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# pandas.api.typing.ExponentialMovingWindow.cov

ExponentialMovingWindow.cov(other=None, pairwise=None, bias=False, numeric_only=False)

  Calculate the ewm (exponential weighted moment) sample covariance.

  The weighting is controlled by the `com`, `span`, `halflife`, or `alpha` parameter specified when calling `DataFrame.ewm()` or `Series.ewm()`.

  Parameters:
    other Series or DataFrame , optional

      If not supplied then will default to self and produce pairwise output.

    pairwise bool, default None

      If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.

    bias bool, default False

      Use a standard estimation bias correction.

    numeric_only bool, default False

      Include only float, int, boolean columns.

  Returns:
    Series or DataFrame

      Return type is the same as the original object with `np.float64` dtype.

  See also

  `Series.ewm`

    Calling ewm with Series data.

  `DataFrame.ewm`

    Calling ewm with DataFrames.

  `Series.cov`

    Aggregating cov for Series.

  `DataFrame.cov`

    Aggregating cov for DataFrame.

  Examples

      >>> ser1 = pd.Series([1, 2, 3, 4])
      >>> ser2 = pd.Series([10, 11, 13, 16])
      >>> ser1.ewm(alpha=0.2).cov(ser2)
      0         NaN
      1    0.500000
      2    1.524590
      3    3.408836
      dtype: float64