mean(self, numeric_only: 'bool | lib.NoDefault' = <no_default>, engine: 'str' = 'cython', engine_kwargs: 'dict[str, bool] | None' = None)
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
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}}
Compute mean of groups, excluding missing values.
DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame.
Series.groupby
Apply a function groupby to a Series.
>>> 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: float64See :
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