pandas.core.groupby.DataFrameGroupBy.mean#
- DataFrameGroupBy.mean(numeric_only=False, skipna=True, engine=None, engine_kwargs=None)[source]#
Compute mean of groups, excluding missing values.
- Parameters:
- numeric_onlybool, default False
Include only float, int, boolean columns.
Changed in version 2.0.0: numeric_only no longer accepts
Noneand defaults toFalse.- skipnabool, default True
Exclude NA/null values. If an entire group is NA, the result will be NA.
Added in version 3.0.0.
- enginestr, 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
Added in version 1.4.0.
- engine_kwargsdict, default None
For
'cython'engine, there are no acceptedengine_kwargsFor
'numba'engine, the engine can acceptnopython,nogilandparalleldictionary keys. The values must either beTrueorFalse. The defaultengine_kwargsfor the'numba'engine is{{'nopython': True, 'nogil': False, 'parallel': False}}
Added in version 1.4.0.
- Returns:
- pandas.Series or pandas.DataFrame
Mean of values within each group. Same object type as the caller.
See also
Series.groupbyApply a function groupby to a Series.
DataFrame.groupbyApply a function groupby to each row or column of a DataFrame.
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 particular column in the group.
>>> df.groupby("A")["B"].mean() A 1 3.0 2 4.0 Name: B, dtype: float64