title: NaN values when new column added to pandas DataFrame
body: <p>I'm trying to generate a new column in a pandas DataFrame that equals values in another pandas DataFrame.  When I attempt to create the new column I just get NaNs for the new column values.</p>
  
  <p>First I use an API call to get some data, and the 'mydata' DataFrame is one column of data indexed by dates</p>
  
  <pre><code>mydata = Quandl.get(["YAHOO/INDEX_MXX.4"],
                      trim_start="2001-04-01", trim_end="2014-03-31",
                      collapse="monthly")
  </code></pre>
  
  <p>The next DataFrame I get from a CSV with the following code, and it contains many columns of data with the same number of rows as 'mydata'</p>
  
  <pre><code>DWDATA = pandas.DataFrame.from_csv("filename",
                                     header=0,
                                     sep=',',
                                     index_col=0,
                                     parse_dates=True,
                                     infer_datetime_format=True)
  </code></pre>
  
  <p>I then try to generate the new column like this:</p>
  
  <pre><code>DWDATA['MXX'] = mydata.iloc[:,0]
  </code></pre>
  
  <p>Again, I just get NaN values.  Can someone help me understand why it's doing this and how to resolve? From what I've read it looks like I might have something wrong with my indexes.  The indexes are dates in each DataFrame, but 'mydata' have end-of-month dates while 'DWDATA' has beginning-of-month dates.</p>