pandas.DataFrame.corrwith#

DataFrame.corrwith(other, axis=0, drop=False, method='pearson', numeric_only=False, min_periods=None)[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 False

Include only float, int or boolean data.

min_periodsint, optional

Minimum number of observations needed to have a valid result.

Added in version 1.5.0.

Changed in version 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