xorbits.pandas.groupby.DataFrameGroupBy.skew#

DataFrameGroupBy.skew(**kw)#

Return unbiased skew within groups.

Normalized by N-1.

Parameters

  • axis ({0 or 'index', 1 or 'columns', None}, default 0 (Not supported yet)) –

    Axis for the function to be applied on.

    Specifying axis=None will apply the aggregation across both axes.

    New in version 2.0.0(pandas).

    Deprecated since version 2.1.0(pandas): For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.

  • skipna (bool, default True (Not supported yet)) – Exclude NA/null values when computing the result.

  • numeric_only (bool, default False (Not supported yet)) – Include only float, int, boolean columns.

  • **kwargs – Additional keyword arguments to be passed to the function.

Return type

DataFrame

See also

DataFrame.skew

Return unbiased skew over requested axis.

Examples

>>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi',  
...            'lion', 'monkey', 'rabbit'],
...           ['bird', 'bird', 'bird', 'bird',
...            'mammal', 'mammal', 'mammal']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class'))  
>>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan,  
...                                  80.5, 21.5, 15.0]},
...                   index=index)
>>> df  
                max_speed
name     class
falcon   bird        389.0
parrot   bird         24.0
cockatoo bird         70.0
kiwi     bird          NaN
lion     mammal       80.5
monkey   mammal       21.5
rabbit   mammal       15.0
>>> gb = df.groupby(["class"])  
>>> gb.skew()  
        max_speed
class
bird     1.628296
mammal   1.669046
>>> gb.skew(skipna=False)  
        max_speed
class
bird          NaN
mammal   1.669046

This docstring was copied from pandas.core.groupby.generic.DataFrameGroupBy.