title: Proper way to use "opposite boolean" in Pandas data frame boolean indexing
body: <p>I wanted to use a boolean indexing, checking for rows of my data frame where a particular column does <strong>not</strong> have <code>NaN</code> values.  So, I did the following:</p>
  
  <pre><code>import pandas as pd
  my_df.loc[pd.isnull(my_df['col_of_interest']) == False].head()
  </code></pre>
  
  <p>to see a snippet of that data frame, including only the values that are not <code>NaN</code> (most values are <code>NaN</code>).</p>
  
  <p>It worked, but seems less-than-elegant.  I'd want to type:</p>
  
  <pre><code>my_df.loc[!pd.isnull(my_df['col_of_interest'])].head()
  </code></pre>
  
  <p>However, that generated an error.  I also spend a lot of time in R, so maybe I'm confusing things.  In Python, I usually put in the syntax "not" where I can.  For instance, <code>if x is not none:</code>, but I couldn't really do it here.  Is there a more elegant way?  I don't like having to put in a senseless comparison.</p>