I have huge data of time series and I am facing problem in changing the time conventions.
Below are different types and I am trying to make them all to one format. Not able to find any guidance accordingly. It is more like a data pre processing/ cleaning process that I am trying to do. So that the next execution process with python and pandas goes smooth. Changing manually is practically impossible need a fix with python script.
The input files are of two types in CSV format.
A three column and multiple rows where col[0] is date-time definitely and rest are other data. Column header is not constant every input file is given some name so cannot use headers.
09/30/2015 12:00 PM,abcsd,434235
09/30/2015 12:30 PM,taer,45824
09/30/2015 13:00 PM,hshfe,4894
The input file with multiple columns and multiple rows
no.,30-09-2015 12:00 PM,30-09-2015 13:00 PM
1111,2345,2342
Types
1. 09/30/2015 12:00:00
2. 30/09/2015 12:00
3. 09/30/2015 12:00 PM
4. 30/09/2015 12:00 PM
5. 30-09-2015 12:00:00
6. 30-09-2015 12:00 PM
The above listed are the types and I want to bring them all to one format as:
1. 30/09/2015 12:00
or
2. 09/30/2015 12:00
I could not find proper guidance in document too. So could not try out any code so far.
Thanks for the valuable suggestions
Types?dd-mmvsmm-ddwill be ambiguous if the day is less than 13. How do you expect to handle that?