Skip to main content
`Ctrl`+`K`

# pandas.api.typing.DataFrameGroupBy.cummin

DataFrameGroupBy.cummin(numeric_only=False, **kwargs)

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

  See also

  `Series.cummin`

    Apply function cummin to a Series.

  `DataFrame.cummin`

    Apply function cummin 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, 0, 4], index=lst)
      >>> ser
      a    1
      a    6
      a    2
      b    3
      b    0
      b    4
      dtype: int64
      >>> ser.groupby(level=0).cummin()
      a    1
      a    1
      a    1
      b    3
      b    0
      b    0
      dtype: int64

  For DataFrameGroupBy:

      >>> data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]]
      >>> df = pd.DataFrame(
      ...     data, columns=["a", "b", "c"], index=["snake", "rabbit", "turtle"]
      ... )
      >>> df
              a   b   c
      snake   1   0   2
      rabbit  1   1   5
      turtle  6   6   9
      >>> df.groupby("a").groups
      {1: ['snake', 'rabbit'], 6: ['turtle']}
      >>> df.groupby("a").cummin()
              b   c
      snake   0   2
      rabbit  0   2
      turtle  6   9