title: Creating pandas DataFrame from numpy array leads to strange errors
body: <p>The short of the long is that DataFrames are spitting out endianess errors when I try to create them from functional numpy arrays. Here is a pastebin, more details below:
  <a href="http://pastebin.com/Sdg9EM61" rel="nofollow">http://pastebin.com/Sdg9EM61</a></p>
  
  <p>In my field we store data in .FIT format, which is a binary format (this may be useful info later)</p>
  
  <p>I don't really know how to address this following few lines of code and error.</p>
  
  <pre><code>d = fits.getdata('file.fit')
  d2 = np.array(d)
  </code></pre>
  
  <p>Then you can do cool tricks with this like:</p>
  
  <pre><code>d2[d2['key1'] &gt; 10.]
  d2[['key1', 'key2']]
  </code></pre>
  
  <p>etc.</p>
  
  <p>When I convert it to a pandas DataFrame</p>
  
  <pre><code>d3 = pandas.DataFrame(d2)
  </code></pre>
  
  <p>things start to get weird. The column names have changed for example:</p>
  
  <pre><code>d3.columns
  </code></pre>
  
  <p>returns</p>
  
  <pre><code>Index([u'key1', u'key2'], dtype='object')
  </code></pre>
  
  <p>with this new u in front of every column name as opposed to</p>
  
  <pre><code>d2.dtype
  </code></pre>
  
  <p>returning</p>
  
  <pre><code>dtype([('key1', '&gt;i4'), ('key2', 'S19')])
  </code></pre>
  
  <p>although the data types look ok in the dataframe when you do d3.dtypes instead of d3.columns...</p>
  
  <p>Anyways, the DataFrame has all the data and preserves the data types and I can print out characterizing data and such, but as soon as I try to do something like this:</p>
  
  <pre><code>d3[d3['key1'] &gt; 10.]
  </code></pre>
  
  <p>I get a monster error about endianess:</p>
  
  <pre><code>ValueError: Big-endian buffer not supported on little-endian compiler
  </code></pre>
  
  <p>Any insight as to what this means and how to fix?</p>