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Fix annotations for DataFrame.apply() including additional overloads #448

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Merged
merged 5 commits into from
Dec 5, 2022
Merged

Fix annotations for DataFrame.apply() including additional overloads #448

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c2c1633
Fix annotations for DataFrame.apply() including additional overloads
gandhis1 Nov 26, 2022
2590294
Expand test suite and eliminate edge cases
gandhis1 Nov 26, 2022
40ab8f5
Cover all argument combinations and add a comprehensive set of test
gandhis1 Nov 26, 2022
6c2adb1
Try to remove unnecessary overloads
gandhis1 Nov 26, 2022
7338162
Refine the full set of overloads to cover value type of Series
gandhis1 Dec 4, 2022
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8 changes: 7 additions & 1 deletion pandas-stubs/_typing.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@ from typing import (
Iterator,
Literal,
Mapping,
MutableSequence,
Optional,
Protocol,
Sequence,
Expand Down Expand Up @@ -151,10 +152,15 @@ num: TypeAlias = complex
SeriesAxisType: TypeAlias = Literal[
"index", 0
] # Restricted subset of _AxisType for series
AxisType: TypeAlias = Literal["columns", "index", 0, 1]
AxisTypeIndex: TypeAlias = Literal["index", 0]
AxisTypeColumn: TypeAlias = Literal["columns", 1]
AxisType: TypeAlias = AxisTypeIndex | AxisTypeColumn
DtypeNp = TypeVar("DtypeNp", bound=np.dtype[np.generic])
KeysArgType: TypeAlias = Any
ListLike = TypeVar("ListLike", Sequence, np.ndarray, "Series", "Index")
ListLikeExceptSeriesAndStr = TypeVar(
"ListLikeExceptSeriesAndStr", MutableSequence, np.ndarray, tuple, "Index"
)
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I agree, the name is awful, but do we have a better way to exclude str from list-likes?

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I don't know of any

ListLikeU: TypeAlias = Union[Sequence, np.ndarray, Series, Index]
StrLike: TypeAlias = Union[str, np.str_]
Scalar: TypeAlias = Union[
Expand Down
116 changes: 110 additions & 6 deletions pandas-stubs/core/frame.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,8 @@ from pandas._typing import (
Axes,
Axis,
AxisType,
AxisTypeColumn,
AxisTypeIndex,
CalculationMethod,
ColspaceArgType,
CompressionOptions,
Expand All @@ -82,6 +84,7 @@ from pandas._typing import (
Label,
Level,
ListLike,
ListLikeExceptSeriesAndStr,
ListLikeU,
MaskType,
MergeHow,
Expand Down Expand Up @@ -1086,36 +1089,137 @@ class DataFrame(NDFrame, OpsMixin):
*args,
**kwargs,
) -> DataFrame: ...

# apply() overloads with default result_type of None, and is indifferent to axis
@overload
def apply(
self,
f: Callable[..., Series],
f: Callable[..., ListLikeExceptSeriesAndStr | Series],
axis: AxisTypeIndex = ...,
raw: _bool = ...,
result_type: Literal[None] = ...,
args=...,
**kwargs,
) -> DataFrame: ...
@overload
def apply(
self,
f: Callable[..., S1],
axis: AxisTypeIndex = ...,
raw: _bool = ...,
result_type: Literal[None] = ...,
args=...,
**kwargs,
) -> Series[S1]: ...

# apply() overloads with keyword result_type, and axis does not matter
@overload
def apply(
self,
f: Callable[..., S1],
axis: AxisType = ...,
raw: _bool = ...,
args=...,
*,
result_type: Literal["expand", "reduce"],
**kwargs,
) -> Series[S1]: ...
@overload
def apply(
self,
f: Callable[..., ListLikeExceptSeriesAndStr | Series],
axis: AxisType = ...,
raw: _bool = ...,
result_type: Literal["expand", "reduce", "broadcast"] | None = ...,
args=...,
*,
result_type: Literal["expand"],
**kwargs,
) -> DataFrame: ...
@overload
def apply(
self,
f: Callable[..., Scalar],
f: Callable[..., ListLikeExceptSeriesAndStr],
axis: AxisType = ...,
raw: _bool = ...,
result_type: Literal["expand", "reduce"] | None = ...,
args=...,
*,
result_type: Literal["reduce"],
**kwargs,
) -> Series: ...
@overload
def apply(
self,
f: Callable[..., Scalar],
result_type: Literal["broadcast"],
f: Callable[..., ListLikeExceptSeriesAndStr | Series | Scalar],
axis: AxisType = ...,
raw: _bool = ...,
args=...,
*,
result_type: Literal["broadcast"],
**kwargs,
) -> DataFrame: ...

# apply() overloads with keyword result_type, and axis does matter
@overload
def apply(
self,
f: Callable[..., Series],
axis: AxisTypeIndex = ...,
raw: _bool = ...,
args=...,
*,
result_type: Literal["reduce"],
**kwargs,
) -> Series: ...

# apply() overloads with default result_type of None, and keyword axis=1 matters
@overload
def apply(
self,
f: Callable[..., S1],
raw: _bool = ...,
result_type: Literal[None] = ...,
args=...,
*,
axis: AxisTypeColumn,
**kwargs,
) -> Series[S1]: ...
@overload
def apply(
self,
f: Callable[..., ListLikeExceptSeriesAndStr],
raw: _bool = ...,
result_type: Literal[None] = ...,
args=...,
*,
axis: AxisTypeColumn,
**kwargs,
) -> Series: ...
@overload
def apply(
self,
f: Callable[..., Series],
raw: _bool = ...,
result_type: Literal[None] = ...,
args=...,
*,
axis: AxisTypeColumn,
**kwargs,
) -> DataFrame: ...

# apply() overloads with keyword axis=1 and keyword result_type
@overload
def apply(
self,
f: Callable[..., Series],
raw: _bool = ...,
args=...,
*,
axis: AxisTypeColumn,
result_type: Literal["reduce"],
**kwargs,
) -> DataFrame: ...

# Add spacing between apply() overloads and remaining annotations
def applymap(
self, func: Callable, na_action: Literal["ignore"] | None = ..., **kwargs
) -> DataFrame: ...
Expand Down
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