pyspark.pandas.DataFrame.cummax

DataFrame.cummax(skipna: bool = True) → FrameLike

Return cumulative maximum over a DataFrame or Series axis.

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

Note

the current implementation of cummax uses Spark’s Window without specifying partition specification. This leads to move all data into single partition in single machine and could cause serious performance degradation. Avoid this method against very large dataset.

Parameters
skipnaboolean, 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.max

Return the maximum 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.

DataFrame.cumprod

Return cumulative product over DataFrame axis.

Series.max

Return the maximum 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 maximum in each column.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

It works identically in Series.

>>> df.B.cummax()
0    1.0
1    NaN
2    1.0
Name: B, dtype: float64