# dask.dataframe.groupby.DataFrameGroupBy.max

DataFrameGroupBy.max(split_every=None, split_out=1, shuffle_method=None, numeric_only=<no_default>)

  Compute max of group values.

  This docstring was copied from pandas.core.groupby.groupby.GroupBy.max.

  Some inconsistencies with the Dask version may exist.

  Parameters:
    numeric_only bool, default False

      Include only float, int, boolean columns.

      Changed in version 2.0.0: numeric_only no longer accepts `None`.

    min_count int, default -1 (Not supported in Dask)

      The required number of valid values to perform the operation. If fewer than `min_count` non-NA values are present the result will be NA.

    engine str, default None None (Not supported in Dask)

      - `'cython'` : Runs rolling apply through C-extensions from cython.

      - `'numba'`Runs rolling apply through JIT compiled code from numba.

          Only available when `raw` is set to `True`.

      - `None` : Defaults to `'cython'` or globally setting `compute.use_numba`

    engine_kwargs dict, default None None (Not supported in Dask)

      - For `'cython'` engine, there are no accepted `engine_kwargs`

      - For `'numba'` engine, the engine can accept `nopython`, `nogil`

          and `parallel` dictionary keys. The values must either be `True` or `False`. The default `engine_kwargs` for the `'numba'` engine is `{'nopython': True, 'nogil': False, 'parallel': False}` and will be applied to both the `func` and the `apply` groupby aggregation.

  Returns:
    Series or DataFrame

      Computed max of values within each group.

  Examples

  For SeriesGroupBy:

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

  For DataFrameGroupBy:

      >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
      >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
      ...                   index=["tiger", "leopard", "cheetah", "lion"])
      >>> df
                a  b  c
        tiger   1  8  2
      leopard   1  2  5
      cheetah   2  5  8
         lion   2  6  9
      >>> df.groupby("a").max()
          b  c
      a
      1   8  5
      2   6  9