title: Dask nested JSON to dataframe
body: <p>I have a gigantic, nested JSON file that I need to parse. I'm currently doing it in Pandas, but would like to find out if I can implement it in Dask for speed/scalability. </p>
  
  <p>My current pandas code is: </p>
  
  <pre><code>import pandas as pd
  json_data = pd.read_json(input_file)
  feature_df = json_normalize(json_obj["Features"], 
      record_path=["Cycles","Pools"],
      meta=["FeatureID"])
  </code></pre>
  
  <p>I've gotten as far as the following code in Dask, but I cannot find any more info on how to do this on the interwebs: </p>
  
  <pre><code>import dask.bag as db
  import json
  json_data = db.read_text(input_file).map(json.loads)
  </code></pre>
  
  <p>This returns a dask bag that I need to figure out how to properly parse. </p>
  
  <p>My JSON is structured as follows: </p>
  
  <pre><code>{
  "FovID":1,
  "Features":[
      {
          "FeatureID":"1_166_155",
          "X":166,
          "Y":155,
          "Cycles":[
              {
                  "CycleID":31,
                  "Pools":[
                      {
                          "PoolID":1,
                          "BC":"1224",
                          "Qual":"999",
                          "Category":"000"
                      },
                      {
                          "PoolID":2,
                          "BC":"0",
                          "Qual":"999",
                          "Category":"000"}
                   }]}
  </code></pre>
  
  <p><strong>Solution:</strong>
  Instead of using a dask bag, I followed @mdurant's advice and read the JSON directly into a dataframe as follows: </p>
  
  <pre><code>json_data = dd.read_json(self.input_s6, orient='columns').compute()
  </code></pre>