title: pandas - filter rows with highest sums
body: <p>The below code demonstrates count of category entries (<code>b</code> for <code>business</code>, <code>e</code> for <code>entertainment</code>, <code>t</code> for <code>science/technology</code>, <code>m</code> for <code>health</code>) for every unique publisher. Where I need help is filtering to top ten pubishers with highest total counts of category entries.</p>
  
  <pre><code>pub_cat_group = headline_df.groupby(['PUBLISHER', 'CATEGORY'])['TITLE'].count()
  pub_cat_group
  
  100.7 WZLX Classic Rock                               b             1
                                                        e            11
  1011now                                               b             2
                                                        e             2
                                                        m             7
                                                        t            11
  106 JACK fm                                           b             8
                                                        t             1
  </code></pre>
  
  <p>I know how to pull highest individual category counts (see code below) but I don't know how to mix this logic with the above code.</p>
  
  <pre><code>top_pub_cats = headline_df['CATEGORY'].groupby(headline_df['PUBLISHER']).value_counts().sort_values().nlargest(10)
  </code></pre>
  
  <p>My ultimate objective is to plot category counts for the top ten publishers so that I can visualize each category's category frequency among top publications. </p>