熊猫 - 每月重新采样每月序列到小时

Question

假设我有一个具有两个索引级别的多指数pandas数据框架：month_begin and month_end，

import pandas as pd

multi_index = pd.MultiIndex.from_tuples([("2022-03-01", "2022-03-31"), 
                                  ("2022-04-01", "2022-04-30"), 
                                  ("2022-05-01", "2022-05-31"),
                                  ("2022-06-01", "2022-06-30")])

multi_index.names = ['month_begin', 'month_end']

df = pd.DataFrame(np.random.rand(4,100), index=multi_index)
df
                              0         1   ...        98        99
month_begin month_end                       ...                    
2022-03-01  2022-03-31  0.322032  0.205307  ...  0.975128  0.673460
2022-04-01  2022-04-30  0.113813  0.278981  ...  0.951049  0.090765
2022-05-01  2022-05-31  0.777918  0.842734  ...  0.667831  0.274189
2022-06-01  2022-06-30  0.221407  0.555711  ...  0.745158  0.648246

我想将数据重新采样以在相应月份的每个小时内一个月内的一个月内具有值：

                              0         1   ...        98        99
                                            ...                    
2022-03-01 00:00       0.322032  0.205307  ...  0.975128  0.673460
2022-03-01 01:00       0.322032  0.205307  ...  0.975128  0.673460
2022-03-01 02:00       0.322032  0.205307  ...  0.975128  0.673460
...
2022-06-30 22:00       0.221407  0.555711  ...  0.745158  0.648246
2022-06-30 23:00       0.221407  0.555711  ...  0.745158  0.648246 

我知道我可以使用resample（ ），但我在为此而苦苦挣扎。有人有线索吗？

Answer 1

iiuc，使用list_compherension尝试使用 pd.date_range ：

df['Date'] = [pd.date_range(s, e, freq='H') for s, e in df.index]

df_out = df.explode('Date').set_index('Date')

输出：输出：

                           0         1   ...        98        99
Date                                     ...                    
2022-03-01 00:00:00  0.396311  0.138263  ...  0.637640  0.106366
2022-03-01 01:00:00  0.396311  0.138263  ...  0.637640  0.106366
2022-03-01 02:00:00  0.396311  0.138263  ...  0.637640  0.106366
2022-03-01 03:00:00  0.396311  0.138263  ...  0.637640  0.106366
2022-03-01 04:00:00  0.396311  0.138263  ...  0.637640  0.106366
...                       ...       ...  ...       ...       ...
2022-06-29 20:00:00  0.129921  0.654878  ...  0.619212  0.142297
2022-06-29 21:00:00  0.129921  0.654878  ...  0.619212  0.142297
2022-06-29 22:00:00  0.129921  0.654878  ...  0.619212  0.142297
2022-06-29 23:00:00  0.129921  0.654878  ...  0.619212  0.142297
2022-06-30 00:00:00  0.129921  0.654878  ...  0.619212  0.142297

[2836 rows x 100 columns]