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# pandas.core.groupby.SeriesGroupBy.cov

SeriesGroupBy.cov(other, min_periods=None, ddof=1)

  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:
    otherSeries

      Series with which to compute the covariance.

    min_periodsint, optional

      Minimum number of observations needed to have a valid result.

    ddofint, 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