pandas.api.typing.SeriesGroupBy.mean#

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

Compute mean of groups, excluding missing values.

Returns the arithmetic mean for each group. Missing values are ignored unless the entire group is NA, in which case the result for that group is NA.

Parameters:
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.

skipnabool, default True

Exclude NA/null values. If an entire group is NA, the result will be NA.

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

engine_kwargsdict, default None

  • For 'cython' engine, there are no accepted engine_kwargs

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

Returns:
pandas.Series or pandas.DataFrame

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

See also

Series.mean

Apply function mean to a Series.

DataFrame.mean

Apply function mean 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
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