pyspark.pandas.groupby.GroupBy.max#

GroupBy.max(numeric_only=False, min_count=- 1)[source]#

Compute max of group values.

New in version 3.3.0.

Parameters

numeric_onlybool, default False: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.

New in version 3.4.0.
min_countbool, 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.

New in version 3.4.0.

See also

Examples

>>> df = ps.DataFrame({"A": [1, 2, 1, 2], "B": [True, False, False, True],
...                    "C": [3, 4, 3, 4], "D": ["a", "a", "b", "a"]})

>>> df.groupby("A").max().sort_index()
      B  C  D
A
1  True  3  b
2  True  4  a

Include only float, int, boolean columns when set numeric_only True.

>>> df.groupby("A").max(numeric_only=True).sort_index()
      B  C
A
1  True  3
2  True  4

>>> df.groupby("D").max().sort_index()
   A      B  C
D
a  2   True  4
b  1  False  3

>>> df.groupby("D").max(min_count=3).sort_index()
     A     B    C
D
a  2.0  True  4.0
b  NaN  None  NaN