title: pandas DataFrame: normalize one JSON column and merge with other columns
body: <p>I have a pandas DataFrame containing one column with multiple JSON data items as list of dicts. I want to normalize the JSON column and duplicate the non-JSON columns:</p>
  
  <pre><code># creating dataframe
  df_actions = pd.DataFrame(columns=['id', 'actions'])
  rows = [[12,json.loads('[{"type": "a","value": "17"},{"type": "b","value": "19"}]')],
     [15, json.loads('[{"type": "a","value": "1"},{"type": "b","value": "3"},{"type": "c","value": "5"}]')]]
  df_actions.loc[0] = rows[0]
  df_actions.loc[1] = rows[1]
  
  &gt;&gt;&gt;df_actions
     id                                            actions
  0  12  [{'type': 'a', 'value': '17'}, {'type': 'b', '...
  1  15  [{'type': 'a', 'value': '1'}, {'type': 'b', 'v...
  </code></pre>
  
  <p>I want</p>
  
  <pre><code>&gt;&gt;&gt;df_actions_parsed
     id      type    value
     12      a        17
     12      b        19
     15      a        1
     15      b        3
     15      c        5
  </code></pre>
  
  <p>I can normalize JSON data using:</p>
  
  <pre><code>pd.concat([pd.DataFrame(json_normalize(x)) for x in df_actions['actions']],ignore_index=True)
  </code></pre>
  
  <p>but I don't know how to join that back to the id column of the original DataFrame.</p>