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# pandas.api.typing.SeriesGroupBy.cummax

SeriesGroupBy.cummax(numeric_only=False, **kwargs)

  Cumulative max for each group.

  Parameters:
    numeric_only bool, default False

      Include only float, int or boolean data.

    **kwargs dict, optional

      Additional keyword arguments to be passed to the function, such as skipna, to control whether NA/null values are ignored.

  Returns:
    Series or DataFrame

      Cumulative max for each group. Same object type as the caller.

  See also

  `Series.cummax`

    Apply function cummax to a Series.

  `DataFrame.cummax`

    Apply function cummax to each row or column of a DataFrame.

  Examples

  For SeriesGroupBy:

      >>> lst = ["a", "a", "a", "b", "b", "b"]
      >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst)
      >>> ser
      a    1
      a    6
      a    2
      b    3
      b    1
      b    4
      dtype: int64
      >>> ser.groupby(level=0).cummax()
      a    1
      a    6
      a    6
      b    3
      b    3
      b    4
      dtype: int64

  For DataFrameGroupBy:

      >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]]
      >>> df = pd.DataFrame(
      ...     data, columns=["a", "b", "c"], index=["cow", "horse", "bull"]
      ... )
      >>> df
              a   b   c
      cow     1   8   2
      horse   1   1   0
      bull    2   6   9
      >>> df.groupby("a").groups
      {1: ['cow', 'horse'], 2: ['bull']}
      >>> df.groupby("a").cummax()
              b   c
      cow     8   2
      horse   8   2
      bull    6   9