DataFrame.
corrwith
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.
New in version 3.4.0.
Object with which to compute correlations.
Can only be set to 0 now.
Drop missing indices from result.
pearson : standard correlation coefficient
spearman : Spearman rank correlation
kendall : Kendall Tau correlation coefficient
Pairwise correlations.
See also
DataFrame.corr
Compute pairwise correlation of columns.
Examples
>>> df1 = ps.DataFrame({ ... "A":[1, 5, 7, 8], ... "X":[5, 8, 4, 3], ... "C":[10, 4, 9, 3]}) >>> df1.corrwith(df1[["X", "C"]]).sort_index() A NaN C 1.0 X 1.0 dtype: float64
>>> df2 = ps.DataFrame({ ... "A":[5, 3, 6, 4], ... "B":[11, 2, 4, 3], ... "C":[4, 3, 8, 5]})
>>> with ps.option_context("compute.ops_on_diff_frames", True): ... df1.corrwith(df2).sort_index() A -0.041703 B NaN C 0.395437 X NaN dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True): ... df1.corrwith(df2, method="kendall").sort_index() A 0.0 B NaN C 0.0 X NaN dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True): ... df1.corrwith(df2.B, method="spearman").sort_index() A -0.4 C 0.8 X -0.2 dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True): ... df2.corrwith(df1.X).sort_index() A -0.597614 B -0.151186 C -0.642857 dtype: float64