oupby.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.corr  #

DataFrameGroupBy.  corr  (  _ method  =  'pearson'  _ , _ min_periods  =  1  _
, _ numeric_only  =  False  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L2727-L2737) #

    

Compute pairwise correlation of columns, excluding NA/null values.

Parameters  :

    

**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. Note that the returned matrix from corr will have 1
along the diagonals and will be symmetric regardless of the callableâs
behavior.

**min_periods** int, optional

    

Minimum number of observations required per pair of columns to have a valid
result. Currently only available for Pearson and Spearman correlation.

**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  :

    

DataFrame

    

Correlation matrix.

See also

` DataFrame.corrwith  `

    

Compute pairwise correlation with another DataFrame or Series.

` Series.corr  `

    

Compute the correlation between two Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using
pairwise complete observations.

  * [ Pearson correlation coefficient ](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)

  * [ Kendall rank correlation coefficient ](https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient)

  * [ Spearmanâs rank correlation coefficient ](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient)

Examples

    
    
    >>> def histogram_intersection(a, b):
    ...     v = np.minimum(a,