Indizierung Datenrahmen von Datetime-Python ignoriert Stunde, Minuten, Sekunden

Question

ich Pandas Datenrahmen df1 wie die folgenden haben, wo die linke Spalte in Datetime-Index:

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 

ich eine df2 erstellen wollen, die nur enthält Daten von 2016-08-31 und 2016-08-30. Was ist eine Möglichkeit, die Eigenschaft datetime zu verwenden, um diese 2 Tage zu indizieren, ohne alle Stunden und Minuten durchlaufen zu müssen? Die gewünschte Ausgabe für df2 ist:

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 

Answer 1

können Sie Strings verwenden, um einen Datetime-Index zu schneiden:

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