Question

假设我有这样的时间序列：

pd.Series(np.random.rand(20), index=pd.date_range("1990-01-01",periods=20))

1990-01-01    0.018363
1990-01-02    0.288625
1990-01-03    0.460708
1990-01-04    0.663063
1990-01-05    0.434250
1990-01-06    0.504893
1990-01-07    0.587743
1990-01-08    0.412223
1990-01-09    0.604656
1990-01-10    0.960338
1990-01-11    0.606765
1990-01-12    0.110480
1990-01-13    0.671683
1990-01-14    0.178488
1990-01-15    0.458074
1990-01-16    0.219303
1990-01-17    0.172665
1990-01-18    0.429534
1990-01-19    0.505891
1990-01-20    0.242567
Freq: D, dtype: float64

假设事件日期是1990-01-05和1990-01-15。我希望将数据子集化为事件周围的长度（-2，+ 2）窗口，但是添加了一列，产生了事件日期的相对天数（值为0）：

1990-01-01    0.460708  -2
1990-01-04    0.663063  -1
1990-01-05    0.434250  0
1990-01-06    0.504893  1
1990-01-07    0.587743  2
1990-01-13    0.671683  -2
1990-01-14    0.178488  -1
1990-01-15    0.458074   0
1990-01-16    0.219303   1
1990-01-17    0.172665   2
Freq: D, dtype: float64

此问题与我之前提出的问题有关：Event Study in Pandas

Answer 1

利用您之前在Pandas＆＃39;事件研究中的解决方案@jezrael：

import numpy as np
import pandas as pd

s  = pd.Series(np.random.rand(20), index=pd.date_range("1990-01-01",periods=20))

date1 = pd.to_datetime('1990-01-05')
date2 = pd.to_datetime('1990-01-15')
window = 2

dates = [date1, date2]

s1 = pd.concat([s.loc[date - pd.Timedelta(window, unit='d'): 
                      date + pd.Timedelta(window, unit='d')] for date in dates])

转换为dataframe：

df = s1.to_frame()

df['Offset'] = pd.Series(data=np.arange(-window,window+1).tolist()*len(dates),index=s1.index)

df

如何计算事件窗口的索引序列

1 个答案: