pandas.core.groupby.SeriesGroupBy.max — pandas 2.2.3 documentation (original) (raw)

SeriesGroupBy.max(numeric_only=False, min_count=-1, engine=None, engine_kwargs=None)[source]#

Compute max of group values.

Parameters:

numeric_onlybool, default False

Include only float, int, boolean columns.

Changed in version 2.0.0: numeric_only no longer accepts None.

min_countint, default -1

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.

enginestr, default None None

• '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_kwargsdict, default None None

• 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 orFalse. 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