title: Finding average in pandas from a value in a dictionary?
body: <p>experimenting on a project with a large dataset of movies. I have a large data frame, with one row named &quot;Genres&quot; and one named &quot;Vote Average&quot;. My goal is to find the 20 highest rated genres bases on &quot;Vote Average&quot;.</p>
  <p>I would use a group by but I can't seem to figure it out because the genre information looks like this in the column &quot;Genres&quot; :</p>
  <pre><code>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'name': 'Drama'}, {'id': 10749, 'name': 'Romance'}]
  </code></pre>
  <p>How can I extract Comedy, Drama and Romance from the list above?
  How can I group by individual genres while assigning the rows &quot;Vote Average to each genre, so I can print the top 20 rated genres in the data frame?</p>
  <pre><code>     Genres  Vote Average
  1      [{'id': 16, 'name': 'Animation'}, {'id': 35, '...           7.7
  2      [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...           6.9
  3      [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...           6.5
  4      [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...           6.1
  5                         [{'id': 35, 'name': 'Comedy'}]           5.7
  ...                                                  ...           ...
  32255           [{'id': 878, 'name': 'Science Fiction'}]           3.5
  32256  [{'id': 18, 'name': 'Drama'}, {'id': 28, 'name...           5.7
  32257  [{'id': 28, 'name': 'Action'}, {'id': 18, 'nam...           3.8
  32258                                                 []           0.0
  32259                                                 []           0.0
  </code></pre>
  <p>EDIT: Example from Data frame is above. movies_metadata.csv from <a href="https://www.kaggle.com/rounakbanik/the-movies-dataset" rel="nofollow noreferrer">https://www.kaggle.com/rounakbanik/the-movies-dataset</a></p>