假设我有一个像这样的框架

import pandas as pd

data = {
    'id1': [1]*12 + [1]*12 + [2]*12 + [2]*12,
    'id2': ['a']*12 + ['b']*12 + ['c']*12 + ['d']*12,
    'date': (['2023-11-20', '2023-11-21', '2023-11-22', '2023-11-23', '2023-11-24', '2023-11-25', '2023-11-26', '2023-11-27', '2023-11-28', '2023-11-29', '2023-11-30', '2023-12-01']*4)[:48],
    'event1': [10, 23, 36, 49, 62, 75, 88, 101, 114, 127, 140, 153, 12, 25, 38, 51, 64, 77, 90, 103, 116, 129, 142, 155, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 567, 785, 1003, 1221, 1439, 1657, 1875, 2093, 2311, 2529, 2747, 2965],
    'event2': ([0]*12 + [3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25] + [45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]*2)[:48]
}

df = pd.DataFrame(data)

print(df)

字符串
正如您所看到的，对于每个id2，event1和event2的值是累积的。也就是说，两个连续日期之间的值差是两个日期中较大日期的事件计数。
对于每个id2，计算过去一天（这里是2023-11-30）的事件数，过去三天的事件总数和过去7天的事件总数。
我试过以下类型的东西：

df.set_index('date', inplace=True)

target_date = pd.to_datetime('2023-11-30')

events_on_target_date = df.loc[target_date, 'event1'] - df.loc[target_date - pd.DateOffset(days=1), 'event1']
events_3_days_before = df.loc[target_date - pd.DateOffset(days=3):target_date, 'event1'].iloc[-1] - df.loc[target_date - pd.DateOffset(days=4), 'event1']
events_5_days_before = df.loc[target_date - pd.DateOffset(days=5):target_date, 'event1'].iloc[-1] - df.loc[target_date - pd.DateOffset(days=6), 'event1']

print(f"Number of events on {target_date}: {events_on_target_date}")
print(f"Total number of events in the 3 days before {target_date}: {events_3_days_before}")
print(f"Total number of events in the 5 days before {target_date}: {events_5_days_before}")

型
它可以很好地处理一个独立的id 1和id 2，但我很难处理：

• 将其应用于每个id2
• 应用此方法以包括event1和event2的计算

期望的结果应该是这样的

id1 id2 count_event1_yesterday  count_event1_past3Days  count_event1_past5Days  count_event2_yesterday  count_event2_past3Days  count_event2_past5Days
1   a   13  39  52  0   0   0
1   b   13  39  52  4   6   8
2   c                       
2   d

型