我有一个像下面这样的pandas数据帧df1，其中左侧列是日期时间索引：
2016-08-25 19:00:00 144.784598 171.696834 187.392857
2016-08-25 20:30:00 144.837891 171.800840 187.531250
2016-08-25 22:00:00 144.930882 171.982199 187.806134
2016-08-25 23:30:00 144.921652 171.939453 187.757102
2016-08-26 01:00:00 144.954799 172.014280 187.845094
2016-08-26 02:30:00 144.900528 171.906090 187.754032
2016-08-26 04:00:00 144.881981 171.828125 187.702679
2016-08-26 05:30:00 144.870937 171.794847 187.655016
2016-08-26 07:00:00 144.840892 171.728800 187.600116
2016-08-26 08:30:00 144.910801 172.001769 188.052317
2016-08-26 10:00:00 145.191640 172.668826 188.868579
2016-08-26 11:30:00 144.477707 171.294408 187.202932
2016-08-26 13:00:00 144.235066 170.835810 186.617500
2016-08-26 14:30:00 144.091562 170.449642 186.164453
2016-08-26 16:00:00 144.017857 170.404412 186.194444
2016-08-28 19:00:00 144.089375 170.459677 186.256250
2016-08-28 20:30:00 144.154567 170.632161 186.528646
2016-08-28 22:00:00 144.177083 170.701823 186.600694
2016-08-28 23:30:00 144.139063 170.636058 186.502604
2016-08-29 01:00:00 144.188802 170.714167 186.653846
2016-08-29 02:30:00 144.266544 170.760066 186.746094
2016-08-29 04:00:00 144.254464 170.792105 186.744420
2016-08-29 05:30:00 144.194643 170.707666 186.626008
2016-08-29 07:00:00 144.168080 170.633899 186.525962
2016-08-29 08:30:00 144.444046 171.226805 187.512533
2016-08-29 10:00:00 144.529018 171.356548 187.731343
2016-08-29 11:30:00 144.578200 171.421900 187.792991
2016-08-29 13:00:00 144.816134 171.924337 188.470633
2016-08-29 14:30:00 144.791319 171.947195 188.438232
2016-08-29 16:00:00 144.884115 172.066621 188.685855
2016-08-29 19:00:00 144.749023 171.873288 188.473404
2016-08-29 20:30:00 144.638091 171.656599 188.183036
2016-08-29 22:00:00 144.663889 171.687962 188.205729
2016-08-29 23:30:00 144.656414 171.689635 188.230183
2016-08-30 01:00:00 144.613005 171.620593 188.083008
2016-08-30 02:30:00 144.532600 171.503879 187.901940
2016-08-30 04:00:00 144.600160 171.569375 187.965000
2016-08-30 05:30:00 144.568487 171.646406 188.067871
2016-08-30 07:00:00 144.785362 171.930504 188.460526
2016-08-30 08:30:00 144.807596 171.831662 188.422468
2016-08-30 10:00:00 144.803997 171.709052 188.194496
2016-08-30 11:30:00 144.709896 171.518804 187.849864
2016-08-30 13:00:00 144.709727 171.573187 187.875962
2016-08-30 14:30:00 144.789761 171.729604 187.790865
2016-08-30 16:00:00 144.821875 171.800000 187.943484
2016-08-30 19:00:00 144.800781 171.762097 187.895833
2016-08-30 20:30:00 144.647727 171.568841 187.679688
2016-08-30 22:00:00 144.654974 171.559630 187.628125
2016-08-30 23:30:00 144.705163 171.652344 187.763672
2016-08-31 01:00:00 144.701202 171.608456 187.714286
2016-08-31 02:30:00 144.677083 171.620052 187.716250
2016-08-31 04:00:00 144.705056 171.551630 187.596755
2016-08-31 05:30:00 144.674479 171.470170 187.554688
2016-08-31 07:00:00 144.667969 171.509430 187.604167
2016-08-31 08:30:00 144.773438 171.754527 187.749107
2016-08-31 10:00:00 144.864793 171.762162 187.853659
2016-08-31 11:30:00 144.820976 171.686443 187.735577
2016-08-31 13:00:00 144.889785 172.005833 188.272672
2016-08-31 14:30:00 144.715252 171.757528 188.100291
2016-08-31 16:00:00 144.637500 171.680804 188.173611
我想创建一个df2，其中只包含来自2016-08-31和2016-08-30的数据。有什么方法可以利用datetime属性来索引这2天而无需遍历所有小时和分钟？ df2的所需输出为：
2016-08-30 01:00:00 144.613005 171.620593 188.083008
2016-08-30 02:30:00 144.532600 171.503879 187.901940
2016-08-30 04:00:00 144.600160 171.569375 187.965000
2016-08-30 05:30:00 144.568487 171.646406 188.067871
2016-08-30 07:00:00 144.785362 171.930504 188.460526
2016-08-30 08:30:00 144.807596 171.831662 188.422468
2016-08-30 10:00:00 144.803997 171.709052 188.194496
2016-08-30 11:30:00 144.709896 171.518804 187.849864
2016-08-30 13:00:00 144.709727 171.573187 187.875962
2016-08-30 14:30:00 144.789761 171.729604 187.790865
2016-08-30 16:00:00 144.821875 171.800000 187.943484
2016-08-30 19:00:00 144.800781 171.762097 187.895833
2016-08-30 20:30:00 144.647727 171.568841 187.679688
2016-08-30 22:00:00 144.654974 171.559630 187.628125
2016-08-30 23:30:00 144.705163 171.652344 187.763672
2016-08-31 01:00:00 144.701202 171.608456 187.714286
2016-08-31 02:30:00 144.677083 171.620052 187.716250
2016-08-31 04:00:00 144.705056 171.551630 187.596755
2016-08-31 05:30:00 144.674479 171.470170 187.554688
2016-08-31 07:00:00 144.667969 171.509430 187.604167
2016-08-31 08:30:00 144.773438 171.754527 187.749107
2016-08-31 10:00:00 144.864793 171.762162 187.853659
2016-08-31 11:30:00 144.820976 171.686443 187.735577
2016-08-31 13:00:00 144.889785 172.005833 188.272672
2016-08-31 14:30:00 144.715252 171.757528 188.100291
2016-08-31 16:00:00 144.637500 171.680804 188.173611
答案 0 :(得分：1)
您可以使用字符串来分割日期时间索引：
df.loc['2016-08-30':'2016-08-31']
Out:
1 2 3
2016-08-30 01:00:00 144.613005 171.620593 188.083008
2016-08-30 02:30:00 144.532600 171.503879 187.901940
2016-08-30 04:00:00 144.600160 171.569375 187.965000
2016-08-30 05:30:00 144.568487 171.646406 188.067871
2016-08-30 07:00:00 144.785362 171.930504 188.460526
2016-08-30 08:30:00 144.807596 171.831662 188.422468
2016-08-30 10:00:00 144.803997 171.709052 188.194496
2016-08-30 11:30:00 144.709896 171.518804 187.849864
2016-08-30 13:00:00 144.709727 171.573187 187.875962
2016-08-30 14:30:00 144.789761 171.729604 187.790865
2016-08-30 16:00:00 144.821875 171.800000 187.943484
2016-08-30 19:00:00 144.800781 171.762097 187.895833
2016-08-30 20:30:00 144.647727 171.568841 187.679688
2016-08-30 22:00:00 144.654974 171.559630 187.628125
2016-08-30 23:30:00 144.705163 171.652344 187.763672
2016-08-31 01:00:00 144.701202 171.608456 187.714286
2016-08-31 02:30:00 144.677083 171.620052 187.716250
2016-08-31 04:00:00 144.705056 171.551630 187.596755
2016-08-31 05:30:00 144.674479 171.470170 187.554688
2016-08-31 07:00:00 144.667969 171.509430 187.604167
2016-08-31 08:30:00 144.773438 171.754527 187.749107
2016-08-31 10:00:00 144.864793 171.762162 187.853659
2016-08-31 11:30:00 144.820976 171.686443 187.735577
2016-08-31 13:00:00 144.889785 172.005833 188.272672
2016-08-31 14:30:00 144.715252 171.757528 188.100291
2016-08-31 16:00:00 144.637500 171.680804 188.173611