title: Pandas join/merge/concat two dataframes
body: <p>I am having issues with joins in pandas and I am trying to figure out what is wrong. 
     Say I have a <code>dataframe</code> x:</p>
  
  <pre><code>&lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 1941 entries, 2004-10-19 00:00:00 to 2012-07-23 00:00:00
  Data columns:
  close    1941  non-null values
  high     1941  non-null values
  low      1941  non-null values
  open     1941  non-null values
  dtypes: float64(4)
  </code></pre>
  
  <p>should I be able to join it with y on index with a simple join command where y = x except colnames have +2.  </p>
  
  <pre><code> &lt;class 'pandas.core.frame.DataFrame'&gt;
   DatetimeIndex: 1941 entries, 2004-10-19 00:00:00 to 2012-07-23 00:00:00
   Data columns:
   close2    1941  non-null values
   high2     1941  non-null values
   low2      1941  non-null values
   open2     1941  non-null values
   dtypes: float64(4)
  
   y.join(x) or pandas.DataFrame.join(y,x):
   &lt;class 'pandas.core.frame.DataFrame'&gt;
   DatetimeIndex: 34879 entries, 2004-12-16 00:00:00 to 2012-07-12 00:00:00
   Data columns:
   close2    34879  non-null values
   high2     34879  non-null values
   low2      34879  non-null values
   open2     34879  non-null values
   close     34879  non-null values
   high      34879  non-null values
   low       34879  non-null values
   open      34879  non-null values
   dtypes: float64(8)
  </code></pre>
  
  <p>I expect the final to have 1941 non-values for both. I tried merge as well but I have the same issue.</p>
  
  <p>I had thought the right answer was pandas.concat([x,y]), but this does not do what I intend either.</p>
  
  <pre><code>In [83]: pandas.concat([x,y]) 
  Out[83]: &lt;class 'pandas.core.frame.DataFrame'&gt; 
  DatetimeIndex: 3882 entries, 2004-10-19 00:00:00 to 2012-07-23 00:00:00 
  Data columns: 
  close2 3882 non-null values 
  high2 3882 non-null values 
  low2 3882 non-null values 
  open2 3882 non-null values 
  dtypes: float64(4) 
  </code></pre>
  
  <p>edit: 
  If you are having issues with join, read Wes's answer below. I had one time stamp that was duplicated.</p>