pyspark.pandas.groupby.GroupBy.mean

GroupBy.mean(numeric_only: Optional[bool] = True) → FrameLike

Compute mean of groups, excluding missing values.

Parameters
numeric_onlybool, default False

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

Returns
pyspark.pandas.Series or pyspark.pandas.DataFrame

Examples

>>> df = ps.DataFrame({'A': [1, 1, 2, 1, 2],
...                    'B': [np.nan, 2, 3, 4, 5],
...                    'C': [1, 2, 1, 1, 2],
...                    'D': [True, False, True, False, True]})

Groupby one column and return the mean of the remaining columns in each group.

>>> df.groupby('A').mean().sort_index()  
     B         C         D
A
1  3.0  1.333333  0.333333
2  4.0  1.500000  1.000000