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@MarcoGorelli
MarcoGorelli committed Mar 15, 2024
1 parent 5f916d5 commit 6c1ad8c
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Showing 2 changed files with 89 additions and 92 deletions.
1 change: 0 additions & 1 deletion narwhals/pandas_like/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,6 @@ def evaluate_into_expr(df: PandasDataFrame, into_expr: IntoExpr) -> list[PandasS
"""
Return list of raw columns.
"""

expr = parse_into_expr(df._implementation, into_expr)
return expr._call(df)

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180 changes: 89 additions & 91 deletions t.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,123 +5,121 @@

import narwhals as nw

df_raw = pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
df = nw.DataFrame(df_raw, is_lazy=True)
df_raw_2 = pd.DataFrame({"a": [1, 3], "c": [7, 9]})
df2 = nw.DataFrame(df_raw_2, is_lazy=True)
# df_raw = pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
# df = nw.DataFrame(df_raw, is_lazy=True)
# df_raw_2 = pd.DataFrame({"a": [1, 3], "c": [7, 9]})
# df2 = nw.DataFrame(df_raw_2, is_lazy=True)

result = df.sort("a", "b")
print(nw.to_native(result))
# result = df.sort("a", "b")
# print(nw.to_native(result))

result = df.filter(nw.col("a") > 1)
print(nw.to_native(result))
# result = df.filter(nw.col("a") > 1)
# print(nw.to_native(result))

result = df.with_columns(
c=nw.col("a") + nw.col("b"),
d=nw.col("a") - nw.col("a").mean(),
)
print(nw.to_native(result))
result = df.with_columns(nw.all() * 2)
print(nw.to_native(result))
# result = df.with_columns(
# c=nw.col("a") + nw.col("b"),
# d=nw.col("a") - nw.col("a").mean(),
# )
# print(nw.to_native(result))
# result = df.with_columns(nw.all() * 2)
# print(nw.to_native(result))

result = df.with_columns(horizonal_sum=nw.sum_horizontal(nw.col("a"), nw.col("b")))
print(nw.to_native(result))
result = df.with_columns(horizonal_sum=nw.sum_horizontal("a", nw.col("b")))
print(nw.to_native(result))
# result = df.with_columns(horizonal_sum=nw.sum_horizontal(nw.col("a"), nw.col("b")))
# print(nw.to_native(result))
# result = df.with_columns(horizonal_sum=nw.sum_horizontal("a", nw.col("b")))
# print(nw.to_native(result))


result = df.select(nw.all().sum())
print(nw.to_native(result))
result = df.select(nw.col("a", "b") * 2)
print(nw.to_native(result))
# result = df.select(nw.all().sum())
# print(nw.to_native(result))
# result = df.select(nw.col("a", "b") * 2)
# print(nw.to_native(result))

# # TODO!
# # result = (
# # df.collect()
# # .group_by("b")
# # .agg(
# # nw.all().sum(),
# # )
# # )
# # print(nw.to_native(result))

result = (
df.collect()
.group_by("b")
.agg(
nw.col("a").sum(),
simple=nw.col("a").sum(),
complex=(nw.col("a") + 1).sum(),
other=nw.sum("a"),
)
)
print(nw.to_native(result))
print("multiple simple")
result = (
df.collect()
.group_by("b")
.agg(
nw.col("a", "z").sum(),
)
)
print(nw.to_native(result))
# # # TODO!
# # # result = (
# # # df.collect()
# # # .group_by("b")
# # # .agg(
# # # nw.all().sum(),
# # # )
# # # )
# # # print(nw.to_native(result))

# result = (
# df.collect()
# .group_by("b")
# .agg(
# nw.col("a").sum(),
# simple=nw.col("a").sum(),
# complex=(nw.col("a") + 1).sum(),
# other=nw.sum("a"),
# )
# )
# print(nw.to_native(result))
# print("multiple simple")
# result = (
# df.collect()
# .group_by("b")
# .agg(
# nw.col("a", "z").sum(),
# )
# )
# print(nw.to_native(result))

result = df.join(df2, left_on="a", right_on="a")
print(nw.to_native(result))
# result = df.join(df2, left_on="a", right_on="a")
# print(nw.to_native(result))


result = df.rename({"a": "a_new", "b": "b_new"})
print(nw.to_native(result))
# result = df.rename({"a": "a_new", "b": "b_new"})
# print(nw.to_native(result))

result = df.collect().to_dict()
print(result)
print(polars.from_pandas(nw.to_native(df)).to_dict())
# result = df.collect().to_dict()
# print(result)
# print(polars.from_pandas(nw.to_native(df)).to_dict())

result = df.collect().to_dict(as_series=False)
print("this")
print(result)
print("that")
print(polars.from_pandas(nw.to_native(df)).to_dict(as_series=False))
# result = df.collect().to_dict(as_series=False)
# print("this")
# print(result)
# print("that")
# print(polars.from_pandas(nw.to_native(df)).to_dict(as_series=False))

agg = (nw.col("b") - nw.col("z").mean()).mean()
print(nw.to_native(df.with_columns(d=agg)))
result = df.group_by("a").agg(agg)
print(nw.to_native(result))
# agg = (nw.col("b") - nw.col("z").mean()).mean()
# print(nw.to_native(df.with_columns(d=agg)))
# result = df.group_by("a").agg(agg)
# print(nw.to_native(result))

print(nw.col("a") + nw.col("b"))
print(nw.col("a", "b").sum())
# print(nw.col("a") + nw.col("b"))
# print(nw.col("a", "b").sum())

result = df.select(nw.col("a", "b").sum())
print(nw.to_native(result))
# result = df.select(nw.col("a", "b").sum())
# print(nw.to_native(result))

print(df.schema)
# print(df.schema)
# print(df.schema['a'].is_numeric())

# df_raw = pd.DataFrame({
# "a": [1, 3, 2],
# "b": [4., 4, 6],
# 'c': ['a', 'b', 'c'],
# 'd': [True, False, True],
# })
# df, pl = narwhals.to_polars_api(df_raw)
df_raw = pd.DataFrame(
{
"a": [1, 3, 2],
"b": [4.0, 4, 6],
"c": ["a", "b", "c"],
"d": [True, False, True],
}
)
df = nw.DataFrame(df_raw)
# print(df.schema)
# print(df.schema['a'].is_numeric())
# print(df.schema['b'].is_numeric())
# print(df.schema['c'].is_numeric())
# print(df.schema['d'].is_numeric())

# result = df.with_columns(nw.col('a').cast(pl.Float32))
# result = df.with_columns(nw.col('a').cast(nw.Float32))
# print(nw.to_native(result))
# print(result._dataframe.dtypes)
# print(result._dataframe._dataframe.dtypes)

# print(df.schema)
# result = df.select([col for (col, dtype) in df.schema.items() if dtype == pl.Float64])
# print(nw.to_native(result))
# print(result._dataframe.dtypes)

# print(nw.all() + nw.col("a"))
# result = df.select(nw.all() + nw.col("a"))
# result = df.select([col for (col, dtype) in df.schema.items() if dtype == nw.Float64])
# print(nw.to_native(result))
# print(result._dataframe.dtypes)
# print(result._dataframe._dataframe.dtypes)

# print(result.collect())
result = df.select("a", "b").select(nw.all() + nw.col("a"))
print(nw.to_native(result))

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