pandas.core.groupby.SeriesGroupBy.skew#

SeriesGroupBy.skew(axis=<no_default>, skipna=True, numeric_only=False, **kwargs)[source]#

Return unbiased skew within groups.

Normalized by N-1.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0: Axis for the function to be applied on. This parameter is only for compatibility with DataFrame and is unused.

Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.
skipnabool, default True: Exclude NA/null values when computing the result.
numeric_onlybool, default False: Include only float, int, boolean columns. Not implemented for Series.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series

See also

Series.skew: Return unbiased skew over requested axis.

Examples

>>> ser = pd.Series([390., 350., 357., np.nan, 22., 20., 30.],
...                 index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
...                        'Parrot', 'Parrot', 'Parrot'],
...                 name="Max Speed")
>>> ser
Falcon    390.0
Falcon    350.0
Falcon    357.0
Falcon      NaN
Parrot     22.0
Parrot     20.0
Parrot     30.0
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).skew()
Falcon    1.525174
Parrot    1.457863
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).skew(skipna=False)
Falcon         NaN
Parrot    1.457863
Name: Max Speed, dtype: float64