pandas.DataFrame.corrwith#

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

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns ofDataFrame with rows or columns of Series or DataFrame. DataFramesare first aligned along both axes before computing thecorrelations.

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’ forcolumn-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 onlyfloat,int orboolean data.

Added in version 1.5.0.

Changed in version 2.0.0:The default value ofnumeric_only is nowFalse.

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      1.0two      1.0three    1.0four     1.0dtype: float64

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