title: Keeping NaN values and dropping nonmissing values
body: <p>I have a DataFrame where I would like to keep the rows when a particular variable has a <code>NaN</code> value and drop the non-missing values.</p>
  <p>Example:</p>
  <pre><code>ticker  opinion  x1       x2  
  aapl    GC       100      70  
  msft    NaN      50       40  
  goog    GC       40       60  
  wmt     GC       45       15  
  abm     NaN      80       90  
  </code></pre>
  <p>In the above DataFrame, I would like to drop all observations where opinion is not missing (so, I would like to drop the rows where ticker is <code>aapl, goog, and wmt</code>).</p>
  <p>Is there anything in pandas that is the opposite to <code>.dropna()</code>?</p>