pandas.DataFrame.corrwith¶

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.

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

pandas.DataFrame.corr

pandas.DataFrame.count