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pandas.DataFrame.corrwith — pandas 1.5.1 documentation

DataFrame.corrwith(other, axis=0, drop=False, method='pearson', numeric_only=_NoDefault.no_default)[source]#

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

otherDataFrame, 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.

dropbool, 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_onlybool, default True

Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of numeric_only will be False in a future version of pandas.

Returns

Series

Pairwise correlations.

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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