oupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.mean  #

DataFrameGroupBy.  mean  (  _ numeric_only  =  False  _ , _ engine  =  None  _
, _ engine_kwargs  =  None  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L2365-L2457) #

    

Compute mean of groups, excluding missing values.

Parameters  :

    

**numeric_only** bool, default False

    

Include only float, int, boolean columns.

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

**engine** str, 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  `

New in version 1.4.0.

**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}}  `

New 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([