pyspark.pandas.groupby.GroupBy.cumprod#

GroupBy.cumprod()[source]#

Cumulative product for each group.

Returns

Series or DataFrame

See also

Series.cumprod
DataFrame.cumprod

Examples

>>> df = ps.DataFrame(
...     [[1, None, 4], [1, 0.1, 3], [1, 20.0, 2], [4, 10.0, 1]],
...     columns=list('ABC'))
>>> df
   A     B  C
0  1   NaN  4
1  1   0.1  3
2  1  20.0  2
3  4  10.0  1

By default, iterates over rows and finds the sum in each column.

>>> df.groupby("A").cumprod().sort_index()
      B   C
0   NaN   4
1   0.1  12
2   2.0  24
3  10.0   1

It works as below in Series.

>>> df.B.groupby(df.A).cumprod().sort_index()
0     NaN
1     0.1
2     2.0
3    10.0
Name: B, dtype: float64