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Classe « Series »

Méthode pandas.Series.sem

Signature de la méthode sem

def sem(self, axis: 'Axis | None' = None, skipna: 'bool' = True, ddof: 'int' = 1, numeric_only: 'bool' = False, **kwargs) 

Description

help(Series.sem)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
----------
axis : {index (0)}
    For `Series` this parameter is unused and defaults to 0.

    .. warning::

        The behavior of DataFrame.sem with ``axis=None`` is deprecated,
        in a future version this will reduce over both axes and return a scalar
        To retain the old behavior, pass axis=0 (or do not pass axis).

skipna : bool, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
ddof : int, default 1
    Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
    where N represents the number of elements.
numeric_only : bool, default False
    Include only float, int, boolean columns. Not implemented for Series.

Returns
-------
scalar or Series (if level specified) 

            Examples
            --------
            >>> s = pd.Series([1, 2, 3])
            >>> s.sem().round(6)
            0.57735

            With a DataFrame

            >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])
            >>> df
                   a   b
            tiger  1   2
            zebra  2   3
            >>> df.sem()
            a   0.5
            b   0.5
            dtype: float64

            Using axis=1

            >>> df.sem(axis=1)
            tiger   0.5
            zebra   0.5
            dtype: float64

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

            >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},
            ...                   index=['tiger', 'zebra'])
            >>> df.sem(numeric_only=True)
            a   0.5
            dtype: float64

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