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Classe « Series »

Méthode pandas.Series.std

Signature de la méthode std

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

Description

help(Series.std)

Return sample standard deviation 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.std 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) 

Notes
-----
To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
default `ddof=1`)

Examples
--------
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                    'age': [21, 25, 62, 43],
...                    'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417
dtype: float64

Alternatively, `ddof=0` can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609
dtype: float64

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