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CLN/PERF: Simplify argmin/argmax #58019

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Apr 1, 2024
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CLN/PERF: Simplify argmin/argmax #58019

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5cab58d
CLN/PERF: Simplify argmin/argmax
rhshadrach Mar 26, 2024
0517148
More simplifications
rhshadrach Mar 26, 2024
7ad5f94
Partial revert
rhshadrach Mar 26, 2024
c9bd1f8
Remove comments
rhshadrach Mar 26, 2024
46bbd5e
fixups
rhshadrach Mar 30, 2024
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4 changes: 2 additions & 2 deletions pandas/core/arrays/_mixins.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,15 +210,15 @@ def argmin(self, axis: AxisInt = 0, skipna: bool = True): # type: ignore[overri
# override base class by adding axis keyword
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return nargminmax(self, "argmin", axis=axis)

# Signature of "argmax" incompatible with supertype "ExtensionArray"
def argmax(self, axis: AxisInt = 0, skipna: bool = True): # type: ignore[override]
# override base class by adding axis keyword
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return nargminmax(self, "argmax", axis=axis)

def unique(self) -> Self:
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/arrays/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -885,7 +885,7 @@ def argmin(self, skipna: bool = True) -> int:
# 2. argmin itself : total control over sorting.
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return nargminmax(self, "argmin")

def argmax(self, skipna: bool = True) -> int:
Expand Down Expand Up @@ -919,7 +919,7 @@ def argmax(self, skipna: bool = True) -> int:
# 2. argmax itself : total control over sorting.
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return nargminmax(self, "argmax")

def interpolate(
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/arrays/sparse/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1623,13 +1623,13 @@ def _argmin_argmax(self, kind: Literal["argmin", "argmax"]) -> int:
def argmax(self, skipna: bool = True) -> int:
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return self._argmin_argmax("argmax")

def argmin(self, skipna: bool = True) -> int:
validate_bool_kwarg(skipna, "skipna")
if not skipna and self._hasna:
raise NotImplementedError
raise ValueError("Encountered an NA value with skipna=False")
return self._argmin_argmax("argmin")

# ------------------------------------------------------------------------
Expand Down
16 changes: 3 additions & 13 deletions pandas/core/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -735,13 +735,8 @@ def argmax(
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)

if skipna and len(delegate) > 0 and isna(delegate).all():
raise ValueError("Encountered all NA values")
elif not skipna and isna(delegate).any():
raise ValueError("Encountered an NA value with skipna=False")

if isinstance(delegate, ExtensionArray):
return delegate.argmax()
return delegate.argmax(skipna=skipna)
else:
result = nanops.nanargmax(delegate, skipna=skipna)
# error: Incompatible return value type (got "Union[int, ndarray]", expected
Expand All @@ -754,15 +749,10 @@ def argmin(
) -> int:
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)

if skipna and len(delegate) > 0 and isna(delegate).all():
raise ValueError("Encountered all NA values")
elif not skipna and isna(delegate).any():
raise ValueError("Encountered an NA value with skipna=False")
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)

if isinstance(delegate, ExtensionArray):
return delegate.argmin()
return delegate.argmin(skipna=skipna)
else:
result = nanops.nanargmin(delegate, skipna=skipna)
# error: Incompatible return value type (got "Union[int, ndarray]", expected
Expand Down
15 changes: 7 additions & 8 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6975,11 +6975,11 @@ def argmin(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
nv.validate_minmax_axis(axis)

if not self._is_multi and self.hasnans:
# Take advantage of cache
if self._isnan.all():
raise ValueError("Encountered all NA values")
elif not skipna:
if not skipna:
raise ValueError("Encountered an NA value with skipna=False")
elif self._isnan.all():
raise ValueError("Encountered all NA values")

return super().argmin(skipna=skipna)

@Appender(IndexOpsMixin.argmax.__doc__)
Expand All @@ -6988,11 +6988,10 @@ def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
nv.validate_minmax_axis(axis)

if not self._is_multi and self.hasnans:
# Take advantage of cache
if self._isnan.all():
raise ValueError("Encountered all NA values")
elif not skipna:
if not skipna:
raise ValueError("Encountered an NA value with skipna=False")
elif self._isnan.all():
raise ValueError("Encountered all NA values")
return super().argmax(skipna=skipna)

def min(self, axis=None, skipna: bool = True, *args, **kwargs):
Expand Down
19 changes: 7 additions & 12 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1439,20 +1439,15 @@ def _maybe_arg_null_out(
return result

if axis is None or not getattr(result, "ndim", False):
if skipna:
if mask.all():
raise ValueError("Encountered all NA values")
else:
if mask.any():
raise ValueError("Encountered an NA value with skipna=False")
if skipna and mask.all():
raise ValueError("Encountered all NA values")
elif not skipna and mask.any():
raise ValueError("Encountered an NA value with skipna=False")
else:
na_mask = mask.all(axis)
if na_mask.any():
if skipna and mask.all(axis).any():
raise ValueError("Encountered all NA values")
elif not skipna:
na_mask = mask.any(axis)
if na_mask.any():
raise ValueError("Encountered an NA value with skipna=False")
elif not skipna and mask.any(axis).any():
raise ValueError("Encountered an NA value with skipna=False")
return result


Expand Down
4 changes: 2 additions & 2 deletions pandas/tests/extension/base/methods.py
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Expand Up @@ -191,10 +191,10 @@ def test_argmax_argmin_no_skipna_notimplemented(self, data_missing_for_sorting):
# GH#38733
data = data_missing_for_sorting

with pytest.raises(NotImplementedError, match=""):
with pytest.raises(ValueError, match="Encountered an NA value"):
data.argmin(skipna=False)

with pytest.raises(NotImplementedError, match=""):
with pytest.raises(ValueError, match="Encountered an NA value"):
data.argmax(skipna=False)

@pytest.mark.parametrize(
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4 changes: 2 additions & 2 deletions pandas/tests/frame/test_reductions.py
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Expand Up @@ -1066,7 +1066,7 @@ def test_idxmin(self, float_frame, int_frame, skipna, axis):
frame.iloc[15:20, -2:] = np.nan
for df in [frame, int_frame]:
if (not skipna or axis == 1) and df is not int_frame:
if axis == 1:
if skipna:
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Shouldn't we still be hitting this with the axis == 1 case?

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When axis==1 we run into all NAs for a single row, but when skipna=False we still raise the error message "Encountered an NA value with skipna=False" for performance - see the bottom half of #57971 (comment)

msg = "Encountered all NA values"
else:
msg = "Encountered an NA value"
Expand Down Expand Up @@ -1116,7 +1116,7 @@ def test_idxmax(self, float_frame, int_frame, skipna, axis):
frame.iloc[15:20, -2:] = np.nan
for df in [frame, int_frame]:
if (skipna is False or axis == 1) and df is frame:
if axis == 1:
if skipna:
msg = "Encountered all NA values"
else:
msg = "Encountered an NA value"
Expand Down
18 changes: 10 additions & 8 deletions pandas/tests/reductions/test_reductions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,9 +171,9 @@ def test_argminmax(self):
obj.argmin()
with pytest.raises(ValueError, match="Encountered all NA values"):
obj.argmax()
with pytest.raises(ValueError, match="Encountered all NA values"):
with pytest.raises(ValueError, match="Encountered an NA value"):
obj.argmin(skipna=False)
with pytest.raises(ValueError, match="Encountered all NA values"):
with pytest.raises(ValueError, match="Encountered an NA value"):
obj.argmax(skipna=False)

obj = Index([NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), NaT])
Expand All @@ -189,9 +189,9 @@ def test_argminmax(self):
obj.argmin()
with pytest.raises(ValueError, match="Encountered all NA values"):
obj.argmax()
with pytest.raises(ValueError, match="Encountered all NA values"):
with pytest.raises(ValueError, match="Encountered an NA value"):
obj.argmin(skipna=False)
with pytest.raises(ValueError, match="Encountered all NA values"):
with pytest.raises(ValueError, match="Encountered an NA value"):
obj.argmax(skipna=False)

@pytest.mark.parametrize("op, expected_col", [["max", "a"], ["min", "b"]])
Expand Down Expand Up @@ -856,7 +856,8 @@ def test_idxmin(self):

# all NaNs
allna = string_series * np.nan
with pytest.raises(ValueError, match="Encountered all NA values"):
msg = "Encountered all NA values"
with pytest.raises(ValueError, match=msg):
allna.idxmin()

# datetime64[ns]
Expand Down Expand Up @@ -888,7 +889,8 @@ def test_idxmax(self):

# all NaNs
allna = string_series * np.nan
with pytest.raises(ValueError, match="Encountered all NA values"):
msg = "Encountered all NA values"
with pytest.raises(ValueError, match=msg):
allna.idxmax()

s = Series(date_range("20130102", periods=6))
Expand Down Expand Up @@ -1146,12 +1148,12 @@ def test_idxminmax_object_dtype(self, using_infer_string):
msg = "'>' not supported between instances of 'float' and 'str'"
with pytest.raises(TypeError, match=msg):
ser3.idxmax()
with pytest.raises(ValueError, match="Encountered an NA value"):
with pytest.raises(TypeError, match=msg):
ser3.idxmax(skipna=False)
msg = "'<' not supported between instances of 'float' and 'str'"
with pytest.raises(TypeError, match=msg):
ser3.idxmin()
with pytest.raises(ValueError, match="Encountered an NA value"):
with pytest.raises(TypeError, match=msg):
ser3.idxmin(skipna=False)

def test_idxminmax_object_frame(self):
Expand Down
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