title: Pandas: Adding zero values where no rows exist (sparse)
body: <p>I have a Pandas DataFrame with a MultiIndex.  The MultiIndex has values in the range <code>(0,0)</code> to <code>(1000,1000)</code>, and the column has two fields <code>p</code> and <code>q</code>.</p>
  
  <p>However, the DataFrame is <em>sparse</em>.  That is, if there was no measurement corresponding to a particular index (say <code>(3,2)</code>), there won't be <em>any</em> row for it <code>(3,2)</code>.  I'd like to make it not sparse, by filling in these rows with <code>p=0 and q=0</code>. Continuing the example, if I do <code>df.loc[3].loc[2]</code>, I want it to return <code>p=0 q=0</code>, <em>not</em> No Such Record (as it currently does).</p>
  
  <p><em>Clarification: By "sparse", I mean it only in the sense I used it, that zero values are omitted.  I'm not referring to anything in Pandas or Numpy internals.</em></p>