title: Explode dict from Pandas column
body: <p>I have the following df:</p>
  <pre><code>    movie_id    rating_all 
  0   tt7653254   [{'age': 'all', 'avg_rating': 8.1, 'count': 109326}, {'age': '&lt;18', 'avg_rating': 8.8, 'count': 318}, {'age': '18-29', 'avg_rating': 8.3, 'count': 29740}, {'age': '30-44', 'avg_rating': 8.0, 'count': 33012}, {'age': '45+', 'avg_rating': 7.7, 'count': 7875}]
  1   tt8579674   [{'age': 'all', 'avg_rating': 8.6, 'count': 9420}, {'age': '&lt;18', 'avg_rating': 9.1, 'count': 35}, {'age': '18-29', 'avg_rating': 8.7, 'count': 2437}, {'age': '30-44', 'avg_rating': 8.5, 'count': 2529}, {'age': '45+', 'avg_rating': 8.3, 'count': 960}]
  2   tt7286456   [{'age': 'all', 'avg_rating': 8.6, 'count': 592441}, {'age': '&lt;18', 'avg_rating': 9.1, 'count': 2244}, {'age': '18-29', 'avg_rating': 8.7, 'count': 160506}, {'age': '30-44', 'avg_rating': 8.5, 'count': 160158}, {'age': '45+', 'avg_rating': 8.3, 'count': 30451}]
  3   tt1302006   [{'age': 'all', 'avg_rating': 8.1, 'count': 187675}, {'age': '&lt;18', 'avg_rating': 8.7, 'count': 461}, {'age': '18-29', 'avg_rating': 8.3, 'count': 41951}, {'age': '30-44', 'avg_rating': 7.9, 'count': 59729}, {'age': '45+', 'avg_rating': 7.8, 'count': 18550}]
  4   tt7131622   [{'age': 'all', 'avg_rating': 7.8, 'count': 323152}, {'age': '&lt;18', 'avg_rating': 8.4, 'count': 955}, {'age': '18-29', 'avg_rating': 7.9, 'count': 82133}, {'age': '30-44', 'avg_rating': 7.6, 'count': 95878}, {'age': '45+', 'avg_rating': 7.5, 'count': 26383}]
  5   tt8637428   [{'age': 'all', 'avg_rating': 7.7, 'count': 21362}, {'age': '&lt;18', 'avg_rating': 8.0, 'count': 45}, {'age': '18-29', 'avg_rating': 7.9, 'count': 5901}, {'age': '30-44', 'avg_rating': 7.6, 'count': 6492}, {'age': '45+', 'avg_rating': 7.3, 'count': 2133}]
  </code></pre>
  <p>And I want to transform it to something like:</p>
  <pre><code>    movie_id    all_avg     all_count   &lt;18_avg     &lt;18_count   18-29_avg
  0   tt7653254   8.1         109326      8.8         318         8.3
  1   tt8579674   8.6         9420        9.1         35          8.7
  2   tt7286456   8.6         592441      9.1         2244        8.7
  3   tt1302006   8.1         187675      8.7         461         8.3
  4   tt7131622   7.8         323152      8.4         955         7.9
  5   tt8637428   7.7         21362       8           45          7.9
  </code></pre>
  <p>and so on...</p>
  <p>I've tried</p>
  <pre><code>ratings.set_index('movie_id')['rating_all'].apply(pd.Series).reset_index()
  </code></pre>
  <p>and using</p>
  <pre><code>json_normalize(data, 
                 record_path=['rating_all'], 
                 meta=['movie_id']).set_index('movie_id')
  </code></pre>
  <p>but none give something similiar. Is there an easy way to explode the dictionary into columns?</p>
  <p>Data to produce the DataFrame:</p>
  <pre><code>data = {'movie_id': ['tt7653254', 'tt8579674', 'tt7286456', 'tt1302006', 'tt7131622', 'tt8637428'], 
          'rating_all': [[{'age': 'all', 'avg_rating': 8.1, 'count': 109326},
                          {'age': '&lt;18', 'avg_rating': 8.8, 'count': 318},   
                          {'age': '18-29', 'avg_rating': 8.3, 'count': 29740},
                          {'age': '30-44', 'avg_rating': 8.0, 'count': 33012},
                          {'age': '45+', 'avg_rating': 7.7, 'count': 7875}],
                         [{'age': 'all', 'avg_rating': 8.6, 'count': 9420},
                          {'age': '&lt;18', 'avg_rating': 9.1, 'count': 35},
                          {'age': '18-29', 'avg_rating': 8.7, 'count': 2437},
                          {'age': '30-44', 'avg_rating': 8.5, 'count': 2529},
                          {'age': '45+', 'avg_rating': 8.3, 'count': 960}],
                         [{'age': 'all', 'avg_rating': 8.6, 'count': 592441},
                          {'age': '&lt;18', 'avg_rating': 9.1, 'count': 2244},
                          {'age': '18-29', 'avg_rating': 8.7, 'count': 160506},
                          {'age': '30-44', 'avg_rating': 8.5, 'count': 160158},
                          {'age': '45+', 'avg_rating': 8.3, 'count': 30451}],
                         [{'age': 'all', 'avg_rating': 8.1, 'count': 187675},
                          {'age': '&lt;18', 'avg_rating': 8.7, 'count': 461},
                          {'age': '18-29', 'avg_rating': 8.3, 'count': 41951},
                          {'age': '30-44', 'avg_rating': 7.9, 'count': 59729},
                          {'age': '45+', 'avg_rating': 7.8, 'count': 18550}],
                         [{'age': 'all', 'avg_rating': 7.8, 'count': 323152},
                          {'age': '&lt;18', 'avg_rating': 8.4, 'count': 955},
                          {'age': '18-29', 'avg_rating': 7.9, 'count': 82133},
                          {'age': '30-44', 'avg_rating': 7.6, 'count': 95878},
                          {'age': '45+', 'avg_rating': 7.5, 'count': 26383}],
                         [{'age': 'all', 'avg_rating': 7.7, 'count': 21362},
                          {'age': '&lt;18', 'avg_rating': 8.0, 'count': 45},
                          {'age': '18-29', 'avg_rating': 7.9, 'count': 5901},
                          {'age': '30-44', 'avg_rating': 7.6, 'count': 6492},
                          {'age': '45+', 'avg_rating': 7.3, 'count': 2133}]]}
  </code></pre>