问题
I am looking for a special window function in pandas: sort of a combination of rolling and expanding. For calculating (for instance) the mean and standard deviating, I want to regard all past data, but ignore the first few records to make sure I have a multiple of 7 (days in my case). That's because I know the data has a strong weekly pattern.
Example:
s = pd.Series([1, 3, 4, 5, 4, 3, 1, 2, 4, 5, 4, 5, 4, 2, 1, 3, 4, 5, 4, 3, 1, 3],
pd.date_range('2020-01-01', '2020-01-22'))
s.rolling(7, 7).mean() # Use last 7 days.
s.expanding(7).mean() # Use all past days.
s.mywindowing(7).mean() # Use last past multiple of 7 days. How?
The effect should be like this:
Of course I can do things manually using for
loops and such, but I imagine the existing pandas machinery can be used to do this...?
回答1:
Pandas custom window rolling
another usage here
import pandas as pd
import numpy as np
from pandas.api.indexers import BaseIndexer
from typing import Optional, Tuple
class CustomIndexer(BaseIndexer):
def get_window_bounds(self,
num_values: int = 0,
min_periods: Optional[int] = None,
center: Optional[bool] = None,
closed: Optional[str] = None
) -> Tuple[np.ndarray, np.ndarray]:
end = np.arange(1, num_values+1, dtype=np.int64)
start = end % 7
return start, end
indexer = CustomIndexer(num_values=len(s))
s.rolling(indexer).mean().round(2)
Outputs:
2020-01-01 NaN
2020-01-02 NaN
2020-01-03 NaN
2020-01-04 NaN
2020-01-05 NaN
2020-01-06 NaN
2020-01-07 3.00
2020-01-08 3.14
2020-01-09 3.29
2020-01-10 3.43
2020-01-11 3.29
2020-01-12 3.43
2020-01-13 3.57
2020-01-14 3.36
2020-01-15 3.36
2020-01-16 3.36
2020-01-17 3.36
2020-01-18 3.36
2020-01-19 3.36
2020-01-20 3.36
2020-01-21 3.24
2020-01-22 3.33
Freq: D, dtype: float64
来源:https://stackoverflow.com/questions/64967769/window-of-full-weeks-in-pandas