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Classe « DataFrame »

Méthode pandas.DataFrame.corr

Signature de la méthode corr

def corr(self, method: 'CorrelationMethod' = 'pearson', min_periods: 'int' = 1, numeric_only: 'bool' = False) -> 'DataFrame' 

Description

help(DataFrame.corr)

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable
    Method of 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. 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 required per pair of columns
    to have a valid result. Currently only available for Pearson
    and Spearman correlation.
numeric_only : bool, default False
    Include only `float`, `int` or `boolean` data.

    .. versionadded:: 1.5.0

    .. versionchanged:: 2.0.0
        The default value of ``numeric_only`` is now ``False``.

Returns
-------
DataFrame
    Correlation matrix.

See Also
--------
DataFrame.corrwith : Compute pairwise correlation with another
    DataFrame or Series.
Series.corr : Compute the correlation between two 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>`_

Examples
--------
>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

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