DataFrame.slice", "polars.DataFrame.sort", "polars.DataFrame.std", "polars.DataFrame.sum", "polars.DataFrame.tail", "polars.DataFrame.take_every", "polars.DataFrame.to_arrow", "polars.DataFrame.to_dict", "polars.DataFrame.to_dicts", "polars.DataFrame.to_dummies", "polars.DataFrame.to_init_repr", "polars.DataFrame.to_numpy", "polars.DataFrame.to_pandas", "polars.DataFrame.to_series", "polars.DataFrame.to_struct", "polars.DataFrame.top_k", "polars.DataFrame.transpose", "polars.DataFrame.unique", "polars.DataFrame.unnest", "polars.DataFrame.unstack", "polars.DataFrame.update", "polars.DataFrame.upsample", "polars.DataFrame.var", "polars.DataFrame.vstack", "polars.DataFrame.width", "polars.DataFrame.with_columns", "polars.DataFrame.with_row_count", "polars.dataframe.groupby.GroupBy.__iter__", "polars.dataframe.groupby.GroupBy.agg", "polars.dataframe.groupby.GroupBy.all", "polars.dataframe.groupby.GroupBy.apply", "polars.dataframe.groupby.GroupBy.count", "polars.dataframe.groupby.GroupBy.first", "polars.dataframe.groupby.GroupBy.head", "polars.dataframe.groupby.GroupBy.last", "polars.dataframe.groupby.GroupBy.max", "polars.dataframe.groupby.GroupBy.mean", "polars.dataframe.groupby.GroupBy.median", "polars.dataframe.groupby.GroupBy.min", "polars.dataframe.groupby.GroupBy.n_unique", "polars.dataframe.groupby.GroupBy.quantile", "polars.dataframe.groupby.GroupBy.sum", "polars.dataframe.groupby.GroupBy.tail", "Attributes", "Computation", "Descriptive", "Export", "GroupBy", "DataFrame", "Miscellaneous", "Manipulation/selection", "Data types", "Exceptions", "Aggregation", "polars.Expr.abs", "polars.Expr.add", "polars.Expr.agg_groups", "polars.Expr.alias", "polars.Expr.all", "polars.Expr.and_", "polars.Expr.any", "polars.Expr.append", "polars.Expr.apply", "polars.Expr.approx_unique", "polars.Expr.arccos", "polars.Expr.arccosh", "polars.Expr.arcsin", "polars.Expr.arcsinh", "polars.Expr.arctan", "polars.Expr.arctanh", "polars.Expr.arg_max", "polars.Expr.arg_min", "polars.Expr.arg_sort", "polars.Expr.arg_true", "polars.Expr.arg_unique", "polars.Expr.arr.max", "polars.Expr.arr.min", "polars.Expr.arr.sum", "polars.Expr.arr.unique", "polars.Expr.backward_fill", "polars.Expr.bin.contains", "polars.Expr.bin.decode", "polars.Expr.bin.encode", "polars.Expr.bin.ends_with", "polars.Expr.bin.starts_with", "polars.Expr.bottom_k", "polars.Expr.cache", "polars.Expr.cast", "polars.Expr.cat.get_categories", "polars.Expr.cat.set_ordering", "polars.Expr.cbrt", "polars.Expr.ceil", "polars.Expr.clip", "polars.Expr.clip_max", "polars.Expr.clip_min", "polars.Expr.cos", "polars.Expr.cosh", "polars.Expr.count", "polars.Expr.cumcount", "polars.Expr.cummax", "polars.Expr.cummin", "polars.Expr.cumprod", "polars.Expr.cumsum", "polars.Expr.cumulative_eval", "polars.Expr.cut", "polars.Expr.degrees", "polars.Expr.diff", "polars.Expr.dot", "polars.Expr.drop_nans", "polars.Expr.drop_nulls", "polars.Expr.dt.base_utc_offset", "polars.Expr.dt.cast_time_unit", "polars.Expr.dt.combine", "polars.Expr.dt.convert_time_zone", "polars.Expr.dt.date", "polars.Expr.dt.datetime", "polars.Expr.dt.day", "polars.Expr.dt.days", "polars.Expr.dt.dst_offset", "polars.Expr.dt.epoch", "polars.Expr.dt.hour", "polars.Expr.dt.hours", "polars.Expr.dt.is_leap_year", "polars.Expr.dt.iso_year", "polars.Expr.dt.microsecond", "polars.Expr.dt.microseconds", "polars.Expr.dt.millisecond", "polars.Expr.dt.milliseconds", "polars.Expr.dt.minute", "polars.Expr.dt.minutes", "polars.Expr.dt.month", "polars.Expr.dt.month_end", "polars.Expr.dt.month_start", "polars.Expr.dt.nanosecond", "polars.Expr.dt.nanoseconds", "polars.Expr.dt.offset_by", "polars.Expr.dt.ordinal_day", "polars.Expr.dt.quarter", "polars.Expr.dt.replace_time_zone", "polars.Expr.dt.round", "polars.Expr.dt.second", "polars.Expr.dt.seconds", "polars.Expr.dt.strftime", "polars.Expr.dt.time", "polars.Expr.dt.timestamp", "polars.Expr.dt.to_string", "polars.Expr.dt.truncate", "polars.Expr.dt.week", "polars.Expr.dt.weekday", "polars.Expr.dt.with_time_unit", "polars.Expr.dt.year", "polars.Expr.entropy", "polars.Expr.eq", "polars.Expr.eq_missing", "polars.Expr.ewm_mean", "polars.Expr.ewm_std", "polars.Expr.ewm_var", "polars.Expr.exclude", "polars.Expr.exp", "polars.Expr.explode", "polars.Expr.extend_constant", "polars.Expr.fill_nan", "polars.Expr.fill_null", "polars.Expr.filter", "polars.Expr.first", "polars.Expr.flatten", "polars.Expr.floor", "polars.Expr.floordiv", "polars.Expr.forward_fill", "polars.Expr.from_json", "polars.Expr.ge", "polars.Expr.gt", "polars.Expr.hash", "polars.Expr.head", "polars.Expr.implode", "polars.Expr.inspect", "polars.Expr.interpolate", "polars.Expr.is_between", "polars.Expr.is_duplicated", "polars.Expr.is_finite", "polars.Expr.is_first", "polars.Expr.is_in", "polars.Expr.is_infinite", "polars.Expr.is_nan", "polars.Expr.is_not", "polars.Expr.is_not_nan", "polars.Expr.is_not_null", "polars.Expr.is_null", "polars.Expr.is_unique", "polars.Expr.keep_name", "polars.Expr.kurtosis", "polars.Expr.last", "polars.Expr.le", "polars.Expr.len", "polars.Expr.limit", "polars.Expr.list.all", "polars.Expr.list.any", "polars.Expr.list.arg_max", "polars.Expr.list.arg_min", "polars.Expr.list.concat", "polars.Expr.list.contains", "polars.Expr.list.count_match", "polars.Expr.list.diff", "polars.Expr.list.difference", "polars.Expr.list.eval", "polars.Expr.list.explode", "polars.Expr.list.first", "polars.Expr.list.get", "polars.Expr.list.head", "polars.Expr.list.intersection", "polars.Expr.list.join", "polars.Expr.list.last", "polars.Expr.list.lengths", "polars.Expr.list.max", "polars.Expr.list.mean", "polars.Expr.list.min", "polars.Expr.list.reverse", "polars.Expr.list.shift", "polars.Expr.list.slice", "polars.Expr.list.sort", "polars.Expr.list.sum", "polars.Expr.list.tail", "polars.Expr.list.take", "polars.Expr.list.to_struct", "polars.Expr.list.union", "polars.Expr.list.unique", "polars.Expr.log", "polars.Expr.log10", "polars.Expr.log1p", "polars.Expr.lower_bound", "polars.Expr.lt", "polars.Expr.map", "polars.Expr.map_alias", "polars.Expr.map_dict", "polars.Expr.max", "polars.Expr.mean", "polars.Expr.median", "polars.Expr.meta.eq", "polars.Expr.meta.has_multiple_outputs", "polars.Expr.meta.is_regex_projection", "polars.Expr.meta.ne", "polars.Expr.meta.output_name", "polars.Expr.meta.pop", "polars.Expr.meta.root_names", "polars.Expr.meta.tree_format", "polars.Expr.meta.undo_aliases", "polars.Expr.meta.write_json", "polars.Expr.min", "polars.Expr.mod", "polars.Expr.mode", "polars.Expr.mul", "polars.Expr.n_unique", "polars.Expr.nan_max", "polars.Expr.nan_min", "polars.Expr.ne", "polars.Expr.ne_missing", "polars.Expr.null_count", "polars.Expr.or_", "polars.Expr.over", "polars.Expr.pct_change", "polars.Expr.pipe", "polars.Expr.pow", "polars.Expr.prefix", "polars.Expr.product", "polars.Expr.qcut", "polars.Expr.quantile", "polars.Expr.radians", "polars.Expr.rank", "polars.Expr.rechunk", "polars.Expr.reinterpret", "polars.Expr.repeat_by", "polars.Expr.reshape", "polars.Expr.reverse", "polars.Expr.rle", "polars.Expr.rle_id", "polars.Expr.rolling_apply", "polars.Expr.rolling_max", "polars.Expr.rolling_mean", "polars.Expr.rolling_median", "polars.Expr.rolling_min", "polars.Expr.rolling_quantile", "polars.Expr.rolling_skew", "polars.Expr.rolling_std", "polars.Expr.rolling_sum", "polars.Expr.rolling_var", "polars.Expr.round", "polars.Expr.sample", "polars.Expr.search_sorted", "polars.Expr.set_sorted", "polars.Expr.shift", "polars.Expr.shift_and_fill", "polars.Expr.shrink_dtype", "polars.Expr.shuffle", "polars.Expr.sign", "polars.Expr.sin", "polars.Expr.sinh", "polars.Expr.skew", "polars.Expr.slice", "polars.Expr.sort", "polars.Expr.sort_by", "polars.Expr.sqrt", "polars.Expr.std", "polars.Expr.str.concat", "polars.Expr.str.contains", "polars.Expr.str.count_match", "polars.Expr.str.decode", "polars.Expr.str.encode", "polars.Expr.str.ends_with", "polars.Expr.str.explode", "polars.Expr.str.extract", "polars.Expr.str.extract_all", "polars.Expr.str.json_extract", "polars.Expr.str.json_path_match", "polars.Expr.str.lengths", "polars.Expr.str.ljust", "polars.Expr.str.lstrip", "polars.Expr.str.n_chars", "polars.Expr.str.parse_int", "polars.Expr.str.replace", "polars.Expr.str.replace_all", "polars.Expr.str.rjust", "polars.Expr.str.rstrip", "polars.Expr.str.slice", "polars.Expr.str.split", "polars.Expr.str.split_exact", "polars.Expr.str.splitn", "polars.Expr.str.starts_with", "polars.Expr.str.strip", "polars.Expr.str.strptime", "polars.Expr.str.to_date", "polars.Expr.str.to_datetime", "polars.Expr.str.to_decimal", "polars.Expr.str.to_lowercase", "polars.Expr.str.to_time", "polars.Expr.str.to_titlecase", "polars.Expr.str.to_uppercase", "polars.Expr.str.zfill", "polars.Expr.struct.field", "polars.Expr.struct.rename_fields", "polars.Expr.sub", "polars.Expr.suffix", "polars.Expr.sum", "polars.Expr.tail", "polars.Expr.take", "polars.Expr.take_every", "polars.Expr.tan", "polars.Expr.tanh", "polars.Expr.to_physical", "polars.Expr.top_k", "polars.Expr.truediv", "polars.Expr.unique", "polars.Expr.unique_counts", "polars.Expr.upper_bound", "polars.Expr.value_counts", "polars.Expr.var", "polars.Expr.where", "polars.Expr.xor", "polars.all", "polars.all_horizontal", "polars.any", "polars.any_horizontal", "polars.apply", "polars.approx_unique", "polars.arange", "polars.arctan2", "polars.arctan2d", "polars.arg_sort_by", "polars.arg_where", "polars.avg", "polars.coalesce", "polars.col", "polars.concat_list", "polars.concat_str", "polars.corr", "polars.count", "polars.cov", "polars.cumfold", "polars.cumreduce", "polars.cumsum", "polars.cumsum_horizontal", "polars.date", "polars.date_range", "polars.date_ranges", "polars.datetime", "polars.duration", "polars.element", "polars.exclude", "polars.first", "polars.fold", "polars.format", "polars.from_epoch", "polars.groups", "polars.head", "polars.implode", "polars.int_range", "polars.int_ranges", "polars.last", "polars.lit", "polars.map", "polars.max", "polars.max_horizontal", "polars.mean", "polars.median", "polars.min", "polars.min_horizontal", "polars.n_unique", "polars.ones", "polars.quantile", "polars.reduce", "polars.repeat", "polars.rolling_corr", "polars.rolling_cov", "polars.select", "polars.sql_expr", "polars.std", "polars.struct", "polars.sum", "polars.sum_horizontal", "polars.tail", "polars.time", "polars.time_range", "polars.time_ranges", "polars.var", "polars.when", "polars.zeros", "Array", "Binary", "Boolean", "Categories", "Columns / names", "Computation", "Functions", "Expressions", "List", "Meta", "Miscellaneous", "Manipulation/selection", "Operators", "String", "Struct", "Temporal", "Window", "Functions", "API reference", "Input/output", "Aggregation", "polars.LazyFrame.bottom_k", "polars.LazyFrame.cache", "polars.LazyFrame.clear", "polars.LazyFrame.clone", "polars.LazyFrame.collect", "polars.LazyFrame.columns", "polars.LazyFrame.drop", "polars.LazyFrame.drop_nulls", "polars.LazyFrame.dtypes", "polars.LazyFrame.explain", "polars.LazyFrame.explode", "polars.LazyFrame.fetch", "polars.LazyFrame.fill_nan", "polars.LazyFrame.fill_null", "polars.LazyFrame.filter", "polars.LazyFrame.first", "polars.LazyFrame.from_json", "polars.LazyFrame.groupby", "polars.LazyFrame.groupby_dynamic", "polars.LazyFrame.groupby_rolling", "polars.LazyFrame.head", "polars.LazyFrame.inspect", "polars.LazyFrame.interpolate", "polars.LazyFrame.join", "polars.LazyFrame.join_asof", "polars.LazyFrame.last", "polars.LazyFrame.lazy", "polars.LazyFrame.limit", "polars.LazyFrame.map", "polars.LazyFrame.max", "polars.LazyFrame.mean", "polars.LazyFrame.median", "polars.LazyFrame.melt", "polars.LazyFrame.merge_sorted", "polars.LazyFrame.min", "polars.LazyFrame.null_count", "polars.LazyFrame.pipe", "polars.LazyFrame.profile", "polars.LazyFrame.quantile", "polars.LazyFrame.read_json", "polars.LazyFrame.rename", "polars.LazyFrame.reverse", "polars.LazyFrame.schema", "polars.LazyFrame.select", "polars.LazyFrame.set_sorted", "polars.LazyFrame.shift", "polars.LazyFrame.shift_and_fill", "polars.LazyFrame.show_graph", "polars.LazyFrame.slice", "polars.LazyFrame.sort", "polars.LazyFrame.std", "polars.LazyFrame.sum", "polars.LazyFrame.tail", "polars.LazyFrame.take_every", "polars.LazyFrame.top_k", "polars.LazyFrame.unique", "polars.LazyFrame.unnest", "polars.LazyFrame.update", "polars.LazyFrame.var", "polars.LazyFrame.width", "polars.LazyFrame.with_columns", "polars.LazyFrame.with_context", "polars.LazyFrame.with_row_count", "polars.LazyFrame.write_json", "polars.lazyframe.groupby.LazyGroupBy.agg", "polars.lazyframe.groupby.LazyGroupBy.all", "polars.lazyframe.groupby.LazyGroupBy.apply", "polars.lazyframe.groupby.LazyGroupBy.count", "polars.lazyframe.groupby.LazyGroupBy.first", "polars.lazyframe.groupby.LazyGroupBy.head", "polars.lazyframe.groupby.LazyGroupBy.last", "polars.lazyframe.groupby.LazyGroupBy.max", "polars.lazyframe.groupby.LazyGroupBy.mean", "polars.lazyframe.groupby.LazyGroupBy.median", "polars.lazyframe.groupby.LazyGroupBy.min", "polars.lazyframe.groupby.LazyGroupBy.n_unique", "polars.lazyframe.groupby.LazyGroupBy.quantile", "polars.lazyframe.groupby.LazyGroupBy.sum", "polars.lazyframe.groupby.LazyGroupBy.tail", "Attributes", "Descriptive", "GroupBy", "LazyFrame", "Miscellaneous", "Manipulation/selection", "Selectors", "Aggregation", "polars.Series.abs", "polars.Series.alias", "polars.Series.all", "polars.Series.any", "polars.Series.append", "polars.Series.apply", "polars.Series.arccos", "polars.Series.arccosh", "polars.Series.arcsin", "polars.Series.arcsinh", "polars.Series.arctan", "polars.Series.arctanh", "polars.Series.arg_max", "polars.Series.arg_min", "polars.Series.arg_sort", "polars.Series.arg_true", "polars.Series.arg_unique", "polars.Series.arr.max", "polars.Series.arr.min", "polars.Series.arr.sum", "polars.Series.arr.unique", "polars.Series.bin.contains", "polars.Series.bin.decode", "polars.Series.bin.encode", "polars.Series.bin.ends_with", "polars.Series.bin.starts_with", "polars.Series.bottom_k", "polars.Series.cast", "polars.Series.cat", "polars.Series.cat.get_categories", "polars.Series.cat.set_ordering", "polars.Series.cbrt", "polars.Series.ceil", "polars.Series.chunk_lengths", "polars.Series.clear", "polars.Series.clip", "polars.Series.clip_max", "polars.Series.clip_min", "polars.Series.clone", "polars.Series.cos", "polars.Series.cosh", "polars.Series.cummax", "polars.Series.cummin", "polars.Series.cumprod", "polars.Series.cumsum", "polars.Series.cumulative_eval", "polars.Series.cut", "polars.Series.describe", "polars.Series.diff", "polars.Series.dot", "polars.Series.drop_nans", "polars.Series.drop_nulls", "polars.Series.dt", "polars.Series.dt.base_utc_offset", "polars.Series.dt.cast_time_unit", "polars.Series.dt.combine", "polars.Series.dt.convert_time_zone", "polars.Series.dt.date", "polars.Series.dt.datetime", "polars.Series.dt.day", "polars.Series.dt.days", "polars.Series.dt.dst_offset", "polars.Series.dt.epoch", "polars.Series.dt.hour", "polars.Series.dt.hours", 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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, 428, 435, 437, 439, 475, 476, 482, 483, 484, 485, 486, 488, 489, 490, 497, 517, 534, 547, 549, 553, 558, 567, 569, 580, 592, 596, 600, 601, 603, 604, 612, 615, 621, 630, 638, 670, 671, 676, 734, 737, 756, 757, 766, 768, 773, 774, 775, 776, 782, 783, 786, 787, 835, 839, 863, 869, 876, 888, 911, 918, 919, 943, 959, 966, 987, 1004, 1018, 1028, 1030, 1031, 1032, 1036, 1049, 1057], "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, 436, 454, 456, 460, 461, 463, 467, 468, 471, 473, 482, 483, 484, 485, 486, 488, 489, 490, 537, 545, 546, 555, 557, 562, 567, 579, 587, 613, 615, 626, 631, 632, 634, 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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, 355, 378, 387, 409, 415, 424, 428, 429, 430, 439, 466, 470, 481, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 502, 503, 521, 527, 528, 533, 534, 535, 536, 539, 569, 573, 587, 588, 600, 601, 612, 615, 616, 617, 621, 626, 627, 629, 630, 638, 649, 659, 661, 669, 670, 671, 675, 676, 680, 684, 695, 700, 707, 712, 715, 718, 734, 743, 744, 835, 854, 867, 892, 898, 907, 911, 912, 913, 919, 946, 947, 948, 949, 950, 951, 953, 954, 955, 957, 961, 968, 972, 973, 991, 997, 998, 1003, 1004, 1005, 1006, 1009, 1030, 1049, 1057], "string": [5, 7, 9, 12, 13, 14, 18, 28, 29, 31, 33, 34, 38, 52, 58, 66, 75, 97, 101, 102, 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, 416, 439, 450, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 508, 509, 512, 513, 514, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 542, 563, 564, 565, 566, 572, 575, 577, 578, 584, 585, 587, 588, 595, 605, 606, 609, 610, 618, 621, 622, 623, 626, 627, 629, 638, 652, 661, 668, 669, 670, 671, 676, 695, 701, 706, 712, 715, 716, 734, 737, 763, 769, 821, 825, 828, 831, 832, 844, 860, 899, 977, 978, 979, 983, 984, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1008, 1010, 1011, 1012, 1028, 1049, 1057], "classmethod": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 375, 638, 668, 691, 734], "activ": [6, 10, 16, 17, 19, 20, 21, 22, 25, 292, 410, 516, 638, 893, 986], "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, 410, 425, 428, 431, 436, 437, 443, 444, 445, 446, 450, 460, 461, 463, 470, 473, 475, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 492, 494, 497, 498, 502, 504, 505, 509, 511, 513, 523, 524, 525, 529, 530, 532, 534, 535, 536, 539, 544, 556, 559, 562, 563, 564, 565, 566, 567, 569, 572, 573, 579, 582, 587, 588, 600, 601, 603, 612, 615, 621, 626, 627, 630, 638, 652, 654, 656, 661, 663, 665, 669, 670, 671, 675, 676, 680, 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117, 215, 225, 226, 254, 268, 309, 345, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 591, 638, 709, 734, 784, 825, 856, 936, 1049, 1057], "consid": [6, 101, 102, 112, 117, 133, 142, 153, 179, 196, 223, 225, 226, 254, 268, 298, 299, 300, 309, 345, 437, 482, 483, 484, 485, 486, 488, 489, 490, 582, 594, 638, 659, 672, 679, 684, 707, 709, 718, 734, 744, 774, 775, 776, 784, 825, 856, 869, 936, 959, 960, 961, 1049], "break": [6, 117, 225, 226, 254, 268, 309, 310, 345, 470, 482, 483, 484, 485, 486, 488, 489, 490, 638, 709, 734, 784, 785, 825, 856, 936, 1049], "chang": [6, 26, 67, 101, 102, 117, 225, 226, 227, 254, 268, 309, 312, 345, 465, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 638, 709, 734, 743, 784, 825, 856, 932, 936, 967, 1049], "current": [6, 9, 26, 54, 91, 97, 103, 129, 132, 135, 136, 172, 254, 324, 345, 429, 465, 638, 649, 654, 655, 675, 734, 737, 773, 777, 792, 800, 825, 932, 1049, 1056], "alpha": [6, 73, 268, 360, 361, 362, 638, 656, 661, 663, 689, 699, 734, 840, 841, 842, 1049], "state": [6, 8, 73, 83, 129, 582, 649, 656, 661, 663, 689, 699, 734], "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, 452, 494, 518, 638, 691, 699, 715, 734, 962, 988, 1049], "previous": 7, "save": [7, 158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 649, 670, 671, 676, 734, 800, 821, 825, 832], "share": [7, 58, 144, 254, 839, 1049], "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, 481, 482, 483, 484, 485, 486, 488, 489, 490, 502, 528, 603, 621, 629, 638, 649, 675, 676, 684, 734, 737, 876, 880, 946, 947, 948, 949, 950, 951, 953, 954, 955, 972, 998, 1027, 1049], "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, 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1005, 1006, 1009, 1037, 1040, 1049, 1056, 1057], "json": [7, 9, 33, 34, 108, 109, 115, 254, 375, 452, 517, 518, 638, 649, 668, 691, 715, 734, 987, 988], "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, 254, 452, 663, 691, 715, 734, 743, 845, 1049], "produc": [7, 123, 124, 126, 407, 447, 587, 588, 604, 680, 734, 890], "filepath": 7, "same": [7, 18, 30, 31, 56, 58, 67, 73, 74, 75, 106, 112, 122, 130, 133, 140, 157, 197, 207, 223, 231, 234, 236, 254, 260, 267, 443, 446, 464, 481, 482, 483, 484, 485, 486, 488, 489, 490, 505, 544, 549, 576, 587, 638, 658, 669, 680, 701, 707, 712, 716, 734, 739, 743, 744, 794, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1040, 1049], "reset": [8, 130], "default": [8, 13, 26, 27, 28, 30, 31, 32, 33, 34, 35, 48, 67, 74, 90, 92, 93, 94, 95, 96, 97, 101, 102, 105, 108, 109, 110, 112, 121, 122, 123, 124, 125, 126, 142, 144, 157, 159, 160, 170, 172, 173, 179, 185, 187, 195, 196, 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675, 680, 734, 880, 1049], "esp": 13, "when": [13, 15, 17, 21, 31, 35, 38, 48, 57, 58, 75, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 101, 102, 106, 110, 112, 113, 114, 115, 124, 126, 133, 134, 143, 144, 146, 148, 152, 158, 159, 172, 186, 195, 197, 200, 207, 215, 217, 221, 223, 254, 298, 299, 300, 344, 352, 360, 361, 362, 368, 429, 437, 439, 448, 466, 480, 504, 505, 521, 527, 533, 534, 572, 582, 587, 594, 638, 652, 665, 670, 671, 675, 680, 695, 701, 706, 707, 734, 743, 774, 775, 776, 824, 832, 839, 840, 841, 842, 845, 848, 919, 945, 960, 961, 991, 997, 1003, 1004, 1026, 1049], "have": [13, 26, 31, 38, 67, 75, 91, 101, 102, 103, 105, 124, 126, 146, 159, 170, 190, 196, 197, 214, 254, 309, 360, 361, 362, 448, 470, 473, 534, 536, 587, 638, 671, 684, 699, 714, 734, 737, 743, 784, 840, 841, 842, 845, 853, 936, 938, 1004, 1006, 1049, 1057], "larg": [13, 67, 158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], 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182, 185, 195, 196, 200, 209, 213, 226, 231, 254, 263, 286, 318, 358, 359, 366, 373, 376, 377, 383, 398, 436, 439, 454, 456, 460, 461, 467, 509, 524, 525, 545, 555, 563, 564, 565, 566, 569, 573, 575, 577, 578, 584, 585, 586, 587, 588, 589, 600, 601, 603, 605, 606, 609, 610, 612, 615, 618, 621, 622, 623, 625, 626, 627, 630, 638, 665, 695, 709, 712, 734, 737, 760, 794, 846, 860, 960, 961, 979, 994, 995, 1049, 1056], "left": [14, 31, 54, 67, 119, 120, 158, 159, 172, 173, 226, 254, 360, 361, 362, 382, 383, 482, 483, 484, 485, 486, 488, 489, 490, 493, 520, 542, 553, 575, 582, 583, 587, 588, 594, 614, 626, 627, 629, 638, 670, 671, 675, 676, 709, 734, 840, 841, 842, 860, 958, 990, 1012, 1032, 1049], "center": [14, 31, 254, 360, 361, 362, 481, 482, 483, 484, 485, 486, 488, 489, 490, 638, 840, 841, 842, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1049], "right": [14, 16, 31, 101, 102, 119, 120, 158, 159, 172, 173, 254, 310, 360, 361, 362, 383, 409, 415, 430, 470, 482, 483, 484, 485, 486, 488, 489, 490, 493, 502, 526, 575, 587, 588, 626, 627, 638, 670, 671, 675, 676, 734, 785, 840, 841, 842, 860, 892, 898, 913, 936, 958, 972, 996, 1049], "cell": [14, 31, 254], "align": [14, 31, 67, 74, 254, 542, 1012], "keyerror": [14, 18], "recognis": [14, 18, 121], "column_abc": 14, "column_xyz": 14, "visibl": [15, 144, 254, 839, 1049], "eg": [15, 23, 31, 103, 254, 345, 534, 536, 555, 638, 1004, 1006], "low": [15, 128], "rang": [15, 31, 103, 139, 144, 158, 171, 254, 310, 311, 322, 336, 342, 343, 345, 352, 353, 382, 470, 569, 577, 586, 587, 588, 589, 600, 601, 625, 626, 627, 638, 670, 734, 785, 786, 798, 816, 822, 823, 825, 832, 833, 839, 932, 936, 1049], "100": [15, 31, 93, 96, 101, 102, 112, 115, 254, 517, 537, 542, 734, 947, 948, 950, 987, 1007, 1049, 1057], "98": [15, 164, 254, 291, 504, 537, 549, 554, 638, 1007], "99": [15, 31, 147, 148, 164, 167, 254, 262, 291, 366, 368, 504, 549, 554, 638, 664, 665, 734, 838, 846, 1049], "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, 414, 423, 424, 427, 433, 465, 466, 470, 503, 542, 548, 562, 575, 582, 587, 591, 594, 596, 598, 603, 604, 624, 627, 638, 656, 670, 671, 672, 674, 679, 688, 689, 695, 703, 704, 712, 716, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 737, 744, 774, 785, 787, 792, 794, 800, 824, 832, 835, 838, 855, 867, 882, 891, 897, 906, 907, 910, 916, 932, 960, 961, 1019, 1049, 1057], "95": [15, 262, 638], "96": [15, 262, 638], "97": [15, 164, 254, 262, 638], "move": [16, 197, 254, 360, 361, 362, 482, 483, 485, 489, 638, 840, 841, 842, 947, 948, 950, 954, 1049], "inlin": [16, 197, 254, 509, 515, 516, 524, 979, 985, 986, 994], "parenthes": 16, "below": [17, 31, 104, 113, 142, 254, 368, 587, 588, 629, 638, 659, 734], "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, 510, 515, 518, 638, 980, 985, 988], "rounded_corn": 18, "style": [18, 31, 187, 254], "border": 18, "line": [18, 31, 101, 102, 105, 112, 156, 166, 168, 254, 515, 985], "includ": [18, 26, 28, 30, 31, 72, 104, 113, 124, 134, 139, 144, 158, 185, 197, 221, 222, 225, 254, 310, 346, 383, 470, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 527, 529, 530, 533, 582, 616, 617, 638, 652, 670, 706, 734, 785, 786, 826, 839, 860, 936, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 999, 1000, 1003, 1039, 1049], "divid": [18, 345, 352, 360, 361, 362, 396, 638, 825, 832, 840, 841, 842, 880, 1049], "dens": [18, 156, 254, 473, 638, 938, 1049], "space": [18, 158, 254, 470, 638, 670, 734, 936, 1049], "horizont": [18, 74, 152, 163, 225, 254, 563, 564, 565, 566, 577, 578, 582, 583, 584, 585, 591, 594, 605, 606, 609, 610, 614, 622, 623], "markdown": 18, "compat": [18, 31, 35, 48, 254, 509, 510, 515, 516, 524, 525, 734, 737, 979, 980, 985, 986, 994, 995], "No": [18, 540, 1010], "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, 437, 466, 481, 482, 483, 485, 489, 524, 534, 535, 536, 539, 562, 582, 583, 594, 604, 614, 629, 638, 672, 679, 680, 688, 734, 796, 797, 798, 802, 804, 805, 809, 811, 814, 816, 819, 822, 823, 826, 829, 833, 834, 836, 841, 842, 946, 947, 948, 950, 954, 994, 1004, 1005, 1006, 1009, 1049], "round": [18, 31, 69, 97, 254, 297, 372, 551, 638, 771, 851, 1049], "corner": [18, 31, 97, 254], "op": [18, 126, 254, 292, 476, 534, 536, 638, 734, 1004, 1006, 1049], "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, 429, 434, 481, 504, 516, 549, 587, 619, 622, 629, 638, 665, 666, 669, 670, 671, 684, 696, 713, 717, 734, 743, 848, 876, 912, 917, 946, 986, 1013, 1049], "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, 431, 434, 437, 463, 492, 502, 516, 534, 535, 536, 556, 619, 622, 629, 638, 684, 707, 734, 737, 744, 759, 774, 775, 776, 786, 876, 880, 914, 957, 972, 986, 1004, 1005, 1006, 1034, 1049, 1057], "semigraph": 18, "box": [18, 133, 254], "draw": [18, 23, 24, 123, 492, 498, 638, 1057], "found": [18, 28, 54, 77, 86, 88, 93, 97, 143, 226, 254, 493, 518, 534, 536, 638, 709, 734, 958, 988, 1004, 1006, 1049, 1056], "unicod": 18, "block": [18, 157, 223, 254, 669, 692, 707, 714, 718, 734, 960, 961, 1049], "http": [18, 31, 91, 103, 132, 138, 254, 515, 985], "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, 737, 960, 961, 1049], "inform": [21, 72, 104, 113, 138, 254, 298, 299, 300, 396, 502, 509, 515, 516, 524, 587, 588, 638, 689, 734, 774, 775, 776, 880, 972, 979, 985, 986, 994, 1049], "separ": [22, 28, 99, 101, 102, 112, 185, 187, 215, 222, 224, 254, 268, 411, 416, 514, 578, 582, 583, 638, 708, 734, 894, 899, 984, 1017, 1026, 1049], "between": [22, 74, 121, 122, 124, 126, 138, 189, 246, 254, 293, 313, 383, 409, 415, 416, 430, 465, 470, 471, 486, 492, 498, 508, 570, 571, 579, 581, 613, 616, 617, 638, 690, 728, 734, 766, 788, 860, 892, 898, 899, 913, 932, 936, 937, 951, 978, 1049], "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, 429, 464, 473, 482, 494, 531, 606, 619, 638, 665, 669, 670, 671, 734, 774, 775, 780, 786, 848, 912, 938, 947, 962, 1001, 1049], "both": [23, 28, 58, 158, 159, 172, 173, 180, 195, 254, 267, 383, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 648, 670, 671, 675, 676, 685, 734, 860, 869, 1049], "tbl_row": 23, "char": [24, 58, 75, 516, 522, 986, 992], "enabl": [25, 75, 129, 200, 231, 254, 494, 638, 695, 712, 734, 962, 1049], "addit": [25, 30, 31, 93, 104, 113, 122, 140, 145, 157, 185, 200, 201, 207, 224, 231, 234, 254, 261, 324, 363, 366, 464, 505, 509, 515, 516, 524, 563, 565, 572, 575, 576, 577, 578, 584, 592, 605, 609, 618, 621, 622, 638, 658, 662, 669, 695, 696, 701, 708, 712, 716, 734, 792, 800, 846, 979, 985, 986, 994, 1049], "verbos": [25, 130, 516, 986], "debug": [25, 656, 663, 680, 734, 1057], "log": [25, 69, 291, 357, 434, 456, 467, 554, 638, 765, 838, 1033, 1049], "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, 439, 470, 480, 638, 692, 694, 734, 919, 1049], "show": [26, 31, 56, 142, 156, 174, 184, 254, 659, 689, 699, 734], "variabl": [26, 49, 54, 125, 128, 179, 215, 254, 684, 734, 1026, 1049, 1056], "restrict": [26, 531, 587, 588, 1001], "dictionari": [26, 31, 90, 92, 93, 94, 96, 101, 102, 107, 108, 109, 111, 112, 170, 185, 195, 196, 197, 213, 214, 254, 439, 638, 734, 919, 1049], "those": [26, 31, 101, 197, 254, 473, 515, 638, 737, 938, 985, 1049], "been": [26, 31, 254, 473, 482, 483, 484, 485, 486, 488, 489, 490, 569, 638, 938, 1049], "set_fmt_float": 26, "directli": [26, 54, 124, 126, 130, 197, 254, 360, 361, 362, 615, 638, 734, 840, 841, 842, 1049, 1057], "via": [26, 101, 102, 105, 112, 114, 115, 116, 170, 196, 254, 268, 638], "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, 734], "avrocompress": [27, 254], "uncompress": [27, 32, 35, 48, 106, 114, 254, 734], "write": [27, 28, 29, 30, 31, 32, 33, 35, 48, 102, 106, 130, 254, 298, 299, 300, 452, 638, 678, 699, 715, 734, 774, 775, 776, 1049], "apach": [27, 35, 100, 103, 254], "avro": [27, 100, 254, 649], "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, 410, 470, 481, 482, 483, 484, 485, 486, 488, 489, 490, 493, 502, 576, 592, 599, 603, 616, 617, 629, 638, 652, 658, 670, 671, 675, 676, 680, 699, 701, 706, 708, 715, 734, 737, 744, 769, 784, 785, 821, 825, 832, 835, 893, 936, 946, 947, 948, 949, 950, 951, 953, 954, 955, 958, 972, 1049], "snappi": [27, 35, 48, 254, 734], "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, 482, 483, 485, 488, 489, 490, 570, 571, 587, 588, 590, 626, 627, 638, 649, 670, 676, 680, 734, 778, 792, 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, 864, 868, 870, 871, 876, 946, 970, 1022, 1049, 1057], "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, 412, 413, 417, 418, 477, 495, 496, 508, 510, 512, 514, 516, 521, 527, 529, 531, 533, 538, 541, 548, 550, 568, 574, 576, 579, 580, 581, 593, 597, 598, 602, 605, 607, 608, 609, 611, 613, 618, 620, 624, 628, 629, 638, 657, 658, 659, 660, 666, 673, 674, 675, 687, 692, 694, 695, 707, 708, 711, 713, 715, 734, 737, 768, 774, 821, 899, 942, 980, 982, 984, 986, 1001, 1008, 1011, 1049], "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, 418, 502, 512, 514, 529, 531, 568, 574, 576, 579, 580, 581, 593, 598, 602, 605, 607, 608, 609, 611, 618, 620, 624, 628, 629, 638, 657, 658, 659, 660, 666, 673, 674, 675, 687, 692, 694, 695, 707, 708, 711, 715, 734, 737, 768, 899, 972, 982, 984, 1001, 1049], "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, 576, 657, 658, 659, 660, 666, 675, 687, 692, 694, 695, 707, 713, 734, 768], "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, 466, 497, 510, 516, 530, 534, 535, 536, 575, 596, 638, 670, 675, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 801, 828, 831, 860, 980, 986, 1000, 1004, 1005, 1006, 1049], "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, 432, 439, 482, 483, 484, 485, 486, 488, 489, 490, 497, 502, 592, 629, 638, 670, 671, 676, 678, 692, 734, 737, 821, 825, 832, 838, 860, 959, 972, 1030, 1049], "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, 734], "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, 649, 734], "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, 429, 437, 464, 470, 477, 481, 482, 483, 484, 485, 486, 488, 489, 490, 494, 496, 529, 530, 536, 555, 569, 582, 583, 587, 588, 604, 612, 615, 616, 617, 630, 638, 653, 670, 671, 675, 680, 689, 698, 715, 716, 734, 743, 744, 828, 831, 840, 841, 842, 845, 880, 936, 942, 946, 947, 948, 949, 950, 951, 953, 954, 955, 962, 965, 999, 1000, 1006, 1030, 1049, 1056, 1057], "If": [28, 29, 30, 31, 32, 33, 34, 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, 424, 429, 437, 439, 450, 464, 470, 473, 475, 477, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 492, 493, 498, 502, 503, 517, 519, 521, 522, 527, 528, 529, 530, 531, 533, 534, 535, 536, 539, 563, 565, 567, 569, 573, 579, 580, 582, 584, 587, 588, 594, 600, 601, 603, 605, 609, 612, 615, 616, 617, 621, 622, 626, 627, 629, 630, 638, 652, 659, 661, 670, 671, 675, 676, 680, 684, 692, 700, 706, 707, 709, 715, 718, 734, 737, 743, 744, 774, 775, 776, 785, 794, 817, 818, 832, 845, 853, 855, 856, 879, 880, 882, 907, 912, 936, 938, 940, 942, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 957, 958, 962, 968, 972, 973, 987, 989, 991, 992, 997, 998, 999, 1000, 1001, 1003, 1004, 1005, 1006, 1009, 1019, 1025, 1030, 1039, 1049, 1056], "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, 470, 482, 483, 484, 485, 486, 488, 489, 490, 519, 521, 527, 533, 563, 565, 569, 573, 584, 587, 588, 600, 601, 605, 609, 612, 615, 621, 622, 626, 627, 630, 638, 664, 670, 671, 676, 695, 712, 715, 734, 737, 753, 785, 821, 825, 832, 936, 960, 961, 989, 991, 997, 1003, 1031, 1049, 1056], "whether": [28, 94, 96, 126, 134, 201, 221, 254, 310, 328, 346, 401, 402, 444, 445, 470, 494, 638, 652, 680, 696, 701, 706, 734, 737, 785, 804, 826, 884, 885, 936, 1049, 1056], "header": [28, 31, 35, 48, 97, 101, 102, 105, 112, 143, 187, 222, 254, 734], "field": [28, 59, 86, 88, 93, 217, 224, 254, 429, 439, 479, 516, 517, 530, 531, 544, 582, 583, 604, 621, 638, 708, 734, 785, 912, 936, 944, 998, 1000, 1001, 1015, 1017, 1049], "symbol": [28, 254], "byte": [28, 48, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 144, 254, 286, 289, 290, 519, 522, 734, 760, 763, 764, 839, 989, 992, 1049], "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, 439, 464, 476, 482, 483, 484, 485, 486, 488, 489, 490, 505, 520, 526, 563, 565, 572, 575, 576, 577, 578, 584, 587, 588, 592, 605, 609, 618, 621, 622, 626, 627, 638, 652, 658, 662, 665, 669, 670, 671, 675, 695, 696, 701, 706, 708, 712, 716, 734, 839, 840, 841, 842, 848, 990, 996, 1049], "defin": [28, 31, 38, 121, 122, 124, 133, 158, 159, 183, 186, 236, 254, 268, 383, 428, 466, 480, 482, 483, 484, 485, 486, 488, 489, 490, 567, 587, 588, 603, 621, 626, 627, 638, 670, 671, 688, 718, 734, 737, 744, 860, 911, 945, 1049], "chrono": [28, 254, 348, 351, 534, 535, 536, 539, 828, 831, 1004, 1005, 1006, 1009], "rust": [28, 35, 83, 106, 110, 133, 236, 254, 744, 1049], "crate": [28, 254, 509, 510, 515, 516, 524, 525, 534, 535, 536, 539, 737, 979, 980, 985, 986, 994, 995, 1004, 1005, 1006, 1009], "fraction": [28, 119, 120, 198, 254, 346, 465, 492, 534, 536, 638, 826, 932, 957, 1004, 1006, 1049], "second": [28, 123, 158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 534, 536, 589, 590, 625, 629, 638, 670, 671, 676, 734, 737, 821, 825, 832, 1004, 1006, 1057], "precis": [28, 31, 38, 39, 170, 196, 197, 214, 254, 317, 537, 737, 793, 1007], "infer": [28, 90, 92, 93, 94, 95, 96, 101, 102, 105, 108, 109, 112, 115, 133, 254, 477, 517, 534, 535, 536, 537, 539, 615, 638, 734, 942, 987, 1004, 1005, 1006, 1007, 1009, 1049], "maximum": [28, 101, 102, 112, 122, 123, 124, 126, 176, 254, 403, 440, 458, 473, 605, 606, 638, 681, 734, 774, 806, 886, 920, 928, 933, 938, 1049], "timeunit": [28, 38, 40, 254, 317, 318, 350, 355, 536, 587, 588, 737, 793, 794, 830, 835, 1006], "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, 654, 685, 688, 734, 737, 773, 1049, 1056, 1057], "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, 482, 483, 484, 485, 486, 488, 489, 490, 534, 536, 553, 587, 588, 590, 596, 603, 626, 627, 638, 670, 671, 676, 734, 737, 791, 792, 793, 794, 795, 796, 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, 876, 889, 890, 928, 929, 961, 1004, 1006, 1032, 1049], "place": [28, 134, 141, 146, 163, 164, 187, 192, 197, 203, 204, 207, 221, 229, 254, 278, 416, 423, 495, 496, 504, 638, 652, 697, 698, 701, 706, 734, 743, 753, 845, 899, 906, 939, 941, 964, 965, 974, 1049], "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, 472, 500, 501, 551, 552, 576, 592, 612, 615, 630, 638, 660, 694, 712, 734, 737, 826, 1031, 1036, 1049], "repres": [28, 50, 65, 90, 92, 94, 95, 96, 208, 228, 233, 254, 389, 391, 507, 560, 563, 576, 579, 592, 603, 616, 617, 620, 628, 638, 702, 710, 734, 961, 976, 1038, 1049], "empti": [28, 81, 93, 101, 102, 105, 112, 135, 136, 158, 167, 179, 254, 603, 618, 654, 655, 670, 684, 734, 737, 773, 777, 863, 1049], "table_nam": [29, 31, 254], "connect": [29, 101, 103, 106, 110, 114, 116, 117, 254, 650], "if_exist": [29, 254], "dbwritemod": [29, 254], "fail": [29, 30, 91, 104, 106, 109, 113, 132, 223, 254, 279, 349, 429, 534, 535, 536, 539, 638, 707, 734, 744, 1004, 1005, 1006, 1009, 1049], "dbwriteengin": [29, 254], "sqlalchemi": [29, 254], "databas": [29, 103, 254, 649], "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, 429, 474, 482, 483, 485, 488, 489, 490, 528, 559, 577, 586, 587, 588, 589, 590, 625, 626, 627, 638, 654, 655, 661, 670, 671, 712, 734, 773, 777, 790, 794, 825, 832, 930, 939, 998, 1030, 1049, 1056, 1057], "append": [29, 30, 124, 146, 172, 173, 254, 310, 470, 474, 587, 588, 629, 638, 675, 676, 734, 845, 1049], "your": [29, 31, 67, 101, 102, 119, 120, 133, 170, 196, 197, 200, 214, 231, 234, 236, 254, 268, 534, 535, 536, 567, 638, 656, 672, 679, 680, 695, 712, 716, 718, 734, 744, 1004, 1005, 1006, 1049, 1057], "special": [29, 101, 102, 112, 254, 516, 744, 986, 1049], "uri": [29, 30, 103, 104, 113, 254], "postgresql": [29, 103, 254, 464, 638], "user": [29, 103, 133, 186, 236, 254, 268, 437, 466, 494, 567, 587, 638, 688, 718, 734, 744, 962, 1049], "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, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 521, 527, 533, 563, 565, 572, 576, 584, 587, 596, 605, 609, 621, 622, 638, 652, 658, 669, 670, 671, 673, 680, 688, 695, 699, 701, 706, 712, 716, 718, 734, 744, 801, 824, 846, 855, 882, 991, 997, 1003, 1019, 1049], "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, 439, 525, 569, 638, 664, 712, 734, 794, 824, 919, 960, 961, 995, 1049], "insert": [29, 101, 102, 106, 110, 112, 114, 115, 116, 164, 192, 222, 224, 254, 493, 508, 542, 638, 708, 734, 958, 978, 1012, 1049], "mode": [29, 30, 52, 254, 516, 612, 615, 630, 638, 734, 986, 1049, 1056], "new": [29, 30, 31, 112, 130, 133, 142, 163, 164, 183, 184, 191, 192, 211, 222, 224, 225, 231, 254, 263, 318, 365, 382, 438, 524, 525, 530, 531, 543, 544, 550, 569, 638, 649, 659, 692, 705, 708, 712, 718, 734, 740, 790, 794, 844, 930, 941, 994, 995, 1000, 1001, 1013, 1015, 1021, 1027, 1049, 1056], "alreadi": [29, 30, 254, 309, 410, 638, 784, 893, 1049], "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, 458, 459, 638, 787, 824, 840, 841, 842, 891, 928, 929, 1049], "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, 488, 490, 507, 560, 579, 616, 617, 620, 628, 638, 649, 702, 710, 734, 953, 955, 976, 1038, 1049], "like": [30, 91, 100, 101, 102, 105, 106, 107, 108, 109, 110, 111, 112, 166, 168, 172, 217, 254, 316, 410, 451, 473, 515, 587, 588, 622, 626, 627, 638, 663, 691, 734, 743, 792, 893, 938, 985, 1049], "categor": [30, 58, 75, 172, 215, 216, 254, 294, 295, 439, 553, 638, 737, 767, 768, 769, 785, 936, 1032, 1049], "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, 587, 588, 603, 626, 627, 649, 670, 671, 691, 734, 737, 767, 791, 883, 977, 1025, 1049], "handl": [30, 74, 97, 101, 102, 112, 117, 254, 312, 408, 542, 638, 787, 891, 1012, 1049], "throw": [30, 91, 254, 293, 517, 518, 638, 766, 987, 988, 1049], "add": [30, 31, 102, 133, 146, 158, 231, 232, 254, 468, 546, 590, 594, 629, 638, 670, 675, 712, 713, 714, 734, 743, 845, 1049], "anyth": [30, 195, 254, 292, 516, 638, 986], "updat": [30, 254, 734], "extra": [30, 35, 48, 101, 104, 105, 106, 110, 113, 114, 116, 146, 158, 254, 670, 734, 743, 845, 1049], "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, 437, 502, 509, 515, 516, 524, 587, 588, 614, 638, 737, 774, 775, 776, 880, 972, 979, 985, 986, 994, 1049, 1057], "here": [30, 31, 90, 92, 93, 94, 96, 103, 104, 108, 109, 113, 122, 124, 126, 254, 518, 734, 988], "gc": [30, 104, 113, 254], "azur": [30, 104, 113, 254], "keyword": [30, 55, 104, 110, 113, 138, 186, 195, 200, 231, 234, 254, 466, 618, 621, 638, 688, 695, 712, 716, 734, 1049], "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, 464, 466, 482, 483, 484, 485, 486, 488, 489, 490, 505, 521, 527, 533, 563, 565, 570, 571, 572, 575, 576, 577, 578, 584, 587, 592, 605, 609, 615, 618, 621, 622, 626, 638, 658, 662, 669, 670, 671, 688, 695, 696, 701, 708, 712, 716, 734, 743, 825, 832, 860, 991, 997, 1003, 1031, 1049], "while": [30, 102, 104, 105, 113, 124, 126, 170, 179, 222, 254, 684, 734], "lake": [30, 104, 113, 254, 649], "instanti": [30, 31, 200, 231, 254, 695, 712, 734], "basic": [30, 31, 254, 1057], "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, 429, 439, 557, 587, 593, 602, 638, 663, 675, 680, 707, 712, 734, 743, 835, 845, 853, 919, 967, 1039, 1049], "match": [30, 31, 38, 74, 84, 90, 92, 93, 94, 96, 108, 109, 119, 120, 148, 173, 195, 254, 445, 487, 509, 510, 513, 515, 516, 517, 518, 524, 525, 532, 534, 535, 536, 576, 638, 665, 676, 734, 737, 869, 876, 952, 979, 980, 983, 985, 986, 987, 988, 994, 995, 1002, 1004, 1005, 1006, 1049], "version": [30, 72, 104, 113, 118, 254, 292, 337, 338, 534, 536, 569, 587, 614, 615, 626, 638, 743, 817, 818, 1004, 1006, 1049], "old": [30, 191, 254, 692, 734], "existing_table_path": [30, 254], "store": [30, 101, 110, 146, 170, 196, 254, 294, 743, 768, 845, 1049], "bucket": [30, 104, 113, 254, 345, 352, 825, 832, 856, 1049], "prefix": [30, 130, 254, 263, 290, 438, 532, 542, 546, 638, 737, 764, 1002, 1012], "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, 464, 470, 505, 523, 563, 565, 570, 571, 572, 575, 576, 577, 578, 584, 592, 605, 609, 618, 621, 622, 638, 658, 662, 669, 695, 696, 701, 708, 712, 716, 734, 840, 841, 842, 936, 993, 1049], "tupl": [31, 103, 133, 170, 195, 196, 197, 202, 233, 254, 477, 638, 689, 699, 734, 737, 942, 1049], "a1": [31, 68, 70, 254], "table_styl": [31, 254], "column_format": [31, 254], "dtype_format": [31, 254], "oneormoredatatyp": [31, 122, 254, 876, 1049], "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, 429, 437, 466, 505, 544, 567, 572, 582, 583, 594, 596, 604, 614, 619, 638, 652, 662, 675, 676, 688, 701, 706, 707, 708, 709, 734, 743, 786, 788, 845, 912, 961, 1015, 1049], "formula": [31, 254, 357, 638, 838, 1049], "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, 649], "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, 309, 448, 482, 483, 484, 485, 486, 488, 489, 490, 554, 567, 569, 638, 670, 671, 718, 734, 765, 773, 784, 786, 853, 866, 1033, 1049], "close": [31, 158, 159, 254, 383, 434, 482, 483, 484, 485, 486, 488, 489, 490, 502, 587, 588, 626, 627, 638, 670, 671, 734, 860, 972, 1049], "xlsx": [31, 105, 254], "work": [31, 39, 102, 105, 192, 254, 268, 284, 297, 298, 299, 300, 363, 372, 410, 431, 464, 480, 522, 556, 638, 759, 771, 774, 775, 776, 851, 893, 914, 992, 1034, 1049], "directori": [31, 110, 254], "sheet1": [31, 254], "valid": [31, 38, 52, 106, 110, 126, 130, 144, 172, 254, 309, 509, 510, 515, 516, 518, 524, 525, 587, 588, 638, 675, 734, 737, 784, 839, 853, 979, 980, 985, 986, 988, 994, 995, 1049], "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, 463, 470, 475, 482, 483, 484, 485, 486, 487, 488, 489, 490, 523, 562, 569, 596, 600, 601, 615, 638, 670, 671, 734, 737, 771, 826, 851, 869, 936, 940, 952, 961, 993, 1049, 1056, 1057], "medium": [31, 254], "kei": [31, 67, 72, 74, 158, 170, 172, 173, 180, 185, 187, 191, 194, 196, 197, 254, 621, 670, 675, 676, 685, 692, 693, 734], "follow": [31, 72, 101, 102, 104, 112, 113, 133, 158, 159, 173, 186, 227, 254, 268, 341, 345, 352, 466, 473, 482, 483, 484, 485, 486, 487, 488, 489, 490, 544, 555, 567, 587, 629, 631, 632, 634, 638, 639, 640, 644, 645, 646, 670, 671, 676, 688, 734, 821, 825, 832, 938, 960, 961, 1041, 1043, 1045, 1049, 1050, 1053, 1054, 1055, 1057], "first_column": [31, 254], "last_column": [31, 254], "banded_column": [31, 254], "banded_row": [31, 254], "sheet": [31, 105, 254], "chart": [31, 254, 689, 734], "subsequ": [31, 57, 190, 218, 254, 429, 629, 661, 734], "colnam": [31, 112, 124, 143, 254, 660, 734], "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, 423, 429, 432, 464, 466, 470, 473, 476, 477, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 493, 495, 496, 516, 534, 536, 544, 567, 592, 604, 615, 616, 617, 638, 652, 662, 670, 671, 680, 688, 697, 698, 701, 706, 709, 718, 734, 737, 744, 785, 792, 795, 828, 830, 831, 838, 839, 856, 860, 879, 889, 906, 912, 915, 918, 930, 936, 938, 942, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 958, 964, 965, 986, 1004, 1006, 1036, 1040, 1049, 1056, 1057], "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, 482, 483, 485, 488, 489, 490, 534, 536, 539, 587, 590, 626, 627, 638, 670, 676, 734, 737, 792, 793, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 829, 830, 832, 833, 834, 835, 836, 1004, 1006, 1009], "overridden": [31, 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485, 489, 561, 563, 582, 584, 585, 594, 614, 623, 638, 656, 661, 663, 669, 670, 671, 687, 689, 699, 716, 734, 737, 782, 783, 838, 839, 947, 948, 950, 953, 954, 955, 1049], "must": [31, 91, 92, 104, 113, 139, 145, 158, 159, 173, 180, 195, 254, 310, 429, 437, 470, 482, 483, 484, 485, 486, 488, 489, 490, 638, 662, 670, 671, 676, 680, 685, 734, 785, 786, 936, 1049], "funcnam": [31, 254], "averag": [31, 254, 360, 361, 362, 473, 638, 840, 841, 842, 938, 1049], "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, 457, 462, 482, 483, 484, 485, 486, 488, 489, 490, 510, 557, 559, 568, 611, 638, 670, 671, 676, 687, 709, 714, 718, 727, 734, 786, 821, 856, 890, 926, 931, 980, 1035, 1037, 1049], "std_dev": [31, 254], "var": [31, 127, 254, 490, 638, 734, 1049], "pixel": [31, 254], "unit": [31, 38, 40, 124, 126, 144, 254, 317, 318, 325, 350, 355, 435, 448, 534, 536, 558, 587, 588, 596, 638, 689, 734, 737, 793, 794, 801, 830, 835, 839, 918, 1004, 1006, 1036, 1049, 1057], "hand": [31, 101, 102, 112, 254, 409, 415, 430, 892, 898, 913], "side": [31, 158, 159, 254, 383, 409, 415, 430, 482, 483, 484, 485, 486, 488, 489, 490, 493, 587, 588, 626, 627, 638, 670, 671, 734, 860, 892, 898, 913, 958, 1049], "call": [31, 56, 102, 124, 126, 130, 133, 157, 158, 159, 174, 253, 254, 268, 305, 308, 395, 468, 546, 605, 609, 638, 650, 669, 670, 671, 733, 734, 737, 744, 1049], "ad": [31, 93, 132, 158, 222, 231, 254, 267, 366, 582, 583, 638, 670, 712, 734, 846, 1049], "end": [31, 101, 102, 110, 112, 158, 254, 286, 289, 290, 316, 341, 345, 346, 363, 383, 424, 509, 513, 516, 528, 532, 569, 576, 587, 588, 592, 600, 601, 626, 627, 629, 638, 670, 672, 679, 689, 734, 737, 763, 792, 821, 825, 860, 907, 983, 986, 998, 1002, 1049], "wise": [31, 67, 152, 254, 270, 271, 272, 273, 274, 275, 301, 302, 364, 433, 499, 500, 501, 551, 552, 582, 583, 594, 605, 609, 614, 638, 745, 746, 747, 748, 749, 750, 778, 779, 843, 916, 917, 969, 970, 971, 1022, 1023, 1049], "particip": [31, 254], "distinct": [31, 126, 185, 254, 284, 431, 473, 590, 638, 759, 914, 938, 1049, 1057], "referenc": [31, 254, 544], "differ": [31, 101, 117, 119, 146, 158, 159, 170, 196, 197, 214, 222, 254, 312, 322, 341, 342, 344, 353, 359, 408, 439, 458, 459, 461, 492, 498, 534, 557, 587, 593, 596, 602, 638, 661, 670, 671, 734, 737, 743, 744, 787, 798, 821, 822, 824, 833, 845, 891, 928, 929, 959, 1004, 1049], "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, 429, 437, 511, 512, 515, 517, 518, 596, 621, 638, 649, 734, 744, 761, 762, 856, 879, 981, 982, 985, 987, 988, 1049, 1056, 1057], "intersect": [31, 254, 737], "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, 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640, 643, 644, 645, 646, 649, 650, 663, 733, 734, 737, 938, 1041, 1043, 1045, 1049, 1050, 1053, 1054, 1055], "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, 481, 638, 737, 946, 1049], "strongli": [31, 128, 133, 195, 236, 254, 268, 638, 744, 1049], "advis": [31, 174, 254], "structur": [31, 81, 85, 87, 146, 186, 197, 217, 254, 466, 638, 688, 734, 743, 845, 1049], "wherev": [31, 133, 236, 254, 268, 638, 744, 1049], "possibl": [31, 101, 133, 134, 157, 170, 196, 221, 223, 236, 254, 268, 435, 447, 531, 558, 638, 652, 669, 701, 706, 707, 734, 744, 1001, 1049], "simpl": [31, 126, 183, 254], "colx": [31, 57, 254, 737, 1057], "coli": [31, 254, 737, 1057], "after": [31, 57, 74, 93, 100, 101, 102, 106, 110, 112, 114, 115, 116, 146, 224, 253, 254, 363, 439, 464, 473, 542, 638, 708, 734, 743, 845, 919, 938, 1012, 1049], "befor": [31, 101, 112, 128, 130, 146, 158, 173, 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1057], "titl": [31, 52, 254], "explicit": [31, 112, 122, 254, 614], "integr": [31, 254, 1057], "multi_fram": [31, 254], "wb": [31, 254], "coordin": [31, 254], "advanc": [31, 254, 429, 912, 1057], "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, 1057], "white": [31, 254], "w": [31, 54, 55, 158, 197, 254, 515, 564, 566, 670, 734, 737, 985, 997], "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, 465, 472, 489, 497, 537, 583, 586, 587, 589, 604, 638, 670, 671, 672, 676, 679, 699, 704, 734, 737, 744, 785, 792, 794, 816, 825, 832, 956, 1007, 1049, 1057], "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, 480, 524, 525, 557, 559, 638, 718, 945, 1035, 1049, 1057], "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, 466, 482, 483, 485, 596, 603, 626, 638, 685, 688, 734, 737, 787, 832, 1049], "35": [31, 118, 254, 312, 638, 787, 1049], "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, 497, 587, 603, 626, 638, 670, 688, 712, 734, 737, 785, 787, 794, 799, 817, 824, 825, 832, 1049, 1057], "15": [31, 118, 123, 133, 158, 159, 164, 254, 309, 312, 338, 345, 352, 466, 489, 583, 615, 626, 638, 670, 671, 734, 737, 784, 787, 825, 832, 1049], "60": [31, 146, 254, 346, 347, 489, 534, 638, 822, 826, 827, 1004], "q3": [31, 254], "40": [31, 146, 186, 254, 345, 352, 378, 537, 638, 688, 734, 805, 832, 1007], "80": [31, 254], "q4": [31, 254], "75": [31, 139, 254, 265, 463, 482, 483, 484, 485, 488, 489, 490, 638, 786, 856, 936, 1049, 1057], "account": [31, 97, 103, 254, 341, 360, 361, 362, 638, 821, 840, 841, 842, 1049], "flavour": [31, 254], "integer_dtyp": [31, 200, 254, 695, 734, 737], "0_": [31, 254], "just": [31, 112, 179, 254, 684, 734], "unifi": [31, 254, 737], "multi": [31, 101, 102, 254, 363, 515, 605, 609, 638, 985], "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, 438, 463, 468, 476, 516, 534, 536, 546, 564, 566, 585, 606, 610, 615, 623, 638, 675, 684, 700, 734, 737, 769, 848, 986, 1004, 1006, 1049, 1056], "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, 489, 510, 516, 570, 571, 603, 626, 638, 671, 734, 737, 794, 825, 832, 980, 986, 1057], "85": [31, 254, 1057], "font": [31, 254], "consola": [31, 254], "standard": [31, 118, 208, 217, 254, 329, 361, 488, 502, 507, 518, 620, 638, 702, 734, 805, 841, 972, 976, 988, 1049, 1057], "stdev": [31, 254], "ipccompress": [32, 254], "arrow": [32, 47, 76, 90, 103, 106, 114, 170, 196, 197, 212, 214, 254, 734, 1025, 1030, 1049], "ipc": [32, 106, 107, 114, 117, 254, 649], "binari": [32, 254, 286, 288, 289, 290, 760, 763, 764], "feather": [32, 106, 114, 254, 649], "lz4": [32, 35, 47, 48, 254, 734], "zstd": [32, 35, 47, 48, 254, 734], "pretti": [33, 254], "row_ori": [33, 254], "iobas": [33, 34, 108, 109, 254, 452, 691, 715, 734], "serial": [33, 34, 254], "represent": [33, 34, 216, 254, 295, 322, 326, 329, 330, 332, 334, 336, 339, 342, 343, 346, 353, 354, 356, 553, 638, 661, 678, 734, 769, 798, 802, 804, 805, 809, 811, 814, 816, 819, 822, 823, 826, 833, 834, 836, 1028, 1032, 1049], "orient": [33, 68, 70, 94, 96, 254, 734], "slower": [33, 94, 96, 133, 157, 185, 227, 236, 254, 268, 567, 638, 669, 718, 734, 744, 1049], "common": [33, 67, 73, 74, 254, 438, 587, 588, 638, 643, 656, 661, 663, 689, 699, 734], "write_ndjson": [33, 254], "newlin": [34, 109, 115, 254], "delimit": [34, 101, 102, 109, 112, 115, 187, 215, 254, 508, 978, 1026, 1049], "parquetcompress": [35, 254], "compression_level": [35, 48, 254, 734], "statist": [35, 48, 101, 102, 110, 116, 139, 254, 361, 362, 396, 482, 483, 484, 485, 486, 487, 488, 489, 490, 502, 638, 734, 786, 841, 842, 880, 952, 972, 1049], "row_group_s": [35, 48, 254, 734], "use_pyarrow": [35, 48, 101, 106, 110, 254, 734, 1029, 1030, 1049], "pyarrow_opt": [35, 104, 110, 113, 254], "parquet": [35, 48, 110, 111, 116, 254, 649, 734], "gzip": [35, 48, 254, 734], "lzo": [35, 48, 254, 734], "brotli": [35, 48, 254, 734], "choos": [35, 47, 48, 187, 254, 734], "good": [35, 47, 48, 170, 254, 734], "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, 464, 534, 535, 536, 638, 652, 670, 671, 676, 701, 706, 714, 734, 744, 1004, 1005, 1006, 1049], "fast": [35, 47, 48, 125, 127, 254, 366, 494, 638, 734, 846, 962, 1049, 1057], "decompress": [35, 47, 48, 254, 734], "backward": [35, 48, 148, 173, 254, 285, 338, 368, 638, 665, 676, 734, 818, 848, 1049], "guarante": [35, 48, 91, 101, 102, 223, 254, 663, 707, 734], "deal": [35, 48, 170, 254, 344, 352, 473, 534, 638, 734, 824, 832, 938, 1004, 1049], "older": [35, 48, 254, 734], "reader": [35, 48, 99, 101, 102, 106, 110, 254, 650, 734], "higher": [35, 48, 189, 246, 254, 471, 486, 613, 638, 690, 728, 734, 937, 951, 1049], "mean": [35, 48, 101, 102, 106, 110, 112, 139, 148, 157, 158, 159, 173, 187, 227, 234, 254, 341, 345, 352, 365, 368, 482, 483, 484, 485, 486, 488, 489, 490, 502, 515, 569, 574, 587, 638, 665, 669, 670, 671, 676, 680, 716, 734, 786, 821, 825, 832, 844, 848, 853, 948, 972, 985, 1049], "smaller": [35, 48, 144, 254, 663, 734, 839, 1049], "disk": [35, 47, 48, 106, 254, 699, 734], "11": [35, 48, 118, 124, 159, 254, 263, 314, 315, 329, 337, 338, 341, 345, 352, 382, 465, 473, 489, 503, 542, 562, 576, 622, 627, 638, 656, 671, 672, 679, 689, 704, 734, 737, 744, 821, 825, 832, 946, 1049], "22": [35, 48, 123, 254, 322, 342, 345, 352, 354, 355, 482, 483, 485, 488, 489, 490, 534, 576, 638, 734, 737, 825, 832, 835, 1004, 1057], "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, 409, 415, 419, 420, 421, 430, 432, 433, 434, 455, 464, 465, 469, 470, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 499, 500, 501, 502, 506, 551, 552, 563, 564, 565, 566, 570, 571, 572, 579, 581, 584, 591, 605, 609, 616, 617, 622, 638, 669, 673, 675, 676, 707, 713, 716, 728, 734, 739, 745, 746, 747, 748, 749, 750, 756, 757, 758, 770, 778, 779, 780, 781, 782, 783, 788, 838, 839, 843, 880, 892, 898, 902, 903, 904, 913, 915, 916, 917, 924, 932, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 969, 970, 971, 972, 975, 1022, 1023, 1031, 1049], "512": [35, 254, 467, 638, 932, 1049], "implement": [35, 91, 132, 133, 236, 254, 268, 395, 468, 546, 567, 638, 718, 744, 960, 961, 1049], "v": [35, 54, 55, 144, 254, 493, 638, 785, 958, 1049], "At": [35, 254], "moment": [35, 138, 254, 396, 502, 638, 880, 972, 1049], "pyarrow": [35, 90, 95, 101, 103, 104, 106, 110, 113, 117, 118, 171, 212, 217, 218, 254, 650, 1025, 1029, 1030, 1031, 1049], "write_t": [35, 254], "calendar": [37, 38, 158, 159, 173, 227, 254, 329, 341, 345, 352, 356, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 805, 821, 825, 832, 836], "time_unit": [38, 40, 97, 317, 318, 325, 350, 355, 534, 536, 587, 588, 596, 737, 793, 794, 801, 830, 835, 1004, 1006], "time_zon": [38, 97, 319, 344, 352, 536, 587, 588, 737, 792, 795, 800, 824, 832, 1006], "timezon": [38, 737], "m": [38, 40, 55, 172, 254, 316, 317, 318, 324, 325, 348, 350, 351, 355, 515, 534, 535, 536, 539, 587, 588, 596, 675, 734, 737, 792, 793, 794, 800, 801, 828, 830, 831, 835, 985, 1004, 1005, 1006, 1009], "zone": [38, 316, 319, 344, 534, 536, 587, 588, 737, 792, 795, 824, 1004, 1006], "zoneinfo": [38, 737], "run": [38, 47, 48, 73, 125, 127, 133, 157, 174, 187, 223, 236, 254, 268, 309, 410, 479, 480, 618, 638, 656, 661, 663, 669, 680, 684, 689, 699, 707, 734, 737, 744, 784, 893, 944, 945, 1049, 1056, 1057], "available_timezon": [38, 737], "check": [38, 101, 102, 112, 119, 120, 153, 158, 159, 167, 169, 172, 254, 264, 266, 286, 289, 290, 383, 387, 406, 509, 513, 532, 638, 670, 671, 675, 680, 734, 741, 742, 760, 763, 764, 861, 863, 866, 867, 869, 874, 875, 876, 878, 879, 889, 959, 979, 983, 1002, 1049], "128": [39, 69, 932, 1049], "bit": [39, 41, 42, 43, 44, 45, 46, 61, 62, 63, 64, 475, 509, 638, 940, 979, 1049], "neg": [39, 158, 159, 161, 175, 203, 204, 206, 210, 254, 423, 424, 466, 495, 496, 503, 528, 638, 670, 671, 697, 698, 700, 714, 734, 855, 882, 906, 907, 964, 965, 973, 998, 1019, 1049], "scale": [39, 144, 254, 466, 537, 638, 839, 1007, 1049], "experiment": [39, 117, 200, 225, 226, 231, 254, 309, 345, 482, 483, 484, 485, 486, 488, 489, 490, 638, 695, 709, 712, 734, 784, 825, 856, 936, 1049], "progress": 39, "expect": [39, 82, 84, 89, 268, 567, 603, 638, 678, 680, 734], "32": [41, 44, 62, 69, 159, 169, 254, 456, 497, 638, 671, 734, 788, 822, 932, 951, 1049], "sign": [43, 44, 45, 46, 341, 475, 542, 638, 821, 869, 940, 1012, 1049], "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, 431, 549, 556, 580, 638, 652, 656, 661, 663, 669, 689, 699, 701, 706, 707, 717, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 759, 914, 1034, 1049], "type_coercion": [47, 48, 73, 656, 661, 663, 689, 699, 734], "predicate_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "projection_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "simplify_express": [47, 48, 73, 656, 661, 663, 689, 699, 734], "no_optim": [47, 48, 73, 656, 663, 680, 689, 734], "slice_pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 699, 734], "persist": [47, 48, 57, 734], "larger": [47, 48, 579, 734], "ram": [47, 48, 734], "maintain": [47, 48, 134, 221, 254, 284, 431, 493, 556, 638, 652, 701, 706, 734, 759, 914, 958, 1034, 1049], "slightli": [47, 48, 734], "faster": [47, 48, 146, 217, 225, 254, 268, 481, 522, 557, 638, 734, 743, 744, 845, 946, 992, 1049], "coercion": [47, 48, 73, 476, 638, 656, 661, 663, 689, 699, 734], "optim": [47, 48, 73, 110, 112, 114, 115, 116, 170, 174, 186, 190, 196, 223, 254, 656, 661, 663, 680, 689, 699, 707, 714, 718, 734, 770, 1049], "predic": [47, 48, 73, 112, 114, 115, 116, 117, 149, 169, 195, 254, 369, 561, 594, 638, 656, 661, 663, 666, 680, 689, 692, 699, 714, 734, 849, 960, 961, 1049], "pushdown": [47, 48, 73, 656, 661, 663, 680, 689, 692, 699, 714, 734, 960, 961, 1049], "project": [47, 48, 73, 112, 114, 115, 116, 268, 504, 505, 638, 656, 661, 663, 680, 689, 692, 699, 715, 734], "turn": [47, 48, 73, 101, 102, 112, 540, 559, 638, 656, 661, 663, 680, 689, 734, 1010], "off": [47, 48, 73, 101, 102, 112, 559, 638, 656, 661, 663, 680, 689, 734], "certain": [47, 48, 80, 104, 113, 164, 227, 254, 576, 656, 689, 734, 1049], "slice": [47, 48, 68, 73, 144, 161, 171, 210, 254, 414, 427, 481, 482, 483, 484, 485, 486, 488, 489, 490, 638, 656, 661, 663, 680, 689, 699, 734, 839, 855, 897, 910, 946, 947, 948, 949, 950, 951, 953, 954, 955, 1019, 1049], "lf": [47, 48, 652, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 669, 670, 672, 673, 674, 675, 677, 678, 679, 680, 681, 682, 683, 684, 686, 687, 688, 689, 690, 692, 693, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 710, 711, 712, 713, 714, 715, 734, 1056, 1057], "scan_csv": [47, 48, 101, 102, 734], "my_larger_than_ram_fil": [47, 48, 734], "data_pagesize_limit": [48, 734], "reduc": [48, 101, 102, 110, 112, 114, 115, 116, 241, 242, 244, 247, 497, 594, 638, 723, 724, 726, 729, 734, 921, 935, 966, 1018, 1049], "pressur": [48, 110, 115, 116, 497, 638, 734, 966, 1049], "improv": [48, 106, 114, 734], "speed": [48, 268, 638, 734], "minimum": [48, 122, 123, 124, 126, 152, 181, 254, 360, 361, 362, 453, 459, 473, 609, 610, 638, 686, 734, 774, 813, 840, 841, 842, 929, 934, 938, 1049, 1057], "limit": [48, 103, 112, 148, 254, 285, 298, 299, 300, 368, 374, 638, 665, 672, 734, 774, 775, 776, 848, 1049], "page": [48, 106, 110, 114, 116, 254, 638, 649, 734, 1049], "arrai": [49, 90, 94, 101, 102, 110, 144, 212, 217, 218, 254, 281, 282, 283, 284, 298, 299, 300, 304, 305, 306, 307, 308, 405, 418, 419, 420, 421, 422, 425, 426, 433, 482, 483, 485, 489, 494, 591, 638, 734, 756, 757, 758, 759, 774, 775, 776, 780, 781, 782, 783, 788, 839, 888, 901, 902, 903, 904, 905, 908, 909, 916, 917, 947, 948, 950, 953, 954, 955, 962, 967, 1025, 1030, 1031, 1039, 1049], "integer_list": 49, "float_list": 49, "wrap": [51, 97, 383, 638], "arbitrari": [51, 124, 737], "python": [51, 101, 102, 118, 133, 170, 196, 197, 214, 236, 254, 268, 318, 437, 567, 587, 588, 626, 627, 629, 638, 643, 718, 744, 794, 806, 807, 808, 813, 1029, 1049], "queri": [52, 54, 73, 103, 106, 112, 114, 115, 116, 146, 174, 186, 254, 268, 437, 439, 518, 638, 656, 661, 663, 672, 679, 689, 699, 714, 718, 734, 743, 744, 845, 988, 1049, 1056], "eager": [52, 53, 54, 55, 56, 57, 158, 225, 254, 310, 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, 470, 482, 483, 485, 488, 489, 490, 569, 573, 587, 588, 596, 600, 601, 612, 615, 621, 626, 627, 630, 637, 638, 670, 734, 792, 793, 795, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 830, 832, 833, 834, 835, 836, 1056], "pars": [52, 101, 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456, 460, 461, 463, 467, 468, 470, 476, 480, 499, 502, 530, 545, 546, 553, 555, 562, 564, 566, 567, 570, 571, 582, 583, 585, 594, 603, 604, 606, 610, 614, 623, 638, 675, 680, 684, 700, 734, 737, 744, 848, 878, 969, 972, 1000, 1032, 1049, 1056], "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, 292, 309, 316, 324, 344, 357, 360, 361, 362, 410, 464, 466, 470, 515, 521, 534, 536, 567, 629, 638, 670, 688, 707, 734, 737, 744, 784, 824, 838, 840, 841, 842, 893, 936, 959, 985, 991, 997, 1004, 1006, 1030, 1039, 1049], "join": [54, 58, 67, 73, 74, 75, 173, 226, 254, 382, 464, 638, 656, 661, 663, 676, 689, 699, 709, 713, 734], "named_fram": [55, 1056], "lf1": [55, 57], "o": [55, 106, 114, 291, 309, 514, 554, 638, 765, 784, 984, 1003, 1033, 1049], "lf2": [55, 57, 734], "p": [55, 69, 186, 254, 466, 621, 638, 688, 734], "q": [55, 197, 254, 310, 470, 621, 638, 936, 1049], "r": [55, 466, 510, 514, 515, 516, 524, 638, 737, 980, 984, 985, 986, 994], "lf3": [55, 734], "lf4": [55, 734], "either": [55, 124, 159, 169, 174, 177, 185, 195, 209, 217, 254, 470, 534, 563, 572, 621, 638, 671, 678, 734, 737, 936, 1004, 1049], "tbl1": [55, 57], "tbl2": [55, 57], "tbl3": 55, "tbl4": 55, "statement": [56, 629], "hello_world": 56, "baz": [56, 164, 165, 187, 224, 254, 529, 531, 674, 708, 734, 737, 1001], "hello_data": 56, "foo_bar": [56, 603], "registr": [57, 649], "lifetim": [57, 130, 648], "context": [57, 58, 128, 183, 237, 254, 262, 268, 303, 369, 410, 447, 504, 505, 559, 567, 580, 593, 596, 602, 615, 618, 637, 638, 648, 649, 713, 719, 734, 893, 1056], "manag": [57, 58, 648, 649, 1056], "often": [57, 130, 158, 159, 254, 407, 476, 638, 670, 671, 734, 890], "want": [57, 93, 133, 146, 183, 254, 268, 298, 299, 300, 352, 369, 437, 439, 480, 482, 483, 484, 485, 486, 488, 489, 490, 582, 594, 612, 615, 630, 638, 656, 672, 679, 734, 737, 743, 744, 774, 775, 776, 832, 845, 945, 1030, 1049], "df0": [57, 180, 254, 685, 734], "exit": [57, 58, 130, 1056], "construct": [57, 90, 92, 93, 94, 95, 96, 254, 375, 439, 612, 615, 630, 638, 668, 691, 734, 1049], "through": [57, 737, 1049], "tbl0": 57, "remain": [57, 101, 102, 112, 144, 254, 530, 531, 569, 680, 734, 839, 1000, 1001, 1049], "text": [57, 522, 524, 525, 619, 992, 1057], "misc": 57, "testing1234": 57, "test1": 57, "test2": 57, "test3": 57, "temporarili": [58, 128, 130, 158, 159, 254, 670, 671, 734], "cach": [58, 73, 75, 106, 112, 114, 116, 129, 439, 482, 483, 484, 485, 486, 488, 489, 490, 534, 535, 536, 539, 638, 648, 656, 661, 663, 689, 699, 734, 1004, 1005, 1006, 1009], "categori": [58, 75, 215, 254, 294, 295, 310, 470, 638, 768, 769, 785, 856, 936, 1049], "until": [58, 174, 254, 587], "finish": [58, 78, 146, 254, 743, 845, 1049], "invalid": [58, 101, 102, 112, 517, 518, 523, 555, 587, 588, 638, 987, 988, 993], "outermost": 58, "color": [58, 75, 236, 286, 288, 289, 290, 718], "red": [58, 75, 236, 718], "green": [58, 75, 236, 718], "blue": [58, 75, 286, 288, 289, 290], "orang": [58, 75, 137, 237, 238, 240, 241, 242, 244, 246, 247, 254, 719, 720, 722, 723, 724, 726, 728, 729], "uint8": [58, 75, 121, 123, 216, 217, 254, 307, 308, 439, 547, 562, 638, 737, 782, 783, 1018, 1049, 1057], "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, 470, 509, 538, 541, 553, 578, 634, 638, 737, 785, 856, 936, 979, 1008, 1011, 1045, 1049], "u8": [58, 75, 215, 216, 254, 439, 562, 638, 737, 1026, 1049, 1057], "composit": [59, 123, 1057], "schemadict": [59, 90, 92, 93, 94, 95, 96, 112, 199, 254, 621, 680, 694, 718, 734], "struct_seri": [59, 718], "dai": [60, 158, 159, 171, 173, 227, 254, 325, 329, 336, 337, 338, 341, 342, 343, 345, 350, 352, 353, 354, 356, 482, 483, 484, 485, 486, 488, 489, 490, 586, 587, 589, 590, 638, 670, 671, 676, 734, 737, 817, 818, 821, 822, 825, 832, 834], "unsign": [61, 62, 63, 64, 475, 638, 869, 940, 1049], "could": [65, 78, 142, 158, 254, 293, 582, 594, 638, 659, 670, 734, 766, 1049], "static": [65, 718], "utf": 66, "frametyp": [67, 1056], "joinstrategi": [67, 172, 254, 675, 734], "outer": [67, 74, 172, 254, 675, 734], "descend": [67, 134, 201, 207, 221, 254, 278, 425, 473, 494, 504, 505, 572, 638, 652, 696, 701, 706, 734, 753, 875, 908, 938, 962, 974, 1049], "fill": [67, 74, 135, 147, 148, 204, 225, 254, 285, 305, 308, 367, 368, 374, 382, 482, 483, 485, 489, 496, 520, 526, 542, 595, 612, 615, 630, 638, 664, 665, 698, 713, 734, 847, 848, 859, 930, 947, 948, 950, 953, 954, 955, 965, 990, 996, 1012, 1049], "sort": [67, 68, 119, 123, 134, 158, 159, 173, 180, 186, 187, 201, 221, 227, 239, 248, 254, 278, 295, 369, 464, 494, 505, 559, 561, 572, 638, 652, 661, 670, 671, 676, 685, 688, 689, 696, 699, 706, 721, 730, 734, 737, 753, 769, 875, 962, 1034, 1037, 1049, 1057], "origin": [67, 101, 102, 223, 254, 344, 395, 439, 464, 475, 476, 477, 510, 515, 516, 518, 520, 526, 542, 570, 571, 638, 707, 734, 785, 824, 919, 936, 942, 980, 985, 986, 988, 990, 996, 1012, 1049], "In": [67, 104, 113, 116, 124, 126, 130, 133, 144, 146, 158, 159, 183, 217, 254, 268, 587, 638, 670, 671, 734, 743, 839, 845, 939, 1049], "duplic": [67, 79, 166, 172, 173, 223, 254, 263, 384, 395, 470, 638, 675, 676, 707, 734, 862, 936, 1049], "behaviour": [67, 74, 509, 515, 516, 524, 555, 638, 979, 985, 986, 994], "strategi": [67, 74, 101, 121, 122, 123, 124, 126, 148, 158, 172, 173, 182, 254, 268, 368, 429, 638, 665, 670, 675, 676, 734, 848, 912, 1049], "suitabl": [67, 74, 122, 133, 254, 268, 493, 638, 744, 958, 1049, 1057], "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, 412, 417, 418, 431, 440, 441, 442, 447, 449, 453, 458, 459, 471, 479, 503, 507, 519, 522, 547, 548, 556, 560, 587, 593, 598, 602, 605, 606, 607, 608, 609, 610, 620, 624, 628, 638, 652, 657, 660, 667, 670, 671, 672, 677, 679, 694, 700, 704, 706, 711, 721, 730, 734, 751, 752, 753, 754, 755, 759, 768, 772, 780, 781, 782, 783, 801, 821, 852, 855, 860, 862, 865, 877, 882, 895, 900, 901, 914, 920, 922, 923, 925, 928, 929, 933, 934, 937, 944, 973, 976, 989, 992, 1019, 1025, 1026, 1034, 1038, 1039, 1049], "speedup": [67, 133, 170, 254, 268, 638, 744, 1049], "receiv": [67, 112, 133, 186, 254, 466, 638, 688, 734, 1057], "now": [67, 159, 254, 470, 534, 536, 638, 671, 734, 1004, 1006], "One": [67, 139, 183, 187, 254, 265, 463, 619, 638, 737, 786, 1049], "whose": [67, 173, 187, 254, 363, 592, 638, 676, 734], "uniqu": [67, 121, 122, 123, 126, 168, 172, 183, 197, 245, 254, 269, 280, 310, 386, 394, 457, 470, 534, 535, 536, 539, 557, 559, 568, 611, 638, 675, 727, 734, 755, 785, 786, 865, 877, 926, 936, 1004, 1005, 1006, 1009, 1035, 1037, 1049], "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, 463, 505, 517, 561, 562, 572, 573, 621, 629, 638, 652, 655, 666, 678, 701, 706, 734, 737, 741, 742, 754, 760, 762, 804, 849, 860, 861, 862, 864, 865, 867, 868, 870, 871, 872, 873, 877, 884, 885, 889, 933, 934, 960, 979, 1030, 1040, 1049], "know": [67, 429, 522, 992, 1030, 1039, 1049], "first": [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, 410, 413, 414, 429, 439, 466, 493, 505, 515, 517, 518, 524, 525, 575, 582, 587, 594, 598, 629, 638, 656, 670, 672, 676, 679, 688, 707, 713, 721, 734, 737, 753, 784, 786, 818, 821, 825, 855, 865, 882, 893, 896, 897, 912, 919, 958, 985, 987, 988, 994, 995, 1019, 1028, 1049, 1057], "2022": [67, 139, 156, 158, 159, 173, 227, 254, 318, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 534, 587, 588, 590, 596, 638, 670, 671, 676, 734, 737, 794, 805, 821, 825, 832, 1004], "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, 416, 436, 438, 460, 461, 463, 468, 476, 480, 534, 535, 536, 546, 555, 562, 564, 566, 570, 571, 585, 603, 606, 610, 623, 638, 675, 684, 700, 734, 737, 828, 831, 1004, 1005, 1006], "df3": [67, 254], "set_tbl_format": 67, "09": [67, 124, 159, 254, 318, 329, 337, 338, 537, 627, 671, 734, 798, 1007], "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, 482, 483, 485, 488, 489, 490, 534, 535, 536, 539, 587, 588, 590, 638, 670, 671, 734, 737, 793, 794, 795, 796, 797, 798, 799, 801, 802, 803, 804, 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, 1004, 1005, 1006, 1009], "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, 482, 483, 484, 485, 486, 488, 489, 490, 491, 534, 535, 536, 539, 587, 588, 590, 638, 670, 671, 676, 734, 737, 793, 794, 796, 797, 801, 802, 803, 806, 807, 808, 809, 811, 813, 814, 815, 816, 817, 818, 819, 821, 822, 823, 824, 825, 826, 827, 829, 830, 832, 833, 834, 835, 912, 1004, 1005, 1006, 1009], "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, 482, 483, 485, 488, 489, 490, 535, 539, 587, 588, 638, 670, 671, 734, 793, 794, 795, 798, 799, 801, 802, 803, 806, 807, 808, 809, 811, 813, 815, 816, 817, 818, 819, 822, 823, 824, 825, 826, 827, 828, 830, 831, 833, 834, 835, 1005, 1009], "af1": 67, "af2": 67, "af3": 67, "keep": [67, 101, 102, 112, 223, 226, 227, 254, 395, 439, 575, 638, 707, 709, 734, 919, 1049], "easili": [67, 200, 231, 234, 254, 576, 621, 695, 712, 716, 734], "dot": [67, 638, 699, 734, 1049], "product": [67, 138, 254, 307, 313, 567, 638, 782, 788, 1049], "fill_nul": [67, 147, 254, 638, 664, 713, 734, 1049], "sum_horizont": [67, 622], "167": 67, "47": 67, "callabl": [68, 69, 70, 71, 112, 133, 152, 186, 236, 254, 268, 429, 437, 438, 466, 481, 567, 582, 583, 594, 604, 614, 638, 680, 688, 718, 734, 744, 912, 946, 1049], "decor": [68, 69, 70, 71, 124, 126, 133, 254, 268, 638, 648, 649, 744, 1049], "under": [68, 69, 70, 71, 631, 632, 634, 639, 640, 644, 645, 646, 1041, 1043, 1045, 1050, 1053, 1054, 1055], "access": [68, 69, 70, 71, 170, 195, 196, 254, 439, 638, 713, 734, 1057], "by_first_letter_of_column_nam": 68, "f": [68, 102, 139, 156, 171, 212, 222, 225, 254, 261, 268, 429, 497, 514, 534, 536, 567, 577, 604, 638, 912, 984, 1004, 1006], "fromkei": [68, 70], "by_first_letter_of_column_valu": 68, "starts_with": [68, 286, 289, 509, 513, 737, 983], "to_seri": [68, 154, 254, 534, 573, 615, 1004], "xx": [68, 70, 123, 126, 737], 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1057], "42": [71, 159, 160, 180, 254, 671, 685, 734, 854, 1049], "961": 71, "1764": 71, "4160": 71, "build": [72, 91, 737, 1057], "wa": 72, "compil": [72, 403, 404, 886, 887], "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, 429, 466, 475, 509, 511, 563, 565, 567, 584, 605, 609, 622, 629, 638, 685, 695, 718, 734, 744, 761, 774, 775, 776, 824, 832, 840, 841, 842, 912, 940, 960, 961, 979, 981, 1049], "depend": [72, 118, 268, 403, 404, 447, 470, 473, 567, 593, 596, 602, 638, 886, 887, 936, 938, 1049, 1057], "host": [72, 101, 106, 110, 114, 116], "git": 72, "lazy_fram": 73, "comm_subplan_elim": [73, 656, 661, 663, 689, 699, 734], "comm_subexpr_elim": [73, 292, 638, 656, 661, 663, 689, 699, 734], "graph": [73, 174, 254, 673, 713, 734], "parallel": [73, 74, 99, 103, 110, 116, 158, 173, 174, 186, 254, 309, 410, 638, 649, 670, 675, 676, 718, 734, 784, 893, 1049], "threadpool": [73, 128], "Will": [73, 656, 661, 663, 689, 699, 734, 1049], "try": [73, 85, 87, 101, 102, 105, 106, 110, 112, 114, 116, 656, 661, 663, 689, 699, 734], "branch": [73, 656, 661, 663, 689, 699, 734], "subplan": [73, 656, 661, 663, 689, 699, 734], "union": [73, 74, 656, 661, 663, 689, 699, 734, 737], "subexpress": [73, 656, 661, 663, 689, 699, 734], "reus": [73, 656, 661, 663, 689, 699, 734], "part": [73, 90, 124, 516, 530, 531, 656, 661, 663, 689, 699, 713, 734, 986, 1000, 1001], "fashion": [73, 172, 254, 656, 661, 663, 689, 699, 734], "item": [74, 102, 195, 198, 254, 365, 406, 413, 416, 492, 531, 638, 844, 889, 896, 899, 957, 1001, 1049], "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, 464, 505, 563, 564, 565, 566, 572, 575, 576, 577, 578, 584, 585, 592, 605, 606, 609, 610, 618, 621, 622, 623, 638, 652, 669, 670, 671, 695, 696, 701, 706, 712, 716, 734, 784, 1049, 1057], "polarstyp": 74, "concatmethod": 74, "vertic": [74, 146, 225, 229, 254, 508, 978], "rechunk": [74, 90, 95, 101, 102, 106, 110, 112, 114, 115, 116, 146, 254, 638, 743, 772, 845, 925, 1049], "combin": [74, 85, 87, 158, 159, 160, 173, 227, 254, 265, 279, 341, 352, 463, 521, 527, 533, 587, 638, 670, 671, 676, 699, 734, 821, 825, 832, 991, 997, 1003], "concaten": [74, 152, 186, 254, 466, 577, 578, 638, 688, 734, 772, 925, 1049], "diagon": [74, 222, 254], "vstack": [74, 146, 254], "vertical_relax": 74, "coerc": [74, 476, 638], "equal": [74, 75, 101, 102, 112, 119, 120, 134, 153, 158, 173, 180, 221, 254, 292, 358, 359, 376, 398, 429, 461, 476, 481, 482, 483, 484, 485, 486, 488, 489, 490, 519, 520, 526, 542, 616, 617, 638, 652, 670, 676, 685, 701, 706, 734, 912, 946, 947, 948, 949, 950, 951, 953, 954, 955, 959, 989, 990, 996, 1012, 1049, 1057], "supertyp": [74, 148, 254, 267, 638, 665, 734], "find": [74, 150, 254, 493, 638, 958, 1049], "miss": [74, 101, 102, 112, 147, 254, 285, 360, 361, 362, 374, 389, 391, 638, 664, 734, 840, 841, 842, 1039, 1049], "stack": [74, 163, 229, 254], "don": [74, 133, 223, 225, 254, 268, 292, 309, 410, 464, 470, 567, 638, 707, 734, 737, 744, 784, 893, 936, 959, 1039, 1049], "auto": [74, 90, 92, 93, 94, 96, 108, 109, 110, 116, 222, 254, 734, 1057], "logic": [74, 133, 236, 254, 268, 553, 567, 638, 668, 675, 691, 715, 718, 734, 744, 1032, 1049], "align_fram": 74, "pattern": [74, 101, 102, 112, 114, 115, 116, 170, 254, 445, 509, 510, 515, 516, 524, 525, 718, 737, 960, 961, 979, 980, 985, 986, 994, 995, 1049], "collis": 74, "need": [74, 97, 101, 102, 103, 105, 119, 120, 158, 159, 197, 205, 217, 254, 429, 439, 497, 519, 537, 612, 615, 630, 638, 670, 671, 734, 966, 989, 1007, 1049], "sure": [74, 90, 95, 101, 102, 106, 110, 158, 159, 190, 254, 670, 671, 734], "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, 458, 459, 517, 518, 579, 582, 594, 638, 670, 734, 928, 929, 987, 988, 1049], "least": [82, 124, 465, 559, 638, 932, 1037, 1049], "unexpect": [83, 254, 268, 437, 638, 744, 1049], "caus": [83, 91, 101, 102, 112, 132, 146, 254, 743, 845, 1049], "panic": 83, "mismatch": [85, 109], "incompat": 87, "pa": [90, 117], "chunkedarrai": [90, 182, 254, 788, 1049], "recordbatch": [90, 171, 254], "schemadefinit": [90, 92, 93, 94, 96, 108, 109, 254, 734], "schema_overrid": [90, 92, 93, 94, 95, 96, 108, 109, 171, 217, 254, 284, 734, 737, 759], "copi": [90, 91, 132, 135, 136, 171, 212, 217, 218, 231, 254, 366, 542, 638, 654, 655, 712, 734, 773, 777, 790, 846, 1012, 1025, 1030, 1031, 1049], "closest": 90, "pair": [90, 92, 93, 94, 96, 108, 109, 123, 191, 254, 692, 734, 1057], "sever": [90, 92, 93, 94, 96, 108, 109, 254, 734, 1057], "wai": [90, 92, 93, 94, 96, 108, 109, 140, 157, 171, 186, 207, 234, 254, 464, 466, 505, 515, 576, 638, 658, 669, 688, 701, 716, 718, 734, 985], 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"typic": [93, 133, 254, 324, 737, 744, 800, 1049], "clearer": 93, "load": [93, 95, 104, 113, 125, 127, 254, 649, 672, 679, 734, 1057], "_partial_": [93, 254, 734], "omit": [93, 97, 122, 124, 126, 130, 183, 197, 254, 626, 627, 737], "mani": [93, 96, 103, 146, 254, 517, 743, 845, 987, 1049], "scan": [93, 96, 101, 102, 110, 112, 113, 114, 115, 116, 117, 158, 159, 254, 663, 670, 671, 672, 679, 734], "slow": [93, 96, 101, 102, 112, 268, 309, 638, 718, 784, 1049], "partial": 93, "present": [93, 119, 124, 387, 638, 1039, 1049], "np": [94, 149, 217, 254, 549, 638, 734, 864, 868, 870, 871, 946, 1020, 1049], "ndarrai": [94, 149, 217, 254, 549, 638, 734, 788, 958, 961, 1020, 1030, 1049], "numpi": [94, 118, 138, 170, 196, 197, 214, 217, 218, 254, 458, 459, 638, 734, 864, 868, 870, 871, 928, 929, 946, 1030, 1031, 1039, 1049], "columnar": [94, 96, 170, 196, 254], "interpret": [94, 96, 101, 102, 112, 254, 734], "yield": [94, 96, 101, 102, 112, 144, 146, 222, 254, 464, 638, 734, 743, 839, 845, 1049], "conclus": [94, 96, 254, 734], "nan_to_nul": [95, 254, 734, 1049], "include_index": 95, "pd": [95, 105, 553, 638, 1031, 1032, 1049], "panda": [95, 105, 118, 158, 218, 254, 337, 338, 553, 638, 670, 734, 817, 818, 1031, 1032, 1049], "instal": [95, 101, 102, 103, 106, 110, 118, 138, 217, 218, 254, 699, 734, 1031, 1049], "nan": [95, 119, 120, 124, 132, 147, 218, 254, 314, 315, 358, 359, 367, 376, 377, 382, 389, 391, 392, 393, 398, 436, 440, 453, 458, 459, 460, 461, 555, 579, 638, 664, 734, 746, 750, 789, 847, 870, 871, 928, 929, 946, 1030, 1031, 1049, 1057], "convert": [95, 104, 105, 113, 132, 213, 214, 215, 216, 217, 220, 254, 311, 319, 348, 351, 429, 438, 472, 523, 534, 535, 536, 537, 539, 638, 734, 795, 828, 831, 912, 993, 1004, 1005, 1006, 1007, 1009, 1017, 1028, 1029, 1030, 1031, 1039, 1049], "pd_df": 95, "pd_seri": 95, "tbl": [97, 99, 102], "reconstruct": 97, "repr": [97, 124, 126], "trim": 97, "whitespac": [97, 521, 527, 533, 991, 997, 1003], "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, 466, 516, 517, 518, 638, 796, 797, 798, 799, 802, 803, 805, 809, 810, 811, 812, 814, 815, 816, 819, 820, 822, 823, 826, 827, 829, 833, 834, 836, 986, 987, 988], "to_init_repr": [97, 254, 1049], "truncat": [97, 158, 170, 196, 197, 214, 254, 341, 670, 689, 734, 821], "identifi": [97, 179, 185, 223, 254, 684, 707, 734], "compound": [97, 197, 254, 737], "struct": [97, 183, 200, 220, 224, 231, 254, 310, 429, 439, 470, 479, 480, 517, 530, 531, 559, 582, 583, 585, 604, 638, 695, 708, 712, 718, 734, 785, 912, 936, 944, 987, 998, 1000, 1001, 1049], "neither": [97, 105, 198, 254, 429, 912], "source_ac": 97, "source_cha": 97, "ident": [97, 135, 136, 254, 348, 479, 480, 638, 654, 655, 734, 773, 777, 828, 944, 945, 1049], "timestamp": [97, 344, 596, 824], "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, 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654, 656, 672, 679, 734], "ideal": 99, "read_csv_batch": [99, 650], "tpch": [99, 102], "tables_scale_100": [99, 102], "lineitem": [99, 102, 103], "try_parse_d": [99, 101, 102, 112], "n_row": [100, 101, 102, 106, 110, 112, 114, 115, 116, 171, 254, 663, 734], "accept": [100, 101, 102, 104, 106, 110, 134, 145, 157, 200, 207, 221, 231, 234, 254, 261, 363, 383, 439, 464, 505, 562, 563, 564, 565, 566, 572, 575, 576, 577, 578, 584, 585, 592, 605, 606, 609, 610, 618, 621, 622, 623, 629, 638, 652, 662, 669, 695, 701, 706, 712, 716, 734, 860, 1049], "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, 482, 483, 485, 488, 489, 490, 638, 793, 795, 798, 801, 802, 804, 809, 811, 814, 816, 819, 822, 823, 825, 826, 830, 832, 833, 834, 835, 836], "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], "we": [101, 102, 105, 112, 158, 159, 173, 227, 254, 268, 341, 345, 352, 410, 437, 482, 483, 484, 485, 486, 488, 489, 490, 523, 603, 629, 638, 670, 671, 676, 680, 734, 744, 821, 825, 832, 856, 893, 993, 1049], "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, 470, 482, 483, 484, 485, 486, 488, 489, 490, 497, 592, 638, 670, 671, 676, 692, 734, 821, 825, 832, 959, 1030, 1049], "builtin": [101, 102, 105], "stringio": [101, 102], "fsspec": [101, 102, 106, 110, 113, 114, 116, 118], "remot": 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429, 437, 438, 481, 567, 582, 583, 594, 604, 614, 638, 680, 688, 718, 734, 744, 840, 841, 842, 912, 1049], "lower": [112, 158, 189, 246, 254, 298, 300, 383, 435, 438, 471, 475, 486, 569, 587, 588, 600, 601, 613, 626, 627, 638, 663, 670, 690, 728, 734, 776, 860, 918, 937, 940, 951, 1036, 1049], "simpli": [112, 464, 582, 594, 638], "idx": [112, 158, 171, 254, 429, 670, 734, 912, 961, 1049], "uint16": [112, 307, 308, 547, 638, 737, 782, 783, 1018, 1049], "u16": [112, 124, 1057], "eu": 113, "central": [113, 396, 502, 638, 880, 972, 1049], "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, 587, 676, 734, 737, 794, 795, 796, 797, 798, 799, 824, 825, 828, 829, 831, 832, 834], "04": [117, 158, 227, 254, 318, 319, 323, 327, 334, 335, 336, 337, 338, 344, 345, 346, 347, 348, 351, 353, 355, 534, 587, 590, 670, 734, 737, 795, 799, 803, 809, 811, 814, 815, 816, 817, 818, 819, 823, 824, 826, 827, 828, 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238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 478, 513, 532, 638, 675, 692, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729, 734, 983, 1002], "banana": [137, 213, 237, 238, 240, 241, 242, 243, 244, 245, 246, 247, 254, 478, 638, 719, 720, 722, 723, 724, 725, 726, 727, 728, 729], "pairwis": [138, 254], "pearson": [138, 254, 396, 502, 579, 638, 880, 972, 1049], "correl": [138, 254, 579, 616], "coeffici": [138, 254, 502, 638, 972, 1049], "corrcoef": [138, 254], "percentil": [139, 254, 786, 1049], "summari": [139, 254, 786, 1049], "glimps": [139, 161, 254], "usd": [139, 156, 254, 1057], "2020": [139, 156, 159, 254, 319, 323, 324, 327, 330, 331, 333, 335, 340, 344, 347, 348, 351, 352, 534, 535, 536, 671, 734, 737, 795, 799, 800, 803, 810, 812, 815, 820, 824, 827, 828, 831, 832, 1004, 1005, 1006], "null_count": [139, 142, 254, 309, 638, 734, 784, 786, 1049], "266667": [139, 254], "666667": [139, 177, 228, 242, 254, 360, 638, 710, 724, 734], "std": [139, 254, 481, 488, 638, 734, 786, 953, 1049], "101514": [139, 254], "707107": [139, 254, 361, 488, 638, 841, 1049], "57735": [139, 254], "median": [139, 187, 254, 368, 484, 638, 713, 734, 786, 949, 1049], "more_column": [140, 145, 201, 224, 254, 363, 592, 638, 658, 662, 696, 708, 734], "Or": [140, 157, 158, 159, 173, 207, 227, 234, 254, 464, 505, 576, 629, 638, 658, 669, 670, 671, 676, 701, 716, 734], "subset": [142, 183, 223, 254, 659, 707, 734], "snippet": [142, 254, 659, 734], "all_horizont": [142, 254, 563, 659, 734], "is_nul": [142, 254, 638, 659, 734, 1049], "sizeunit": [144, 254, 839, 1049], "heap": [144, 254, 839, 1049], "its": [144, 254, 318, 345, 352, 505, 638, 794, 825, 832, 839, 1049], "bitmap": [144, 254, 839, 1049], "therefor": [144, 254, 629, 839, 1049], "structarrai": [144, 254, 839, 1049], "constant": [144, 159, 254, 316, 366, 638, 671, 734, 792, 839, 846, 1049], "unchang": [144, 254, 553, 638, 680, 718, 734, 839, 1032, 1049], "capac": [144, 205, 254, 839, 967, 1049], "ffi": [144, 254, 839, 1049], "kb": [144, 254, 839, 1049], "mb": [144, 254, 839, 1049], "gb": [144, 254, 839, 1049], "tb": [144, 254, 839, 1049], "revers": [144, 254, 304, 305, 306, 307, 308, 438, 468, 546, 638, 734, 780, 781, 782, 783, 1049], "1_000_000": [144, 254, 839, 1049], "25888898": [144, 254], "689577102661133": [144, 254], "long": [145, 179, 225, 254, 662, 684, 734], "letter": [145, 239, 248, 254, 363, 516, 592, 638, 662, 721, 730, 734, 737, 986], "onlin": [146, 254, 743, 845, 1049], "rerun": [146, 254, 743, 845, 1049], "conveni": [146, 254, 743, 845, 1049], "evalu": [147, 149, 173, 254, 265, 279, 309, 381, 401, 402, 429, 439, 463, 563, 565, 569, 573, 587, 588, 591, 600, 601, 612, 615, 621, 626, 627, 629, 630, 638, 666, 673, 675, 676, 734, 754, 784, 884, 885, 1040, 1049], "Not": [147, 254, 389, 391, 439, 638, 664, 734], "To": [147, 254, 314, 315, 341, 368, 509, 515, 516, 524, 540, 622, 638, 664, 734, 821, 979, 985, 986, 994, 1010, 1030, 1049], "fillnullstrategi": [148, 254, 368, 638, 665, 734, 848, 1049], "matches_supertyp": [148, 254, 665, 734], "forward": [148, 173, 254, 337, 368, 374, 638, 665, 676, 734, 817, 848, 1049], "consecut": [148, 254, 285, 368, 374, 508, 638, 665, 734, 848, 978, 1049], "fill_nan": [148, 254, 638, 734, 1049], "OR": [149, 254, 565, 566, 666, 734, 737], "reduct": [152, 254], "supercast": [152, 254], "parent": [152, 254], "rule": [152, 254], "arithmet": [152, 254], "zip_with": [152, 254, 1049], "foo11": [152, 254], "bar22": [152, 254], "null_equ": [153, 254, 959, 1049], "retriev": [154, 254, 403, 404, 543, 886, 887, 1013], "return_as_str": [156, 254, 450], "preview": [156, 254], "wide": [156, 179, 225, 254, 684, 734], "nice": [156, 254], "few": [156, 254], "rather": [156, 173, 254, 450, 480, 542, 638, 676, 734, 945, 1012, 1049], "head": [156, 175, 210, 254, 267, 400, 638, 679, 734, 882, 1019, 1049], "tail": [156, 161, 254, 267, 502, 638, 734, 855, 972, 1049], "more_bi": [157, 185, 207, 254, 505, 638, 669, 701, 734], "consist": [157, 185, 254, 534, 669, 734, 743, 845, 1004, 1049], "regardless": [157, 254, 518, 988], "agg": [157, 158, 159, 254, 262, 268, 369, 371, 504, 505, 549, 561, 580, 638, 656, 661, 663, 669, 670, 671, 689, 699, 734, 737], "index_column": [158, 159, 254, 670, 671, 734], "timedelta": [158, 159, 227, 254, 322, 325, 326, 329, 334, 336, 341, 342, 343, 345, 346, 350, 352, 353, 354, 356, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 737, 806, 808, 813, 821, 825, 832, 928, 929, 1049], "period": [158, 159, 203, 204, 254, 345, 352, 360, 361, 362, 423, 465, 495, 496, 587, 588, 626, 627, 638, 670, 671, 697, 698, 734, 825, 832, 840, 841, 842, 906, 932, 964, 965, 1049], "include_boundari": [158, 254, 670, 734], "closedinterv": [158, 159, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 860, 1049], "start_bi": [158, 254, 670, 734], "startbi": [158, 254, 670, 734], "window": [158, 159, 254, 309, 345, 352, 360, 361, 362, 464, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 577, 616, 617, 638, 670, 671, 734, 784, 825, 832, 840, 841, 842, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 1049], "check_sort": [158, 159, 254, 670, 671, 734], "dynamicgroupbi": [158, 254], "groupbi": [158, 159, 183, 254, 262, 268, 309, 369, 371, 410, 464, 504, 505, 549, 561, 567, 580, 638, 656, 661, 663, 670, 671, 689, 699, 734, 737, 784, 893, 1049], "member": [158, 254, 670, 734, 867, 1049], "seen": [158, 254, 285, 374, 638, 670, 734], "roll": [158, 159, 254, 337, 338, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 577, 616, 617, 638, 670, 671, 734, 817, 818, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 1049], "slot": [158, 254, 309, 312, 408, 638, 670, 734, 784, 787, 891, 1049], "interv": [158, 159, 227, 254, 310, 328, 345, 346, 352, 383, 470, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 626, 627, 638, 670, 671, 734, 785, 798, 801, 802, 804, 809, 811, 814, 816, 819, 822, 823, 825, 826, 830, 832, 833, 834, 836, 860, 936, 1049], "1n": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "nanosecond": [158, 159, 173, 227, 254, 341, 345, 346, 352, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 821, 825, 826, 832], "1u": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "microsecond": [158, 159, 170, 173, 196, 197, 214, 227, 254, 341, 345, 346, 352, 482, 483, 484, 485, 486, 488, 489, 490, 589, 590, 625, 638, 670, 671, 676, 689, 734, 737, 821, 825, 832], "1m": [158, 159, 173, 227, 254, 330, 331, 333, 340, 341, 345, 347, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 810, 812, 820, 821, 825, 827, 832], "millisecond": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 737, 821, 825, 832], "minut": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 589, 590, 625, 626, 638, 670, 671, 676, 734, 737, 821, 825, 832], "1h": [158, 159, 173, 227, 254, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 626, 627, 638, 670, 671, 676, 734, 800, 802, 821, 825, 832], "hour": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 589, 590, 625, 626, 638, 670, 671, 676, 734, 737, 821, 825, 832], "1d": [158, 159, 173, 227, 254, 317, 327, 335, 341, 345, 352, 355, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 603, 638, 670, 671, 676, 734, 737, 793, 801, 803, 806, 807, 808, 813, 815, 821, 825, 830, 832, 834, 835], "1w": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "week": [158, 159, 173, 227, 254, 341, 345, 352, 354, 482, 483, 484, 485, 486, 488, 489, 490, 590, 638, 670, 671, 676, 734, 737, 821, 825, 832, 834], "1mo": [158, 159, 173, 227, 254, 319, 323, 337, 338, 341, 344, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 638, 670, 671, 676, 734, 795, 799, 816, 817, 818, 821, 822, 823, 824, 825, 832, 833], "month": [158, 159, 173, 227, 254, 322, 337, 338, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 586, 587, 588, 589, 638, 670, 671, 676, 734, 798, 817, 818, 821, 825, 832], "1q": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "quarter": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "1y": [158, 159, 173, 227, 254, 328, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 638, 670, 671, 676, 734, 804, 821, 825, 832, 836], "1i": [158, 159, 173, 227, 254, 341, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821], "3d12h4m25": [158, 159, 173, 227, 254, 345, 352, 670, 671, 676, 734, 825, 832], "suffix": [158, 159, 172, 173, 200, 227, 231, 234, 254, 263, 289, 341, 345, 352, 389, 391, 392, 393, 438, 464, 468, 478, 482, 483, 484, 485, 486, 488, 489, 490, 513, 638, 670, 671, 675, 676, 695, 712, 713, 716, 734, 737, 763, 821, 825, 832, 983], "_satur": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 587, 588, 638, 670, 671, 676, 734, 821, 825, 832], "satur": [158, 159, 173, 227, 254, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 821, 825, 832], "28": [158, 159, 173, 213, 227, 254, 261, 341, 344, 345, 352, 355, 482, 483, 484, 485, 486, 488, 489, 490, 587, 638, 670, 671, 676, 734, 821, 824, 825, 832, 835, 1057], "correspond": [158, 159, 173, 217, 227, 254, 329, 341, 345, 352, 473, 480, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 805, 821, 825, 832, 938, 945, 1049], "due": [158, 159, 173, 197, 227, 254, 263, 293, 324, 341, 345, 352, 395, 468, 482, 483, 484, 485, 486, 488, 489, 490, 492, 498, 546, 638, 670, 671, 676, 734, 766, 800, 821, 825, 832, 1049], "daylight": [158, 159, 173, 227, 254, 316, 324, 341, 345, 352, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 671, 676, 734, 800, 821, 825, 832], "10i": [158, 159, 254, 670, 671, 734], "ascend": [158, 159, 254, 670, 671, 734], "dynam": [158, 254, 429, 482, 483, 484, 485, 486, 488, 489, 490, 638, 670, 734, 912], "matter": [158, 159, 170, 196, 197, 214, 254, 670, 671, 734], "_lower_bound": [158, 254, 670, 734], "_upper_bound": [158, 254, 670, 734], "harder": [158, 254, 670, 734], "tempor": [158, 159, 170, 196, 197, 214, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 638, 649, 670, 671, 734, 737, 860, 876, 1049], "inclus": [158, 159, 254, 383, 482, 483, 484, 485, 486, 488, 489, 490, 529, 530, 569, 587, 588, 600, 601, 626, 627, 638, 670, 671, 734, 860, 999, 1000, 1049], "datapoint": [158, 254, 670, 734], "mondai": [158, 254, 352, 354, 670, 734, 832, 834], "tuesdai": [158, 254, 670, 734], "wednesdai": [158, 254, 670, 734], "thursdai": [158, 254, 670, 734], "fridai": [158, 254, 670, 734], "saturdai": [158, 254, 670, 734], "sundai": [158, 254, 354, 670, 734, 834], "weekli": [158, 254, 352, 670, 734, 832], "sorted": [158, 159, 254, 670, 671, 734], "metadata": [158, 159, 254, 670, 671, 734], "verifi": [158, 159, 254, 670, 671, 734], "incorrectli": [158, 159, 254, 429, 670, 671, 734], "incorrect": [158, 159, 254, 355, 494, 638, 670, 671, 718, 734, 835, 962, 1049], "re": [158, 217, 254, 337, 338, 670, 734, 817, 818, 1056], "come": [158, 254, 337, 338, 396, 638, 650, 670, 733, 734, 817, 818, 880, 1049], "set_index": [158, 254, 670, 734], "resampl": [158, 254, 670, 734], "reset_index": [158, 254, 670, 734], "though": [158, 254, 670, 734], "evenli": [158, 254, 470, 638, 670, 734, 936, 1049], "upsampl": [158, 254, 670, 734], "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, 482, 483, 485, 488, 489, 490, 638, 670, 734, 792, 793, 795, 798, 799, 800, 801, 802, 803, 804, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 830, 832, 833, 834, 835, 836], "30m": [158, 254, 345, 352, 670, 734, 825, 832], "time_min": [158, 254, 670, 734], "time_max": [158, 254, 670, 734], "23": [158, 159, 254, 318, 322, 326, 342, 345, 354, 482, 483, 485, 488, 489, 490, 589, 625, 626, 627, 638, 670, 671, 734, 737, 794, 802, 1057], "boundari": [158, 254, 298, 299, 300, 352, 638, 670, 734, 774, 775, 776, 832, 856, 1049], "time_count": [158, 254, 670, 734], "_lower_boundari": [158, 254, 670, 734], "_upper_boundari": [158, 254, 670, 734], "lower_bound": [158, 254, 298, 300, 383, 638, 670, 734, 774, 776, 860, 1036, 1049], "upper_bound": [158, 254, 298, 299, 383, 429, 638, 670, 734, 774, 775, 860, 918, 1049], "time_agg_list": [158, 254, 670, 734], "int_rang": [158, 225, 236, 254, 569, 670, 718, 734], "2i": [158, 254, 670, 734], "3i": [158, 254, 670, 734], "a_agg_list": [158, 254, 670, 734], "rollinggroupbi": [159, 254], "dynamic_groupbi": [159, 254, 671, 734], "groupby_dynam": [159, 254, 671, 734], "t_0": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "t_1": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "t_n": [159, 254, 482, 483, 484, 485, 486, 488, 489, 490, 638, 671, 734], "19": [159, 173, 254, 345, 483, 485, 537, 638, 671, 676, 734, 825, 832, 1007], "43": [159, 254, 308, 489, 638, 671, 734], "strptime": [159, 254, 344, 671, 734, 824], "set_sort": [159, 173, 227, 254, 638, 671, 676, 734, 1049], "2d": [159, 217, 254, 603, 671, 734, 792, 798], "sum_a": [159, 254, 671, 734], "min_a": [159, 254, 671, 734], "max_a": [159, 254, 671, 734], "seed": [160, 198, 254, 378, 473, 492, 498, 638, 854, 938, 957, 968, 1049], "seed_1": [160, 254, 378, 638, 854, 1049], "seed_2": [160, 254, 378, 638, 854, 1049], "seed_3": [160, 254, 378, 638, 854, 1049], "hash": [160, 254, 638, 1049], "u64": [160, 254, 378, 475, 638, 689, 734, 854, 1049], "10783150408545073287": [160, 254], "1438741209321515184": [160, 254], "10047419486152048166": [160, 254], "2047317070637311557": [160, 254], "ab": [161, 175, 210, 231, 254, 543, 544, 638, 712, 734, 855, 882, 1019, 1049], "grown": [163, 254], "intermedi": [165, 254, 674, 734, 1057], "linear": [165, 189, 246, 254, 382, 405, 442, 471, 486, 577, 578, 613, 638, 674, 690, 728, 734, 859, 888, 937, 951, 1049], "mask": [166, 168, 254, 287, 384, 386, 394, 509, 511, 638, 761, 849, 860, 862, 865, 877, 933, 934, 960, 979, 981, 1040, 1049], "visual": [166, 168, 254], "buffer_s": [170, 254], "intern": [170, 254], "veri": [170, 222, 254, 268, 638, 655, 718, 734, 777, 1049], "fit": [170, 205, 225, 254, 497, 615, 638, 966, 967, 1049], "signific": [170, 254, 268, 638], "4x": [170, 254], "materialis": [170, 171, 195, 196, 197, 254], "rows_by_kei": [170, 196, 254], "iter_row": [171, 195, 196, 197, 254], "17_500": [171, 254], "klmnoopqrstuvwxyz": [171, 254], "__name__": [171, 254], "7500": [171, 254], "15_000": [171, 254], "record_batch": [171, 254], "to_arrow": [171, 254, 1049], "to_batch": [171, 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"polars.all"]], "all_horizontal() (in module polars)": [[564, "polars.all_horizontal"]], "any() (in module polars)": [[565, "polars.any"]], "any_horizontal() (in module polars)": [[566, "polars.any_horizontal"]], "apply() (in module polars)": [[567, "polars.apply"]], "approx_unique() (in module polars)": [[568, "polars.approx_unique"]], "arange() (in module polars)": [[569, "polars.arange"]], "arctan2() (in module polars)": [[570, "polars.arctan2"]], "arctan2d() (in module polars)": [[571, "polars.arctan2d"]], "arg_sort_by() (in module polars)": [[572, "polars.arg_sort_by"]], "arg_where() (in module polars)": [[573, "polars.arg_where"]], "avg() (in module polars)": [[574, "polars.avg"]], "coalesce() (in module polars)": [[575, "polars.coalesce"]], "col() (in module polars)": [[576, "polars.col"]], "concat_list() (in module polars)": [[577, "polars.concat_list"]], "concat_str() (in module polars)": [[578, "polars.concat_str"]], "corr() (in module polars)": [[579, "polars.corr"]], "count() (in module polars)": [[580, "polars.count"]], "cov() (in module polars)": [[581, "polars.cov"]], "cumfold() (in module polars)": [[582, "polars.cumfold"]], "cumreduce() (in module polars)": [[583, "polars.cumreduce"]], "cumsum() (in module polars)": [[584, "polars.cumsum"]], "cumsum_horizontal() (in module polars)": [[585, "polars.cumsum_horizontal"]], "date() (in module polars)": [[586, "polars.date"]], "date_range() (in module polars)": [[587, "polars.date_range"]], "date_ranges() (in module polars)": [[588, "polars.date_ranges"]], "datetime() (in module polars)": [[589, "polars.datetime"]], "duration() (in module polars)": [[590, "polars.duration"]], "element() (in module polars)": [[591, "polars.element"]], "exclude() (in module polars)": [[592, "polars.exclude"]], "first() (in module polars)": [[593, "polars.first"]], "fold() (in module polars)": [[594, "polars.fold"]], "format() (in module polars)": [[595, "polars.format"]], "from_epoch() (in module polars)": [[596, 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"arg_max() (polars.series method)": [[751, "polars.Series.arg_max"]], "arg_min() (polars.series method)": [[752, "polars.Series.arg_min"]], "arg_sort() (polars.series method)": [[753, "polars.Series.arg_sort"]], "arg_true() (polars.series method)": [[754, "polars.Series.arg_true"]], "arg_unique() (polars.series method)": [[755, "polars.Series.arg_unique"]], "max() (polars.series.arr method)": [[756, "polars.Series.arr.max"]], "min() (polars.series.arr method)": [[757, "polars.Series.arr.min"]], "sum() (polars.series.arr method)": [[758, "polars.Series.arr.sum"]], "unique() (polars.series.arr method)": [[759, "polars.Series.arr.unique"]], "contains() (polars.series.bin method)": [[760, "polars.Series.bin.contains"]], "decode() (polars.series.bin method)": [[761, "polars.Series.bin.decode"]], "encode() (polars.series.bin method)": [[762, "polars.Series.bin.encode"]], "ends_with() (polars.series.bin method)": [[763, "polars.Series.bin.ends_with"]], "starts_with() (polars.series.bin 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\ No newline at end of file