pyspark.pandas.Series.max

Series.max(axis: Union[int, str, None] = None, numeric_only: bool = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, pyspark.pandas.series.Series]

Return the maximum of the values.

Parameters

axis{index (0), columns (1)}

Axis for the function to be applied on.

numeric_onlybool, default None

If True, include only float, int, boolean columns. This parameter is mainly for pandas compatibility. False is supported; however, the columns should be all numeric or all non-numeric.

Returns

maxscalar for a Series, and a Series for a DataFrame.

Examples

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.max()
a    3.0
b    0.3
dtype: float64

>>> df.max(axis=1)
0    1.0
1    2.0
2    3.0
3    NaN
dtype: float64

On a Series:

>>> df['a'].max()
3.0

pyspark.pandas.Series.mad pyspark.pandas.Series.mean