title: Pandas read nested json
body: <p>I am curious how I can use pandas to read nested json of the following structure:</p>
  
  <pre><code>{
      "number": "",
      "date": "01.10.2016",
      "name": "R 3932",
      "locations": [
          {
              "depTimeDiffMin": "0",
              "name": "Spital am Pyhrn Bahnhof",
              "arrTime": "",
              "depTime": "06:32",
              "platform": "2",
              "stationIdx": "0",
              "arrTimeDiffMin": "",
              "track": "R 3932"
          },
          {
              "depTimeDiffMin": "0",
              "name": "Windischgarsten Bahnhof",
              "arrTime": "06:37",
              "depTime": "06:40",
              "platform": "2",
              "stationIdx": "1",
              "arrTimeDiffMin": "1",
              "track": ""
          },
          {
              "depTimeDiffMin": "",
              "name": "Linz/Donau Hbf",
              "arrTime": "08:24",
              "depTime": "",
              "platform": "1A-B",
              "stationIdx": "22",
              "arrTimeDiffMin": "1",
              "track": ""
          }
      ]
  }
  </code></pre>
  
  <p>This here keeps the array as json. I would rather prefer it to be expanded into columns.</p>
  
  <pre><code>pd.read_json("/myJson.json", orient='records')
  </code></pre>
  
  <h1>edit</h1>
  
  <p>Thanks for the first answers.
  I should refine my question:
  A flattening of the nested attributes in the array is not mandatory.
  It would be ok to just [A, B, C] concatenate the df.locations['name'].</p>
  
  <p>My file contains multiple JSON objects (1 per line) I would like to keep number, date, name, and locations column. However, I would need to join the locations.</p>
  
  <pre><code>allLocations = ""
  isFirst = True
  for location in result.locations:
      if isFirst:
          isFirst = False
          allLocations = location['name']
      else:
          allLocations += "; " + location['name']
  allLocations
  </code></pre>
  
  <p>My approach here does not seem to be efficient / pandas style.</p>