pandas.core.groupby.DataFrameGroupBy.mean#

DataFrameGroupBy.mean(numeric_only=False, 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 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.

Returns:
pandas.Series or pandas.DataFrame

See also

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply 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