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| From | Neil Cerutti <neilc@norwich.edu> |
| Newsgroups | comp.lang.python |
| Subject | Re: analyzing time |
| Date | 5 Jul 2013 19:35:33 GMT |
| Organization | Norwich University |
| Lines | 31 |
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On 2013-07-05, noydb <jenn.duerr@gmail.com> wrote: > Hello All, > > I have a table with a column of type date, with dates and time > combined (like '1/6/2013 3:52:69PM'), that spans many months. > How would I pull out records that are the first and last > entries per day? > > Also, if I wanted to find time clusters per day (or per week) > -- like if an entry is made every day around 11am -- is there a > way to get at that temporal statistical cluster? > > Python 2.7, Windows 7. > > Any guidance would be greatly appreciated! Time seems tricky... Time *is* really tricky, but that's because we humans created a tricky system. If can ignore issues of timespampts, timezones and daylight savings time, then time handling in Python can be simple. datetime.datetime.strptime can translate the time format above into datetime.datetime objects, which provide all the methods you will need. To find clusters and min and max values you will likely need to put the datetime objects in a list, and use some Python builtins and list methods. -- Neil Cerutti
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analyzing time noydb <jenn.duerr@gmail.com> - 2013-07-05 12:18 -0700 Re: analyzing time Neil Cerutti <neilc@norwich.edu> - 2013-07-05 19:35 +0000 Re: analyzing time Skip Montanaro <skip@pobox.com> - 2013-07-05 14:43 -0500 Re: analyzing time Gary Herron <gherron@digipen.edu> - 2013-07-05 12:47 -0700 Re: analyzing time Terry Reedy <tjreedy@udel.edu> - 2013-07-05 15:54 -0400
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