pyspark.pandas.Series.cummax¶

Series.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

pyspark.pandas.Series.count

pyspark.pandas.Series.cummin