# Exporting ISO date in Numpy

I am new in python and have a basic question I can not find a solution on the net.

I have a table named IBTsample.txt, containing:

• Season,ISO_time,Latitude,Longitude,Enum
• 2009,2009-12-24 12:00:00,6.50,85.00,2
• 2009,2009-12-25 06:00:00,8.00,84.50,6
• 2009,2009-12-25 00:00:00,7.00,84.50,4
• 2009,2009-12-24 18:00:00,6.50,85.00,3
• 2009,2009-12-24 09:00:00,6.50,85.00,1
• 2009,2009-12-25 03:00:00,7.00,84.50,5

What I would like to do is import it as a numpy array, do some processing (for the time being simply sort the records par date) and export the processed table in a new .txt file.

`from numpy import * import pylab rawtable = loadtxt('IBTsample.txt', delimiter=',', skiprows=1, converters= {1:pylab.datestr2num},\ dtype={'names':('Season','ISO_time','Latitude','Longitude','Enum'),\ 'formats':('uint16','float','float16','float16','uint8')}) sortable = (sort(rawtable, order=('ISO_time'))).copy() savetxt('IBTsorted.txt', sortable, fmt='%d,%.3f,%.1f,%.1f,%d') `

I have ti use the pylab.datestr2num function during the importation but can not find an inverse function to export the date and time in an ISO format.

Any help or idea would be helful, thanks in advance.

You could use `matplotlib.dates.num2date` to convert the nums back into datetime objects. Then call `isoformat()` to get the date as a string in ISO-8601 format.

```import numpy as np import matplotlib.dates as md```

``` def num2isodate(num): result=md.num2date(num).isoformat() return result ```

```rawtable = np.loadtxt( 'IBTsample.txt', delimiter=',', skiprows=1, converters= {1:md.datestr2num}, dtype={'names':['Season','ISO_time','Latitude','Longitude','Enum'], 'formats':['uint16','float','f4','f4','uint8']}) ```

Convert the `ISO_time` dtype to `object`. This allows the column to hold float values at first, and strings later. Note that `astype` (below) returns a copy, so calling `copy` explicitly is no longer needed. Also, since you called `copy`, I assume holding two copies of the array in memory is not a problem. (If memory were tight, we could write the array line-by-line using the csv module instead of using `np.savetxt`. But since memory is not a problem, `np.savetxt` is more convenient.)

```sortable = rawtable.astype({'names':['Season','ISO_time','Latitude','Longitude','Enum'], 'formats':['uint16','object','f4','f4','uint8']}) sortable = np.sort(sortable, order=('ISO_time')) sortable['ISO_time'] = [num2isodate(num) for num in sortable['ISO_time']] np.savetxt('IBTsorted.txt', sortable, fmt='%d,%s,%.1f,%.1f,%d') ```

PS. I'd recommend never using `from module import *`, especially when `module` is `numpy`. It overwrites a number of Python builtin functions, such as `abs`, `all`, `any`, `min`, `max`, `sum`, `round`, etc. Not only does that make it harder to call the Python builtins, but also makes it easy to write code which looks right but contains hard-to-find or subtle bugs.

In addition to @unutbu's answer, if you're using the latest version of numpy (`>= 1.7`), there is a native date dtype.

In that case, try something like this:

```import numpy as np import dateutil.parser```

``` rawtable = np.genfromtxt('test_in.txt', names=True, delimiter=',', converters={1:dateutil.parser.parse}, dtype=[np.uint16, np.datetime64, np.float, np.float, np.uint8]) sorttable = np.sort(rawtable, order=('ISO_time')) ```

```with open('test_out.txt', 'w') as outfile: outfile.write(','.join(sorttable.dtype.names) + '\n') np.savetxt(outfile, sorttable, fmt='%i,%r,%f,%f,%i') ```

This yields:

```Season,ISO_time,Latitude,Longitude,Enum 2009,2009-12-24 09:00:00,6.500000,85.000000,1 2009,2009-12-24 12:00:00,6.500000,85.000000,2 2009,2009-12-24 18:00:00,6.500000,85.000000,3 2009,2009-12-25 00:00:00,7.000000,84.500000,4 2009,2009-12-25 03:00:00,7.000000,84.500000,5 2009,2009-12-25 06:00:00,8.000000,84.500000,6 ```

Category:python Views:0 Time:2011-11-02

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