e.groupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
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    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
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    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
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    * [ 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)
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  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

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

#  pandas.core.groupby.SeriesGroupBy.cov  #

SeriesGroupBy.  cov  (  _ other  _ , _ min_periods  =  None  _ , _ ddof  =  1
_ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L1200-L1207) #

    

Compute covariance with Series, excluding missing values.

The two  Series  objects are not required to be the same length and will be
aligned internally before the covariance is calculated.

Parameters  :

    

**other** Series

    

Series with which to compute the covariance.

**min_periods** int, optional

    

Minimum number of observations needed to have a valid result.

**ddof** int, default 1

    

Delta degrees of freedom. The divisor used in calculations is ` N  -  ddof  `
, where ` N  ` represents the number of elements.

Returns  :

    

float

    

Covariance between Series and other normalized by N-1 (unbiased estimator).

See also

` DataFrame.cov  `

    

Compute pairwise covariance of columns.

Examples

    
    
    >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
    >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
    >>> s1.cov(s2)
    -0.01685762652715874
    

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