pyspark.pandas.groupby.GroupBy.cummin#

GroupBy.cummin()[source]#

Cumulative min for each group.

Returns

Series or DataFrame

See also

Series.cummin
DataFrame.cummin

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").cummin().sort_index()
      B  C
0   NaN  4
1   0.1  3
2   0.1  2
3  10.0  1

It works as below in Series.

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