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/v1.30.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/groupby_overrides.py#L690-L705)¶
- 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 setting- compute.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 accepted- engine_kwargs
- For 'numba'engine, the engine can acceptnopython,nogil
- and - paralleldictionary keys. The values must either be- Trueor- False. The default- engine_kwargsfor 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