title: How to merge mostly duplicated rows
body: <p>Some of my data looks like:</p>
  <pre><code>date, name, value1, value2, value3, value4
  1/1/2001,ABC,1,1,,
  1/1/2001,ABC,,,2,
  1/1/2001,ABC,,,,35
  </code></pre>
  <p>I am trying to get to the point where I can run</p>
  <pre><code>data.set_index(['date', 'name'])
  </code></pre>
  <p>But, with the data as-is, there are of course duplicates (as shown in the above), so I cannot do this (and I don't want an index with duplicates, and I can't simply drop_duplicates(), since this would lose data).</p>
  <p>I would like to be able to force rows which have the same [date, name] values into a single rows, if they can be successfully converged based on certain values being NaN (similar to the behavior of combine_first()).  E.g., the above would end up at</p>
  <pre><code>date, name, value1, value2, value3, value4
  1/1/2001,ABC,1,1,2,35
  </code></pre>
  <p>If two values are different and one is not NaN, the two rows should not be converged (this would probably be an error that I would need to follow up on).</p>
  <p>(To extend the above example, there may in fact be an arbitrary number of lines--given an arbitrary number of columns--which should be able to be converged into one single line.)</p>
  <p>This feels like a problem that should be very solvable via pandas, but I am having trouble figuring out an elegant solution.</p>