SeriesGroupBy.mean(numeric_only=False, engine=None, engine_kwargs=None)[source]#

Compute mean of groups, excluding missing values.

numeric_onlybool, default False

Include only float, int, boolean columns.

Changed in version 2.0.0: numeric_only no longer accepts None and defaults to False.

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 setting compute.use_numba

Added in version 1.4.0.

engine_kwargsdict, default None
  • For 'cython' engine, there are no accepted engine_kwargs

  • For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}}

Added in version 1.4.0.

pandas.Series or pandas.DataFrame

Mean of values within each group. Same object type as the caller.

See also


Apply a function groupby to a Series.


Apply a function groupby to each row or column of a DataFrame.


>>> 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
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()
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()
1    3.0
2    4.0
Name: B, dtype: float64