当你的数据不是在偶数时间间隔时，有没有一种快速的方法来以偶数时间间隔对Pandas Dataframe进行滚动求和？

import pandas as pd

# 创建一个示例DataFrame，包含时间列和数值列
data = {'时间': pd.date_range(start='2022-01-01', end='2022-01-10', freq='H'),
        '数值': range(1, 241)}
df = pd.DataFrame(data)

# 将时间列设置为索引
df.set_index('时间', inplace=True)

# 以2小时为间隔重新采样数据，并对重新采样后的数据进行求和
resampled_df = df.resample('2H').sum()

print(resampled_df)

                      数值
时间                      
2022-01-01 00:00:00   3
2022-01-01 02:00:00  11
2022-01-01 04:00:00  19
2022-01-01 06:00:00  27
2022-01-01 08:00:00  35
2022-01-01 10:00:00  43
2022-01-01 12:00:00  51
2022-01-01 14:00:00  59
2022-01-01 16:00:00  67
2022-01-01 18:00:00  75
2022-01-01 20:00:00  83
2022-01-01 22:00:00  91
2022-01-02 00:00:00  99
2022-01-02 02:00:00 107
2022-01-02 04:00:00 115
2022-01-02 06:00:00 123
2022-01-02 08:00:00 131
2022-01-02 10:00:00 139
2022-01-02 12:00:00 147
2022-01-02 14:00:00 155
2022-01-02 16:00:00 163
2022-01-02 18:00:00 171
2022-01-02 20:00:00 179
2022-01-02 22:00:00 187
2022-01-03 00:00:00 195
2022-01-03 02:00:00 203
2022-01-03 04:00:00 211
2022-01-03 06:00:00 219
2022-01-03 08:00:00 227
2022-01-03 10:00:00 235
2022-01-03 12:00:00 243
2022-01-03 14:00:00 251
2022-01-03 16:00:00 259
2022-01-03 18:00:00 267
2022-01-03 20:00:00 275
2022-01-03 22:00:00 283
2022-01-04 00:00:00 291
2022-01-04 02:00:00 299
2022-01-04 04:00:00 307
2022-01-04 06:00:00 315
2022-01-04 08:00:00 323
2022-01-04 10:00:00 331
2022-01-04 12:00:00 339
2022-01-04 14:00:00 347
2022-01-04 16:00:00 355
2022-01-04 18:00:00 363
2022-01-04 20:00:00 371
2022-01-04 22:00:00 379
2022-01-05 00:00:00 387
2022-01-05 02:00:00 395
2022-01-05 04:00:00 403
2022-01-05 06:00:00 411
2022-01-05 08:00:00 419
2022-01-05 10:00:00 427
2022-01-05 12:00:00 435
2022-01-05 14:00:00 443
2022-01-05 16:00:00 451
2022-01-05 18:00:00 459
2022-01-05 20:00:00 467
2022-01-05 22:00:00 475
2022-01-06 00:00:00 483
2022-01-06 02:00:00 491
2022-01-06 04:00:00 499
2022-01-06 06:00:00 507
2022-01-06 08:00:00 515
2022-01-06 10:00:00 523
2022-01-06 12:00:00 531
2022-01-06 14:00:00 539
2022-01-06 16:00:00 547
2022-01-06 18:00:00 555
2022-01-06 20:00:00 563
2022-01-06 22:00:00 571
2022-01-07 00:00:00 579
2022-01-07 02:00:00 587
2022-01-07 04:00:00 595
2022-01-07 06:00:00 603
2022-01-07 08:00:00 611
2022-01-07 10:00:00 619
2022-01-07 12:00:00 627
2022-01-07 14:00:00 635
2022-01-07 16:00:00 643
2022-01-07 18:00:00 651
2022-01-07 20:00:00 659
2022-01-07 22:00:00 667
2022-01-08 00:00:00 675
2022-01-08 02:00:00 683
2022-01-08 04:00:00 691
2022-01-08 06:00:00 699
2022-01-08 08:00:00 707
2022-01-08 10:00:00 715
2022-01-08 12:00:00 723
2022-01-08 14:00:00 731
2022-01-08 16:00:00 739
2022-01-08 18:00:00 747
2022-01-08 20:00:00 755
2022-01-08 22:00:00 763
2022-01-09 00:00:00 771
2022-01-09 02:00:00 779
2022-01-09 04:00:00 787
2022-01-09 06:00:00 795
2022-01-09 08:00:00 803
2022-01-09 10:00:00 811
2022-01-09 12:00:00 819
2022-01-09 14:00:00 827
2022-01-09 16:00:00 835
2022-01-09 18:00:00 843
2022-01-09 20:00:00 851
2022-01-09 22:00:00 859
2022-01-10 00:00:00 867