pyspark.pandas.window.Rolling.max¶
-
Rolling.max() → FrameLike[source]¶ Calculate the rolling maximum.
Note
the current implementation of this API 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.
- Returns
- Series or DataFrame
Return type is determined by the caller.
See also
pyspark.pandas.Series.rollingSeries rolling.
pyspark.pandas.DataFrame.rollingDataFrame rolling.
pyspark.pandas.Series.maxSimilar method for Series.
pyspark.pandas.DataFrame.maxSimilar method for DataFrame.
Examples
>>> s = ps.Series([4, 3, 5, 2, 6]) >>> s 0 4 1 3 2 5 3 2 4 6 dtype: int64
>>> s.rolling(2).max() 0 NaN 1 4.0 2 5.0 3 5.0 4 6.0 dtype: float64
>>> s.rolling(3).max() 0 NaN 1 NaN 2 5.0 3 5.0 4 6.0 dtype: float64
For DataFrame, each rolling maximum is computed column-wise.
>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2}) >>> df A B 0 4 16 1 3 9 2 5 25 3 2 4 4 6 36
>>> df.rolling(2).max() A B 0 NaN NaN 1 4.0 16.0 2 5.0 25.0 3 5.0 25.0 4 6.0 36.0
>>> df.rolling(3).max() A B 0 NaN NaN 1 NaN NaN 2 5.0 25.0 3 5.0 25.0 4 6.0 36.0