pandas 1.4.2

ParametersReturns
mean(self, numeric_only: 'bool | lib.NoDefault' = <no_default>, engine: 'str' = 'cython', engine_kwargs: 'dict[str, bool] | None' = None)

Parameters

numeric_only : bool, default True

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.

engine : str, default None
engine_kwargs : dict, 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}}

versionadded

Returns

pandas.Series or pandas.DataFrame

Compute mean of groups, excluding missing values.

See Also

DataFrame.groupby

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

Series.groupby

Apply a function groupby to a Series.

Examples

This example is valid syntax, but we were not able to check execution
>>> 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.

This example is valid syntax, but we were not able to check execution
>>> 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.

This example is valid syntax, but we were not able to check execution
>>> 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.

This example is valid syntax, but we were not able to check execution
>>> df.groupby('A')['B'].mean()
A
1    3.0
2    4.0
Name: B, dtype: float64
See :

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File: /pandas/core/groupby/groupby.py#1876
type: <class 'function'>
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