Hello,
I have a file containing mixed data types: strings, floats, datetime
output(i.e. strings), and ints. Something like:
#ID, name, date, value
1,sample,2008-07-10 12:34:20,344.56
And so forth. It seems using recarrays is efficient and a prefered habit to
get into wrg to numpy, so I am trying the following:
dtype=[('ID','int'),('name','|S10'),('date','????'),('value','float')]
DATA = np.loadtxt(infile,dtype=dtype)
This works if I set 'date' type to 'S20', but wouldn't it be more
appropriate to use object? Also, what is the next step to then use the
values as datetime objects?
Do I have to take the data out of the array, convert to datetime, then put
back in the array?
dates = DATA['date']
dates = [dt.datetime.strptime(d,'%Y-%m-%d %H:%M:%S') for d in dates]
DATA['dates'] = np.array(dates)
????
Thanks!
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