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    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
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    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
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    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
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  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.SeriesGroupBy.cummax  #

SeriesGroupBy.  cummax  (  _ axis  =  _NoDefault.no_default  _ , _
numeric_only  =  False  _ , _ **  kwargs  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L5013-L5086) #

    

Cumulative max for each group.

Returns  :

    

Series or 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

For SeriesGroupBy:

    
    
    >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
    >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst)
    >>> ser
    a    1
    a    6
    a    2
    b    3
    b    1
    b    4
    dtype: int64
    >>> ser.groupby(level=0).cummax()
    a    1
    a    6
    a    6
    b    3
    b    3
    b    4
    dtype: int64
    

For DataFrameGroupBy:

    
    
    >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]]
    >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
    ...                   index=["cow", "horse", "bull"])
    >>> df
            a   b   c
    cow     1   8   2
    horse   1   1   0
    bull    2   6   9
    >>> df.groupby("a").groups
    {1: ['cow', 'horse'], 2: ['bull']}
    >>> df.groupby("a").cummax()
            b   c
    cow     8   2
    horse   8   2
    bull    6   9
    

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  * ` SeriesGroupBy.cummax()  `

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