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Allow subtypes of List[Scalar] by introducing ScalarArg #124

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Merged
merged 4 commits into from
Jul 10, 2022
Merged

Allow subtypes of List[Scalar] by introducing ScalarArg #124

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0cee941
Allow subtypes of List[Scalar] by introducing ScalarArg
Dr-Irv Jul 10, 2022
76ffbdd
add check on result
Dr-Irv Jul 10, 2022
46e7637
black on test_frame.py
Dr-Irv Jul 10, 2022
5dfdcf1
ScalarArg -> ScalarT
Dr-Irv Jul 10, 2022
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1 change: 1 addition & 0 deletions pandas-stubs/_typing.pyi
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Expand Up @@ -122,6 +122,7 @@ Scalar = Union[
Timestamp,
Timedelta,
]
ScalarArg = TypeVar("ScalarArg", bound=Scalar)
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Maybe ScalarT to emphasize that it is a TypeVar.

Even though _typing.py is private, it might be good to make all variables that are not actually in pandas explicitly private. Users might want to import some of the type variables from pandas to annotate their own code.

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Maybe ScalarT to emphasize that it is a TypeVar.

Good idea. Done in next commit.

Even though _typing.py is private, it might be good to make all variables that are not actually in pandas explicitly private. Users might want to import some of the type variables from pandas to annotate their own code.

That could be something we do in the future. One thing I would like to do is reconcile the things in pandas._typing.py and pandas-stubs._typing.pyi, because there are some inconsistencies. Created #128

# Refine the definitions below in 3.9 to use the specialized type.
np_ndarray_int8 = npt.NDArray[np.int8]
np_ndarray_int16 = npt.NDArray[np.int16]
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3 changes: 2 additions & 1 deletion pandas-stubs/core/frame.pyi
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Expand Up @@ -65,6 +65,7 @@ from pandas._typing import (
MaskType,
Renamer,
Scalar,
ScalarArg,
SeriesAxisType,
StrLike,
T as TType,
Expand Down Expand Up @@ -368,7 +369,7 @@ class DataFrame(NDFrame, OpsMixin):
Series[_bool],
DataFrame,
List[_str],
List[Scalar],
List[ScalarArg],
Index,
np_ndarray_str,
np_ndarray_bool,
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8 changes: 8 additions & 0 deletions tests/test_frame.py
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Expand Up @@ -1169,3 +1169,11 @@ def test_func(h: Hashable):
test_func(pd.DataFrame()) # type: ignore[arg-type]
test_func(pd.Series([], dtype=object)) # type: ignore[arg-type]
test_func(pd.Index([])) # type: ignore[arg-type]


def test_columns_mixlist() -> None:
# GH 97
df = pd.DataFrame({"a": [1, 2, 3], 1: [3, 4, 5]})
key: List[Union[int, str]]
key = [1]
check(assert_type(df[key], pd.DataFrame), pd.DataFrame)