pandas.DataFrame.skew#

DataFrame.skew(axis=0,skipna=True,numeric_only=False,**kwargs)[source]#

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters:

axis{index (0), columns (1)}

Axis for the function to be applied on.ForSeries this parameter is unused and defaults to 0.

For DataFrames, specifyingaxis=None will apply the aggregationacross both axes.

Added in version 2.0.0.

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 or scalar

Examples

>>>s=pd.Series([1,2,3])>>>s.skew()0.0

With a DataFrame

>>>df=pd.DataFrame({'a':[1,2,3],'b':[2,3,4],'c':[1,3,5]},...index=['tiger','zebra','cow'])>>>df        a   b   ctiger   1   2   1zebra   2   3   3cow     3   4   5>>>df.skew()a   0.0b   0.0c   0.0dtype: float64

Using axis=1

>>>df.skew(axis=1)tiger   1.732051zebra  -1.732051cow     0.000000dtype: float64

In this case,numeric_only should be set toTrue to avoidgetting an error.

>>>df=pd.DataFrame({'a':[1,2,3],'b':['T','Z','X']},...index=['tiger','zebra','cow'])>>>df.skew(numeric_only=True)a   0.0dtype: float64