.groupby.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.SeriesGroupBy.corr  #

SeriesGroupBy.  corr  (  _ other  _ , _ method  =  'pearson'  _ , _
min_periods  =  None  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L1188-L1198) #

    

Compute correlation with  other  Series, excluding missing values.

The two  Series  objects are not required to be the same length and will be
aligned internally before the correlation function is applied.

Parameters  :

    

**other** Series

    

Series with which to compute the correlation.

**method** {âpearsonâ, âkendallâ, âspearmanâ} or callable

    

Method used to compute 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. 

Warning

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 needed to have a valid result.

Returns  :

    

float

    

Correlation with other.

See also

` DataFrame.corr  `

    

Compute pairwise correlation between columns.

` DataFrame.corrwith  `

    

Compute pairwise correlation with another DataFrame or 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)

Automatic data alignment: as with all pandas operations, automatic data
alignment is performed for this method. ` corr()  ` automatically considers
values with matc