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Eliminated the skipna / dropna inconsistency in the docs. #34209

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Eliminated the skipna / dropna inconsistency in the docs. #34209

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2bf6bb2
Update strings.py
ashwinpn Mar 6, 2020
2e5852d
Update strings.py
ashwinpn Mar 6, 2020
a7b18db
Merge pull request #1 from pandas-dev/master
ashwinpn May 16, 2020
544fb3e
Eliminated the skipna / dropna inconsistency.
ashwinpn May 16, 2020
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20 changes: 10 additions & 10 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8367,7 +8367,7 @@ def _count_level(self, level, axis=0, numeric_only=False):
return result

def _reduce(
self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds
self, op, name, axis=0, dropna=True, numeric_only=None, filter_type=None, **kwds
):

assert filter_type is None or filter_type == "bool", filter_type
Expand Down Expand Up @@ -8400,7 +8400,7 @@ def _reduce(
constructor = self._constructor

def f(x):
return op(x, axis=axis, skipna=skipna, **kwds)
return op(x, axis=axis, dropna=dropna, **kwds)

def _get_data(axis_matters):
if filter_type is None:
Expand Down Expand Up @@ -8431,9 +8431,9 @@ def _get_data(axis_matters):

def blk_func(values):
if isinstance(values, ExtensionArray):
return values._reduce(name, skipna=skipna, **kwds)
return values._reduce(name, dropna=dropna, **kwds)
else:
return op(values, axis=1, skipna=skipna, **kwds)
return op(values, axis=1, dropna=dropna, **kwds)

# After possibly _get_data and transposing, we are now in the
# simple case where we can use BlockManager._reduce
Expand Down Expand Up @@ -8562,7 +8562,7 @@ def nunique(self, axis=0, dropna=True) -> Series:
"""
return self.apply(Series.nunique, axis=axis, dropna=dropna)

def idxmin(self, axis=0, skipna=True) -> Series:
def idxmin(self, axis=0, dropna=True) -> Series:
"""
Return index of first occurrence of minimum over requested axis.

Expand All @@ -8572,7 +8572,7 @@ def idxmin(self, axis=0, skipna=True) -> Series:
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
skipna : bool, default True
dropna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.

Expand Down Expand Up @@ -8624,7 +8624,7 @@ def idxmin(self, axis=0, skipna=True) -> Series:
dtype: object
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
indices = nanops.nanargmin(self.values, axis=axis, dropna=dropna)

# indices will always be np.ndarray since axis is not None and
# values is a 2d array for DataFrame
Expand All @@ -8635,7 +8635,7 @@ def idxmin(self, axis=0, skipna=True) -> Series:
result = [index[i] if i >= 0 else np.nan for i in indices]
return self._constructor_sliced(result, index=self._get_agg_axis(axis))

def idxmax(self, axis=0, skipna=True) -> Series:
def idxmax(self, axis=0, dropna=True) -> Series:
"""
Return index of first occurrence of maximum over requested axis.

Expand All @@ -8645,7 +8645,7 @@ def idxmax(self, axis=0, skipna=True) -> Series:
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
skipna : bool, default True
dropna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.

Expand Down Expand Up @@ -8697,7 +8697,7 @@ def idxmax(self, axis=0, skipna=True) -> Series:
dtype: object
"""
axis = self._get_axis_number(axis)
indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
indices = nanops.nanargmax(self.values, axis=axis, dropna=dropna)

# indices will always be np.ndarray since axis is not None and
# values is a 2d array for DataFrame
Expand Down