title: Integer slicing in pandas different for rows and columns?
body: <p>Coming from R I try to get my head around integer slicing for pandas dataframes. 
  What puzzles me is the different slicing behavior for rows and columns using the same integer/slice expression.</p>
  
  <pre><code>import pandas as pd
  
  x = pd.DataFrame({'a': range(0,6),
                 'b': range(7,13),
                 'c': range(14, 20)})
  x.ix[0:2, 0:2]   # Why 3 x 2 and not 3 x 3 or 2 x 2?
  
      a   b
  0   0   7
  1   1   8
  2   2   9
  </code></pre>
  
  <p>We get 3 rows but only 2 columns. In the docs I find that different from standard python, <a href="http://pandas.pydata.org/pandas-docs/stable/gotchas.html#endpoints-are-inclusive" rel="nofollow">label based slicing in pandas is inclusive</a>. Does this apply here and is it inclusive for rows but not for columns then?</p>
  
  <p><strong>Can someone explain the behavior and the rationale behind it?</strong> </p>