pandas.core.groupby.DataFrameGroupBy.skew#

DataFrameGroupBy.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.

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

Added in version 2.0.0.

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.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

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