title: Pandas json_normalize and null values in JSON
body: <p>I have this sample JSON</p>
  
  <pre><code>{
      "name":"John",
      "age":30,
      "cars": [
          { "name":"Ford", "models":[ "Fiesta", "Focus", "Mustang" ] },
          { "name":"BMW", "models":[ "320", "X3", "X5" ] },
          { "name":"Fiat", "models":[ "500", "Panda" ] }
      ]
   }
  </code></pre>
  
  <p>When I need to convert JSON to pandas DataFrame I use following code </p>
  
  <pre><code>import json
  from pandas.io.json import json_normalize
  from pprint import pprint
  
  with open('example.json', encoding="utf8") as data_file:
      data = json.load(data_file)
  normalized = json_normalize(data['cars'])
  </code></pre>
  
  <p>This code works well but in the case of some empty cars (null values) I'm not possible to normalize_json.</p>
  
  <p>Example of json</p>
  
  <pre><code>{
      "name":"John",
      "age":30,
      "cars": [
          { "name":"Ford", "models":[ "Fiesta", "Focus", "Mustang" ] },
          null,
          { "name":"Fiat", "models":[ "500", "Panda" ] }
      ]
   }
  </code></pre>
  
  <p>Error that was thrown</p>
  
  <pre><code>AttributeError: 'NoneType' object has no attribute 'keys'
  </code></pre>
  
  <p>I tried to ignore errors in json_normalize, but didn't help</p>
  
  <pre><code>normalized = json_normalize(data['cars'], errors='ignore')
  </code></pre>
  
  <p>How should I handle null values in JSON?</p>