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Classe « DataFrame »

Méthode pandas.DataFrame.corrwith

Signature de la méthode corrwith

def corrwith(self, other: 'DataFrame | Series', axis: 'Axis' = 0, drop: 'bool' = False, method: 'CorrelationMethod' = 'pearson', numeric_only: 'bool' = False) -> 'Series' 

Description

help(DataFrame.corrwith)

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of
DataFrame with rows or columns of Series or DataFrame. DataFrames
are first aligned along both axes before computing the
correlations.

Parameters
----------
other : DataFrame, Series
    Object with which to compute correlations.
axis : {0 or 'index', 1 or 'columns'}, default 0
    The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for
    column-wise.
drop : bool, default False
    Drop missing indices from result.
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.

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
-------
Series
    Pairwise correlations.

See Also
--------
DataFrame.corr : Compute pairwise correlation of columns.

Examples
--------
>>> index = ["a", "b", "c", "d", "e"]
>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
>>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
>>> df1.corrwith(df2)
one      1.0
two      1.0
three    1.0
four     1.0
dtype: float64

>>> df2.corrwith(df1, axis=1)
a    1.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

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