按日期選擇隨機時段-有解無憂

對于我的資料框中的每個 id，我想在給定 id 的最小和最大日期內的隨機日期范圍期間（可以是 1、2、4 周）之間對行進行采樣。例如，對于給定的 id，我有介于兩者之間的資料2022-09-01，2022-09-28我想隨機抽樣 7 天，這將為我提供隨機開始日期和結束日期之間的所有記錄，例如2022-09-06and 2022-09-12。

我的一個想法是隨機抽樣一個 start_date，然后在它上面添加我想要的間隔以找到 end_date，檢查它是否是一個可行的 end_date，如果不重復第一個 start_date 抽樣，如果可行，然后根據該范圍過濾資料，但是通過這種方法，我錯過了很多組合，并且覺得必須有更好的方法。

示例 df：

data = [{'account_id': 1, 'date': Timestamp('2022-09-01 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-02 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-03 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-04 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-05 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-06 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-07 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-08 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-09 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-10 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-11 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-12 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-13 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-14 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-15 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-16 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-17 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-18 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-19 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-20 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-21 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-22 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-23 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-24 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-25 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-26 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-27 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-28 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-01 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-02 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-03 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-04 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-05 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-06 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-07 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-08 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-09 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-10 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-11 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-12 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-13 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-14 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-15 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-16 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-17 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-18 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-19 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-20 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-21 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-22 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-23 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-24 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-25 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-26 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-27 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-28 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-29 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-30 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-10-01 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-10-02 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-10-03 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-10-04 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-10-05 00:00:00')}]

df = pd.DataFrame(data)

輸出示例：

output_data = [{'account_id': 1, 'date': Timestamp('2022-09-06 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-07 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-08 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-09 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-10 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-11 00:00:00')},
 {'account_id': 1, 'date': Timestamp('2022-09-12 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-15 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-16 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-17 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-18 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-19 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-20 00:00:00')},
 {'account_id': 2, 'date': Timestamp('2022-09-21 00:00:00')}]

output = pd.DataFrame(output_data)

uj5u.com熱心網友回復：

IIUC，這是您可以做到的一種方法：

基本上你 groupby account_id，然后創建一個pd.date_range組，從它的最小值開始，在它的最大值減去你選擇的天數（或其他任何東西。有關更多資訊，請閱讀檔案pandas.tseries.offsets.DateOffset）你選擇，因為你不能得到隨機 7如果您在組最大值前 2 天開始，則增加天數。 pd.date_range回傳一個串列，其中包含您可以開始的所有可能日期。從這個串列中，您選擇一個使用日期random.choice并將其傳遞給另一個pd.date_range作為開始日期，以及您選擇的間隔數。

import pandas as pd
import random

num_of_days = 7 # or whatever interval you choose
out = (
    df
    .groupby('account_id')
    .apply(lambda grp: 
           pd.date_range(
               start = random.choice(
                   [*pd.date_range(
                       grp['date'].min(), 
                       grp['date'].max() - pd.DateOffset(days=num_of_days)
                   )
                   ]
               ), 
               periods = num_of_days)
          )
    .explode()
    .reset_index(name='random_consecutive_days')
)
print(out)

    account_id random_consecutive_days
0            1              2022-09-13
1            1              2022-09-14
2            1              2022-09-15
3            1              2022-09-16
4            1              2022-09-17
5            1              2022-09-18
6            1              2022-09-19
7            2              2022-09-20
8            2              2022-09-21
9            2              2022-09-22
10           2              2022-09-23
11           2              2022-09-24
12           2              2022-09-25
13           2              2022-09-26

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