title: HDF5 taking more space than CSV?
body: <p>Consider the following example:</p>
  <h2>Prepare the data:</h2>
  <pre><code>import string
  import random
  import pandas as pd
  
  matrix = np.random.random((100, 3000))
  my_cols = [random.choice(string.ascii_uppercase) for x in range(matrix.shape[1])]
  mydf = pd.DataFrame(matrix, columns=my_cols)
  mydf['something'] = 'hello_world'
  </code></pre>
  <h2>Set the highest compression possible for HDF5:</h2>
  <pre><code>store = pd.HDFStore('myfile.h5',complevel=9, complib='bzip2')
  store['mydf'] = mydf
  store.close()
  </code></pre>
  <h2>Save also to CSV:</h2>
  <pre><code>mydf.to_csv('myfile.csv', sep=':')
  </code></pre>
  <p>The result is:</p>
  <ul>
  <li><code>myfile.csv</code> is 5.6 MB big</li>
  <li><code>myfile.h5</code> is 11 MB big</li>
  </ul>
  <p>The difference grows bigger as the datasets get larger.</p>
  <p>I have tried with other compression methods and levels. Is this a bug? (I am using Pandas 0.11 and the latest stable version of HDF5 and Python).</p>