cudf.Series.rolling#
- Series.rolling(window, min_periods=None, center=False, axis=0, win_type=None)#
Rolling window calculations.
- Parameters:
- windowint, offset or a BaseIndexer subclass
Size of the window, i.e., the number of observations used to calculate the statistic. For datetime indexes, an offset can be provided instead of an int. The offset must be convertible to a timedelta. As opposed to a fixed window size, each window will be sized to accommodate observations within the time period specified by the offset. If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined
get_window_bounds
method.- min_periodsint, optional
The minimum number of observations in the window that are required to be non-null, so that the result is non-null. If not provided or
None
,min_periods
is equal to the window size.- centerbool, optional
If
True
, the result is set at the center of the window. IfFalse
(default), the result is set at the right edge of the window.
- Returns:
Rolling
object.
Examples
>>> import cudf >>> a = cudf.Series([1, 2, 3, None, 4])
Rolling sum with window size 2.
>>> print(a.rolling(2).sum()) 0 1 3 2 5 3 4 dtype: int64
Rolling sum with window size 2 and min_periods 1.
>>> print(a.rolling(2, min_periods=1).sum()) 0 1 1 3 2 5 3 3 4 4 dtype: int64
Rolling count with window size 3.
>>> print(a.rolling(3).count()) 0 1 1 2 2 3 3 2 4 2 dtype: int64
Rolling count with window size 3, but with the result set at the center of the window.
>>> print(a.rolling(3, center=True).count()) 0 2 1 3 2 2 3 2 4 1 dtype: int64
Rolling max with variable window size specified by an offset; only valid for datetime index.
>>> a = cudf.Series( ... [1, 9, 5, 4, np.nan, 1], ... index=[ ... pd.Timestamp('20190101 09:00:00'), ... pd.Timestamp('20190101 09:00:01'), ... pd.Timestamp('20190101 09:00:02'), ... pd.Timestamp('20190101 09:00:04'), ... pd.Timestamp('20190101 09:00:07'), ... pd.Timestamp('20190101 09:00:08') ... ] ... )
>>> print(a.rolling('2s').max()) 2019-01-01T09:00:00.000 1 2019-01-01T09:00:01.000 9 2019-01-01T09:00:02.000 9 2019-01-01T09:00:04.000 4 2019-01-01T09:00:07.000 2019-01-01T09:00:08.000 1 dtype: int64
Apply custom function on the window with the apply method
>>> import numpy as np >>> import math >>> b = cudf.Series([16, 25, 36, 49, 64, 81], dtype=np.float64) >>> def some_func(A): ... b = 0 ... for a in A: ... b = b + math.sqrt(a) ... return b ... >>> print(b.rolling(3, min_periods=1).apply(some_func)) 0 4.0 1 9.0 2 15.0 3 18.0 4 21.0 5 24.0 dtype: float64
And this also works for window rolling set by an offset
>>> import pandas as pd >>> c = cudf.Series( ... [16, 25, 36, 49, 64, 81], ... index=[ ... pd.Timestamp('20190101 09:00:00'), ... pd.Timestamp('20190101 09:00:01'), ... pd.Timestamp('20190101 09:00:02'), ... pd.Timestamp('20190101 09:00:04'), ... pd.Timestamp('20190101 09:00:07'), ... pd.Timestamp('20190101 09:00:08') ... ], ... dtype=np.float64 ... ) >>> print(c.rolling('2s').apply(some_func)) 2019-01-01T09:00:00.000 4.0 2019-01-01T09:00:01.000 9.0 2019-01-01T09:00:02.000 11.0 2019-01-01T09:00:04.000 7.0 2019-01-01T09:00:07.000 8.0 2019-01-01T09:00:08.000 17.0 dtype: float64