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GH1488 Add overload for DataFrame.max when axis is None #1490

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Merged
merged 4 commits into from
Nov 17, 2025
+52 −8
Merged

GH1488 Add overload for DataFrame.max when axis is None #1490

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7ac5836
GH1488 Add overload for DataFrame.max when axis is None
loicdiridollou Nov 14, 2025
54882b3
GH1488 PR feedback
loicdiridollou Nov 16, 2025
f5e2d5b
GH1488 PR feedback
loicdiridollou Nov 16, 2025
9088387
GH1488 PR Feedback
loicdiridollou Nov 17, 2025
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44 changes: 40 additions & 4 deletions pandas-stubs/core/frame.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -2344,30 +2344,66 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
axis: Axis | None = ...,
level: Level | None = None,
) -> Self: ...
@overload
def max(
self,
axis: Axis | None = 0,
axis: None,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Scalar: ...
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@cmp0xff cmp0xff Nov 16, 2025

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We can probably further restrict to _OrderableT from pandas-stubs/_libs/intervals.pyi, instead of just Scalar. But this can be done in a separate PR.

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@loicdiridollou loicdiridollou Nov 17, 2025

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Is it a type that can be returned? I thought it was more of a generic for Interval or something like that. I have left it for later.

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@cmp0xff cmp0xff Nov 17, 2025

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I'm not sure, either. @Dr-Irv can we return _OrderableT?

@overload
def max(
self,
axis: Axis = 0,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Series: ...
@overload
def mean(
self,
axis: Axis | None = 0,
axis: None,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Scalar: ...
@overload
def mean(
self,
axis: Axis = 0,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Series: ...
@overload
def median(
self,
axis: Axis | None = 0,
axis: None,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Scalar: ...
@overload
def median(
self,
axis: Axis = 0,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Series: ...
@overload
def min(
self,
axis: Axis | None = 0,
axis: None,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
) -> Scalar: ...
@overload
def min(
self,
axis: Axis = 0,
skipna: _bool | None = True,
numeric_only: _bool = False,
**kwargs: Any,
Expand Down
12 changes: 8 additions & 4 deletions tests/series/test_series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -578,10 +578,14 @@ def test_types_cumsum() -> None:

def test_types_min() -> None:
s = pd.Series([1, 2, 3, np.nan])
s.min()
s.min(axis=0)
s.groupby(level=0).min()
s.min(skipna=False)
check(assert_type(s.min(), float), np.floating)
check(assert_type(s.min(axis=0), float), np.floating)
check(
assert_type(s.groupby(level=0).min(), "pd.Series[float]"),
pd.Series,
np.floating,
)
check(assert_type(s.min(skipna=False), float), np.floating)


def test_types_max() -> None:
Expand Down
4 changes: 4 additions & 0 deletions tests/test_frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -713,6 +713,7 @@ def test_types_mean() -> None:
df = pd.DataFrame(data={"col1": [2, 1], "col2": [3, 4]})
check(assert_type(df.mean(), pd.Series), pd.Series)
check(assert_type(df.mean(axis=0), pd.Series), pd.Series)
check(assert_type(df.mean(axis=None), Scalar), np.floating)
check(assert_type(df.groupby(level=0).mean(), pd.DataFrame), pd.DataFrame)
check(
assert_type(df.mean(axis=1, skipna=True, numeric_only=False), pd.Series),
Expand All @@ -724,6 +725,7 @@ def test_types_median() -> None:
df = pd.DataFrame(data={"col1": [2, 1], "col2": [3, 4]})
check(assert_type(df.median(), pd.Series), pd.Series)
check(assert_type(df.median(axis=0), pd.Series), pd.Series)
check(assert_type(df.median(axis=None), Scalar), np.floating)
check(assert_type(df.groupby(level=0).median(), pd.DataFrame), pd.DataFrame)
check(
assert_type(df.median(axis=1, skipna=True, numeric_only=False), pd.Series),
Expand Down Expand Up @@ -820,12 +822,14 @@ def test_types_min() -> None:
df = pd.DataFrame(data={"col1": [2, 1], "col2": [3, 4]})
check(assert_type(df.min(), pd.Series), pd.Series)
check(assert_type(df.min(axis=0), pd.Series), pd.Series)
check(assert_type(df.min(axis=None), Scalar), np.integer)


def test_types_max() -> None:
df = pd.DataFrame(data={"col1": [2, 1], "col2": [3, 4]})
check(assert_type(df.max(), pd.Series), pd.Series)
check(assert_type(df.max(axis=0), pd.Series), pd.Series)
check(assert_type(df.max(axis=None), Scalar), np.integer)


def test_types_quantile() -> None:
Expand Down