On 5/21/2012 1:04 PM Jeremy Traurig said...
Hello,
I am reading a data file with a string time stamp as the first column,
example below:
'03/10/2010 02:00:00'
'03/10/2010 02:10:00'
'03/10/2010 02:20:00'
'03/10/2010 02:30:00'
etc to n number of rows.
I'm using the numpy function genfromtxt to read this data:
import numpy as np
datetime_IN = np.genfromtxt('SIL633_original.txt', delimiter='\t',
skip_header=141, dtype='|S19',
usecols=0)
Now I have a variable called datetime_IN which is an array of datetime
strings to the nth row. I'd like to convert this strings to a numpy
array of integers where each column represents a value of time.
For example, using the same values above, i want an array of integers
to look like this:
3,10,2010,2,0,0
3,10,2010,2,10,0
3,10,2010,2,20,0
3,10,2010,2,30,0
etc to n number of rows.
Using only builtin python functions you can do this as follows:
>>> source = ['03/10/2010 02:00:00',
... '03/10/2010 02:10:00',
... '03/10/2010 02:20:00',
... '03/10/2010 02:30:00']
>>>
>>> for ts in source:
... print ts, [ int(ii) for ii in
ts.replace("/","").replace(":","").split() ]
...
03/10/2010 02:00:00 [3, 10, 2010, 2, 0, 0]
03/10/2010 02:10:00 [3, 10, 2010, 2, 10, 0]
03/10/2010 02:20:00 [3, 10, 2010, 2, 20, 0]
03/10/2010 02:30:00 [3, 10, 2010, 2, 30, 0]
HTH,
Emile
_______________________________________________
Tutor maillist - Tutor@python.org
To unsubscribe or change subscription options:
http://mail.python.org/mailman/listinfo/tutor
