I have a dataset that is structured as a multiindex in pandas, with first index "Date" and second index "TagId". So I would access the data by using: df.loc['01/06/2022'].loc['0x24025F44AD21'][...] Here is a snippet of what that looks like: X Y Time Date TagId 01/06/2022 0x24025F44AD21 3.121672 5.188564 1900-01-01 08:37:17.000 0x24025F44AD21 3.121672 5.188564 1900-01-01 08:37:17.200 0x24025F44AD21 3.121672 5.188564 1900-01-01 08:37:17.400 0x24025F44AD21 3.121672 5.188564 1900-01-01 08:37:17.600 0x24025F44AD21 3.121672 5.188564 1900-01-01 08:37:17.800 ... ... ... 0x24025F44AD21 [......] [......] 1900-01-01 11:59:26.400 0x24046130B076 [......] [......] 1900-01-01 08:58:10:200 ... ... ... 0x24046130B076 7.611438 2.346377 1900-01-01 12:31:05.800 0x24046130B076 7.611438 2.346377 1900-01-01 12:31:06.000 0x24046130B076 7.533170 2.035381 1900-01-01 12:31:06.200 0x24046130B076 7.533170 2.035381 1900-01-01 12:31:06.400 I want an easy and efficient way to make sure all the first and last times (from the "Time" Columns) lign up. So, if you will, I want to find the latest first time and the earliest last time so that I can trim the other datasets accordingly. Additionally, which you can't see in the data above, there is a gap in the data. A break of sorts, where no measurements were taken. I want those "breaks" to align as well. So my goal is that the output of the above dataset looks something like this: X Y Time Date TagId 01/06/2022 0x24025F44AD21 [......] [......] 1900-01-01 08:58:10:200 ... ... ... 0x24025F44AD21 [......] [......] 1900-01-01 11:59:26.400 0x24046130B076 [......] [......] 1900-01-01 08:58:10:200 ... ... ... 0x24025F44AD21 [......] [......] 1900-01-01 11:59:26.400 (Here I used [......] because I don't know what these values are. It is mainly about the time column) It would be great if somebody could help me. Thanks in advance! Continue reading...
