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snowflake.snowpark.modin.plugin.extensions.groupby_overrides.DataFrameGroupBy.cumsum¶

DataFrameGroupBy.cumsum(axis: Union[int, Literal['index', 'columns', 'rows']] = 0, *args, **kwargs)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/groupby_overrides.py#L518-L524)¶

Cumulative sum for each group.

Return type:: 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