y.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.corrwith  #

DataFrameGroupBy.  corrwith  (  _ other  _ , _ axis  =  _NoDefault.no_default
_ , _ drop  =  False  _ , _ method  =  'pearson'  _ , _ numeric_only  =  False
_ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L2808-L2825) #

    

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  :

    

**other** DataFrame, Series

    

Object with which to compute correlations.

**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0

    

The axis to use. 0 or âindexâ to compute row-wise, 1 or âcolumnsâ for
column-wise.

**drop** bool, default False

    

Drop missing indices from result.

**method** {âpearsonâ, âkendallâ, âspearmanâ} 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_only** bool, 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

    
    
    >>> 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, a