title: PySpark - Convert list of JSON objects to rows
body: <p>I want to convert a list of objects and store their attributes as columns.</p>
  
  <pre><code>{
    "heading": 1,
    "columns": [
      {
        "col1": "a",
        "col2": "b",
        "col3": "c"
      },
      {
        "col1": "d",
        "col2": "e",
        "col3": "f"
      }
    ]
  }
  </code></pre>
  
  <p>Final Result</p>
  
  <pre><code>heading | col1 | col2 | col3
  1       | a    | b    | c
  1       | d    | e    | f
  </code></pre>
  
  <p>I am currently flattening my data (and excluding the columns column)</p>
  
  <pre><code>df = target_table.relationalize('roottable', temp_path)
  </code></pre>
  
  <p>However, for this use case, I will need the columns column. I saw examples where <code>arrays_zip</code> and <code>explode</code> was used. Would I need to iterate through each object or is there an easier way to extract each object and convert into a row?</p>