snowflake.snowpark.modin.plugin.extensions.groupby_overrides.SeriesGroupBy.mean¶
- SeriesGroupBy.mean(numeric_only: bool = False, engine: Optional[Literal['cython', 'numba']] = None, engine_kwargs: Optional[dict[str, bool]] = None)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/groupby_overrides.py#L686-L701)¶
Compute mean of groups, excluding missing values.
- Parameters:
numeric_only (bool, default False) – Include only float, int, boolean columns.
engine (str, default None) –
'cython'
: Runs the operation through C-extensions from cython.'numba'
: Runs the operation through JIT compiled code from numba.None
: Defaults to'cython'
or globally settingcompute.use_numba
This parameter is ignored in Snowpark pandas, as the execution is always performed in Snowflake.
engine_kwargs (dict, default None) –
For
'cython'
engine, there are no acceptedengine_kwargs
- For
'numba'
engine, the engine can acceptnopython
,nogil
and
parallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_kwargs
for the'numba'
engine is{{'nopython': True, 'nogil': False, 'parallel': False}}
- For
This parameter is ignored in Snowpark pandas, as the execution is always performed in Snowflake.
- Return type:
Examples
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
Groupby one column and return the mean of the remaining columns in each group.
>>> df.groupby('A').mean() B C A 1 3.0 1.333333 2 4.0 1.500000
Groupby two columns and return the mean of the remaining column.
>>> df.groupby(['A', 'B']).mean() C A B 1 2.0 2.0 4.0 1.0 2 3.0 1.0 5.0 2.0
Groupby one column and return the mean of only one particular column in the group.
>>> df.groupby('A')['B'].mean() A 1 3.0 2 4.0 Name: B, dtype: float64