Python 2.7 CSV graph time format - python-2.7

Question

I have a CSV file, one of colons value is timestamps but when I use numpy.getfromtxt it change it to string. My goal is to create a graph but with normal time format I prefer seconds only.
this is my array that I get from bellow code:
array([('0:00:00',), ('0:00:00.001000',), ('0:00:00.002000',),
('0:00:00.081000',), ('0:00:00.095000',), ('0:00:00.195000',),
('0:00:00.294000',), ...
this is my code:
col1 = numpy.genfromtxt("mycsv.csv",usecols=(1),delimiter=',',dtype=None, names=True)
The problem that I am having that format is in string but I need it in seconds (us can be ignored or not). How can I achive that?

Answer 1

If you can, the best way for working with csv files in python is to use pandas. It takes care of this for you. I will assume the name of the time column is time, change it to whatever you use:
>>> import numpy as np
>>> import pandas as pd
>>>
>>> df = pd.read_csv('test.csv', parse_dates=[1]) # read time as date
>>> print(df)
test1 time test2 test3
0 5 2015-08-20 00:00:00.000 10 11.7
1 5 2015-08-20 00:00:00.001 11 11.6
2 5 2015-08-20 00:00:00.002 12 11.5
3 5 2015-08-20 00:00:00.081 13 11.4
4 5 2015-08-20 00:00:00.095 14 11.3
5 5 2015-08-20 00:00:00.195 15 11.2
6 5 2015-08-20 00:00:00.294 16 11.1
>>> df['time'] -= pd.datetime.now().date() # convert to timedelta
>>> print(df)
test1 time test2 test3
0 5 00:00:00 10 11.7
1 5 00:00:00.001000 11 11.6
2 5 00:00:00.002000 12 11.5
3 5 00:00:00.081000 13 11.4
4 5 00:00:00.095000 14 11.3
5 5 00:00:00.195000 15 11.2
6 5 00:00:00.294000 16 11.1
>>> df['time'] /= np.timedelta64(1,'s') # convert to seconds
>>> print(df)
test1 time test2 test3
0 5 0.000 10 11.7
1 5 0.001 11 11.6
2 5 0.002 12 11.5
3 5 0.081 13 11.4
4 5 0.095 14 11.3
5 5 0.195 15 11.2
6 5 0.294 16 11.1
You can work with pandas dataframes (what you have here) and series (what you would from getting a single column, such as df['time']) in most of the same ways as numpy arrays, including plotting. However, if you really, really need to convert it to a numpy array, it is as easy as arr = df['time'].values.

Answer 2

use the datetime library
import datetime
for x in array:
for y .... # it's not realy obvious what the nesting is here...
timestamp = datetime.strptime(y, '%H:%M:%S.%f')

Answer 3

You can use a converter for the timestamp field.
For example, suppose times.dat contains:
time
0:00:00
0:00:00.001000
0:00:00.002000
0:00:00.081000
0:00:00.095000
0:00:00.195000
0:00:00.294000
Define a converter that converts a timestamp string into the number of seconds as a floating point value:
In [5]: def convert_timestamp(s):
...: h, m, s = [float(w) for w in s.split(':')]
...: return h*3600 + m*60 + s
...:
Then use the converter in genfromtxt:
In [21]: genfromtxt('times.dat', skiprows=1, converters={0: convert_timestamp})
Out[21]: array([ 0. , 0.001, 0.002, 0.081, 0.095, 0.195, 0.294])