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. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across 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.

**kwargs

Additional keyword arguments to be passed to the function.

Returns:

Series or scalar: Unbiased skew over requested axis.

See also

Dataframe.kurt: Returns unbiased kurtosis over requested axis.

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   c
tiger   1   2   1
zebra   2   3   3
cow     3   4   5
>>> df.skew()
a   0.0
b   0.0
c   0.0
dtype: float64

Using axis=1

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

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame(
...     {"a": [1, 2, 3], "b": ["T", "Z", "X"]}, index=["tiger", "zebra", "cow"]
... )
>>> df.skew(numeric_only=True)
a   0.0
dtype: float64