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# pandas.api.typing.DataFrameGroupBy.corr

DataFrameGroupBy.corr(method='pearson', min_periods=1, numeric_only=False)

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

  Parameters:
    method{‘pearson’, ‘kendall’, ‘spearman’} or callable

      Method of correlation:

      - pearson : standard correlation coefficient

      - kendall : Kendall Tau correlation coefficient

      - spearman : Spearman rank correlation

      - callable: callable with input two 1d ndarrays

          and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

    min_periods int, optional

      Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

    numeric_only bool, default False

      Include only float, int or boolean data.

      Changed in version 2.0.0: The default value of `numeric_only` is now `False`.

  Returns:
    DataFrame

      Correlation matrix.

  See also

  `DataFrame.corrwith`

    Compute pairwise correlation with another DataFrame or Series.

  `Series.corr`

    Compute the correlation between two Series.

  Notes

  Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

  - Pearson correlation coefficient

  - Kendall rank correlation coefficient

  - Spearman’s rank correlation coefficient

  Examples

      >>> def histogram_intersection(a, b):
      ...     v = np.minimum(a, b).sum().round(decimals=1)
      ...     return v
      >>> df = pd.DataFrame(
      ...     [(0.2, 0.3), (0.0, 0.6), (0.6, 0.0), (0.2, 0.1)],
      ...     columns=["dogs", "cats"],
      ... )
      >>> df.corr(method=histogram_intersection)
            dogs  cats
      dogs   1.0   0.3
      cats   0.3   1.0

      >>> df = pd.DataFrame(
      ...     [(1, 1), (2, np.nan), (np.nan, 3), (4, 4)], columns=["dogs", "cats"]
      ... )
      >>> df.corr(min_periods=3)
            dogs  cats
      dogs   1.0   NaN
      cats   NaN   1.0