title: HDFStore with string columns gives issues
body: <p>I have a pandas DataFrame <code>myDF</code> with a few string columns (whose <code>dtype</code> is <code>object</code>) and many numeric columns. I tried the following:</p>
  <pre class="lang-py prettyprint-override"><code>d=pandas.HDFStore(&quot;C:\\PF\\Temp.h5&quot;)
  d['test']=myDF
  </code></pre>
  <p>I got this result:</p>
  <pre class="lang-text prettyprint-override"><code>C:\PF\WinPython-64bit-3.3.3.3\python-3.3.3.amd64\lib\site-packages\pandas\io\pytables.py:2446: PerformanceWarning: 
  
  your performance may suffer as PyTables will pickle object types that it cannot
  map directly to c-types [inferred_type-&gt;mixed,key-&gt;block2_values] 
  [items-&gt;[0, 1, 3, 4, 5, 6, 9, 10, 292, ...]]
  
  warnings.warn(ws, PerformanceWarning)
  </code></pre>
  <p>It seems like the issue is occurring for every column that is a string. For example if I try</p>
  <pre class="lang-py prettyprint-override"><code>myDF[0].dtype
  </code></pre>
  <p>I get</p>
  <pre class="lang-text prettyprint-override"><code>Out[38]: dtype('O')
  </code></pre>
  <p>How can I fix the issue, i.e. change the <code>dtype</code> for string columns so that HDFStore can treat it like a string column?</p>
  <hr />
  <p><strong>EDIT</strong></p>
  <p>More info as requested</p>
  <pre><code>&gt;&gt;&gt; pandas.__version__
  Out[49]: '0.13.1'
  
  &gt;&gt;&gt; tables.__version__
  Out[53]: '3.1.0'
  </code></pre>
  <p>Constructing the pandas data frame as follows:</p>
  <pre class="lang-py prettyprint-override"><code>pandas.read_csv(fName,sep=&quot;|&quot;,header=None, low_memory=False)
  </code></pre>
  <p>When I try</p>
  <pre class="lang-py prettyprint-override"><code>myDF.info()
  </code></pre>
  <p>I get</p>
  <pre class="lang-text prettyprint-override"><code>Int64Index: 153895 entries, 0 to 153894
  Data columns (total 644 columns):
  0      object
  1      object
  2      int64
  3      object
  4      object
  5      object
  6      object
  7      int64
  8      float64
  9      object
  10     object
  11     float64
  12     float64
  ...
  ...
  642    float64
  643    float64
  dtypes: float64(619), int64(2), object(23)
  </code></pre>
  <p>All string columns have been read as <code>object</code>.</p>