title: Pandas replace all items in a row with NaN if one value is NaN
body: <p>I want to get rid of some records with NaNs. This works perfectly:</p>
  
  <pre><code>df.dropna(axis=0, how='any',inplace=True)
  </code></pre>
  
  <p>However, it changes the shape of my dataframe, and the index is no longer uniformly spaced. Therefore, I'd like to replace all items in these rows with <code>np.nan</code>. Is there a simple way to do this? </p>
  
  <p>I was thinking about resampling the dataframe after <code>dropna</code>, but that only seems to work with a prescribed interval, whereas I would rather use the original index. Another approach would be to loop over the dataframe with <code>iterrows</code>, but that also feels cumbersome.</p>