e.groupby.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.SeriesGroupBy.max  #

SeriesGroupBy.  max  (  _ numeric_only  =  False  _ , _ min_count  =  -1  _ ,
_ engine  =  None  _ , _ engine_kwargs  =  None  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L3269-L3335) #

    

Compute max of group 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  ` .

**min_count** int, default -1

    

The required number of valid values to perform the operation. If fewer than `
min_count  ` non-NA values are present the result will be NA.

**engine** str, default None None

    

  * ` 'cython'  ` : Runs rolling apply through C-extensions from cython. 

  * ` 'numba'  ` Runs rolling apply through JIT compiled code from numba. 
    

Only available when ` raw  ` is set to ` True  ` .

  * ` None  ` : Defaults to ` 'cython'  ` or globally setting ` compute.use_numba  `

**engine_kwargs** dict, default None 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}  ` and will be
applied to both the ` func  ` and the ` apply  ` groupby aggregation.

Returns  :

    

Series or DataFrame

    

Computed max of values within each group.

Examples

For SeriesGroupBy:

    
    
    >>> lst = ['a', 'a', 'b', 'b']
    >>> ser = pd.Series([1, 2, 3, 4], index=lst)
    >>> ser
    a    1
    a    2
    b    3
    b    4
    dtype: int64
    >>> ser.groupby(level=0).max()
    a    2
    b    4
    dtype: int64
    

For DataFrameGroupBy:

    
    
    >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
    >>> df =