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Classe « Series »

Méthode pandas.Series.skew

Signature de la méthode skew

def skew(self, axis: 'Axis | None' = 0, skipna: 'bool' = True, numeric_only: 'bool' = False, **kwargs) 

Description

help(Series.skew)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters
----------
axis : {index (0)}
    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.

    .. versionadded:: 2.0.0

skipna : bool, default True
    Exclude NA/null values when computing the result.
numeric_only : bool, default False
    Include only float, int, boolean columns. Not implemented for Series.

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

Returns
-------
scalar 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   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

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