title: Import historical Metatrader CSV data to Python with Pandas library (date/time parsing)
body: <p>I have some CSV data like</p>
  
  <pre><code>2011.12.08,22:45,1.33434,1.33465,1.33415,1.33419,265
  2011.12.08,23:00,1.33419,1.33542,1.33419,1.33472,391
  2011.12.08,23:15,1.33470,1.33483,1.33383,1.33411,420
  2011.12.08,23:30,1.33413,1.33451,1.33389,1.33400,285
  </code></pre>
  
  <p>coming from Metatrader 4 in a file named EURUSD15.csv</p>
  
  <p>I would like to import this file with Python using Pandas library and read_csv function...</p>
  
  <p>So I did this :</p>
  
  <pre><code>#!/usr/bin/env python
  from pandas import *
  df = read_csv('data/EURUSD15.csv', header=None)
  df.columns = ['Date', 'Time', 'Open', 'High', 'Low', 'Close', 'Volume']
  print(df)
  </code></pre>
  
  <p>I would like now to have date/time parsed...</p>
  
  <p>so I changed</p>
  
  <pre><code>df = read_csv('data/EURUSD15.csv', header=None)
  </code></pre>
  
  <p>to</p>
  
  <pre><code>df = read_csv('data/EURUSD15.csv', header=None, parse_dates=[[1, 2]])
  </code></pre>
  
  <p>But I get this error message</p>
  
  <blockquote>
  <pre><code>Exception: Length mismatch (7 vs 6)
  </code></pre>
  </blockquote>
  
  <p>How can I parse date and time columns and have the 2 columns considered as 1 "datetime" column.</p>