pyspark.pandas.DataFrame.cummin#

DataFrame.cummin(skipna=True)#

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Note

the current implementation of cummin uses Spark’s Window without specifying partition specification. This leads to moveing all data into a single partition in a single machine and could cause serious performance degradation. Avoid this method with very large datasets.

Parameters
skipna: boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
DataFrame or Series

See also

DataFrame.min

Return the minimum over DataFrame axis.

DataFrame.cummax

Return cumulative maximum over DataFrame axis.

DataFrame.cummin

Return cumulative minimum over DataFrame axis.

DataFrame.cumsum

Return cumulative sum over DataFrame axis.

Series.min

Return the minimum over Series axis.

Series.cummax

Return cumulative maximum over Series axis.

Series.cummin

Return cumulative minimum over Series axis.

Series.cumsum

Return cumulative sum over Series axis.

Series.cumprod

Return cumulative product over Series axis.

Examples

>>> df = ps.DataFrame([[2.0, 1.0], [3.0, None], [1.0, 0.0]], columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

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

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

It works identically in Series.

>>> df.A.cummin()
0    2.0
1    2.0
2    1.0
Name: A, dtype: float64