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# pandas.api.typing.Expanding.cov

Expanding.cov(other=None, pairwise=None, ddof=1, numeric_only=False)

  Calculate the expanding sample covariance.

  When `other` is provided, computes pairwise covariance between `self` and `other`; otherwise computes all pairwise covariances of the columns.

  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 MultiIndexed DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.

    ddof int, default 1

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

    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.expanding`

    Calling expanding with Series data.

  `DataFrame.expanding`

    Calling expanding with DataFrames.

  `Series.cov`

    Aggregating cov for Series.

  `DataFrame.cov`

    Aggregating cov for DataFrame.

  Examples

      >>> ser1 = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"])
      >>> ser2 = pd.Series([10, 11, 13, 16], index=["a", "b", "c", "d"])
      >>> ser1.expanding().cov(ser2)
      a         NaN
      b    0.500000
      c    1.500000
      d    3.333333
      dtype: float64