pandas.core.groupby.DataFrameGroupBy.corrwith#

DataFrameGroupBy.corrwith(other, drop=False, method='pearson', numeric_only=False)[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.

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.

New 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

>>> df1 = pd.DataFrame(
...     {
...         "Day": [1, 1, 1, 2, 2, 2, 3, 3, 3],
...         "Data": [6, 6, 8, 5, 4, 2, 7, 3, 9],
...     }
... )
>>> df2 = pd.DataFrame(
...     {
...         "Day": [1, 1, 1, 2, 2, 2, 3, 3, 3],
...         "Data": [5, 3, 8, 3, 1, 1, 2, 3, 6],
...     }
... )
>>> df1.groupby("Day").corrwith(df2)
         Data  Day
Day
1    0.917663  NaN
2    0.755929  NaN
3    0.576557  NaN