roupby.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)
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    * [ 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)
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  * [ 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.cumsum  #

SeriesGroupBy.  cumsum  (  _ axis  =  _NoDefault.no_default  _ , _ *  args  _
, _ **  kwargs  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L4877-L4936) #

    

Cumulative sum 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', 'b']
    >>> ser = pd.Series([6, 2, 0], index=lst)
    >>> ser
    a    6
    a    2
    b    0
    dtype: int64
    >>> ser.groupby(level=0).cumsum()
    a    6
    a    8
    b    0
    dtype: int64
    

For DataFrameGroupBy:

    
    
    >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
    >>> df = pd.DataFrame(data, columns=["a", "b", "c"],
    ...                   index=["fox", "gorilla", "lion"])
    >>> df
              a   b   c
    fox       1   8   2
    gorilla   1   2   5
    lion      2   6   9
    >>> df.groupby("a").groups
    {1: ['fox', 'gorilla'], 2: ['lion']}
    >>> df.groupby("a").cumsum()
              b   c
    fox       8   2
    gorilla  10   7
    lion      6   9
    

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  * ` SeriesGroupBy.cumsum()  `

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](../../_sources/reference/api/pandas.core.groupby.SeriesGroupBy.cumsum.rst.txt)

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