title: Convert row-column-value data to an array - numpy
body: <p>I have data in long format that stores the row#, column# and value as shown below:</p>
  
  <pre><code>ROW  COLUMN   VALUE
    1       1       1
    1       3       3
    2       1       1
    2       2       2
    3       1       1
    3       2       2
    3       3       3
  </code></pre>
  
  <p>Please note that the certain ROW, COLUMN combinations are missing (for instance there is no value for ROW = 1 and COLUMN = 2). I would like to convert this into a 3 x 3 array like so. The missing row column combination gets filled in by 0:</p>
  
  <pre><code>   1    0   3
     1    2   0
     1    2   3
  </code></pre>
  
  <p>My initial approach to this problem was to declare an empty 3 x 3 array, read in the three columns as 1d arrays and loop over rows and columns and update the array based on the value array. For small dimensional cases this seems doable, but for higher dimensions this does not seem to be the "Pythonic" way to do it. Has this problem been tackled in some canned function in <code>numpy</code> package? I looked into <code>reshape</code> - but that assumes no missing values.</p>