roupby.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.cov  #

DataFrameGroupBy.  cov  (  _ min_periods  =  None  _ , _ ddof  =  1  _ , _
numeric_only  =  False  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L2739-L2749) #

    

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

Compute the pairwise covariance among the series of a DataFrame. The returned
data frame is the [ covariance matrix
](https://en.wikipedia.org/wiki/Covariance_matrix) of the columns of the
DataFrame.

Both NA and null values are automatically excluded from the calculation. (See
the note below about bias from missing values.) A threshold can be set for the
minimum number of observations for each value created. Comparisons with
observations below this threshold will be returned as ` NaN  ` .

This method is generally used for the analysis of time series data to
understand the relationship between different measures across time.

Parameters  :

    

**min_periods** int, optional

    

Minimum number of observations required per pair of columns 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. This argument is applicable
only when no ` nan  ` is in the dataframe.

**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

    

The covariance matrix of the series of the DataFrame.

See also

` Series.cov  `

    

Compute covariance with another Series.

` core.window.ewm.ExponentialMovingWindow.cov  `

    

Exponential weighted sample covariance.

` core.window.expanding.Expanding.cov  `

    

Expanding sample covariance.

` core.window.rolling.Rolling.cov  `

    

Rolling sample covariance.

Notes

Returns the covariance m