title: Using compression with Pandas and HD5 / HDFStore
body: <p>For a few aspects of a project, using "h5" storage would be ideal. However, the files are becoming massive and frankly we're running out of space.</p>
  
  <p>This statement...</p>
  
  <pre><code> store.put(storekey, data, table=False, compression='gzip')
  </code></pre>
  
  <p>does not produce any difference in terms of file size than...</p>
  
  <pre><code> store.put(storekey, data, table=False)
  </code></pre>
  
  <p>Is using compression even possible when going through Pandas?</p>
  
  <p>... if it isn't possible, I don't mind using h5py, however, I'm uncertain what to put for a "datatype" as the DataFrame contains all sorts of types (strings, float, int etc.)</p>
  
  <p>Any help/insight would be appreciated!</p>