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Classe « Series »

Méthode pandas.Series.cov

Signature de la méthode cov

def cov(self, other: 'Series', min_periods: 'int | None' = None, ddof: 'int | None' = 1) -> 'float' 

Description

help(Series.cov)

Compute covariance with Series, excluding missing values.

The two `Series` objects are not required to be the same length and
will be aligned internally before the covariance is calculated.

Parameters
----------
other : Series
    Series with which to compute the covariance.
min_periods : int, optional
    Minimum number of observations needed to have a valid result.
ddof : int, default 1
    Delta degrees of freedom.  The divisor used in calculations
    is ``N - ddof``, where ``N`` represents the number of elements.

Returns
-------
float
    Covariance between Series and other normalized by N-1
    (unbiased estimator).

See Also
--------
DataFrame.cov : Compute pairwise covariance of columns.

Examples
--------
>>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
>>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
>>> s1.cov(s2)
-0.01685762652715874

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