title: Selecting columns from pandas.HDFStore table
body: <p>How can I retrieve specific columns from a pandas HDFStore?  I regularly work with very large data sets that are too big to manipulate in memory.  I would like to read in a csv file iteratively, append each chunk into HDFStore object, and then work with subsets of the data.  I have read in a simple csv file and loaded it into an HDFStore with the following code:    </p>
  
  <pre><code>tmp = pd.HDFStore('test.h5')
  chunker = pd.read_csv('cars.csv', iterator=True, chunksize=10, names=['make','model','drop'])
  tmp.append('df', pd.concat([chunk for chunk in chunker], ignore_index=True))
  </code></pre>
  
  <p>And the output:</p>
  
  <pre><code>In [97]: tmp
  Out[97]:
  &lt;class 'pandas.io.pytables.HDFStore'&gt;
  File path: test.h5
  /df     frame_table (typ-&gt;appendable,nrows-&gt;1930,indexers-&gt;[index])
  </code></pre>
  
  <p>My Question is how do I access specific columns from <code>tmp['df']</code>?  The documenation makes mention of a <code>select()</code> method and some <code>Term</code> objects.  The examples provided are applied to Panel data; however, and I'm too much of a novice to extend it to the simpler data frame case.  My guess is that I have to create an index of the columns somehow.  Thanks!</p>