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