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Classe « Series »

Méthode pandas.Series.corr

Signature de la méthode corr

def corr(self, other: 'Series', method: 'CorrelationMethod' = 'pearson', min_periods: 'int | None' = None) -> 'float' 

Description

help(Series.corr)

Compute correlation with `other` Series, excluding missing values.

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

Parameters
----------
other : Series
    Series with which to compute the correlation.
method : {'pearson', 'kendall', 'spearman'} or callable
    Method used to compute correlation:

    - pearson : Standard correlation coefficient
    - kendall : Kendall Tau correlation coefficient
    - spearman : Spearman rank correlation
    - callable: Callable with input two 1d ndarrays and returning a float.

    .. warning::
        Note that the returned matrix from corr will have 1 along the
        diagonals and will be symmetric regardless of the callable's
        behavior.
min_periods : int, optional
    Minimum number of observations needed to have a valid result.

Returns
-------
float
    Correlation with other.

See Also
--------
DataFrame.corr : Compute pairwise correlation between columns.
DataFrame.corrwith : Compute pairwise correlation with another
    DataFrame or Series.

Notes
-----
Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

* `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
* `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
* `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_

Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method.
``corr()`` automatically considers values with matching indices.

Examples
--------
>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> s1 = pd.Series([.2, .0, .6, .2])
>>> s2 = pd.Series([.3, .6, .0, .1])
>>> s1.corr(s2, method=histogram_intersection)
0.3

Pandas auto-aligns the values with matching indices

>>> s1 = pd.Series([1, 2, 3], index=[0, 1, 2])
>>> s2 = pd.Series([1, 2, 3], index=[2, 1, 0])
>>> s1.corr(s2)
-1.0

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