pyspark.pandas.window.Rolling.max

Rolling.max() → FrameLike

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

Series.rolling

Series rolling.

DataFrame.rolling

DataFrame rolling.

Series.max

Similar method for Series.

DataFrame.max

Similar 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

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