title: Using pandas.io.json.json_normalize() with empty list attributes
body: <p>I'm using <code>pandas.io.json.json_normalize()</code> to convert some json into a dataframe, which is then pushed to an SQLite database via <code>df.to_sql()</code>.</p>
  
  <p>However, I'm getting <code>sqlite3.InterfaceError: Error binding parameter 1 - probably unsupported type.</code> when progressing with this, I think due to one of my json fields being an empty array.</p>
  
  <p>I understand I can pass additional path arguments to <code>json_normalize</code> to have it pull out array values and augment the rows with the parent data:</p>
  
  <p><code>json_normalize(json_data, 'field_name_of_empty_array_value')</code></p>
  
  <p>However I can't use this for this instance, as the array is empty and so it returns no values.</p>
  
  <p>Is there a way to set the empty list value to <code>None</code>, or to serialize the empty list via the <code>json_serialize</code> function? Or do I need to do this on the returned <code>dataFrame</code>?</p>
  
  <p>The reason I want to understand whether this is possible is to limit the complexity when dealing with more difficult <code>json</code> which may have empty array values nested deep within its structure.</p>
  
  <p>I've search for this specific question but can't seem to find an example dealing with this specific case, and not sure the <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.json.json_normalize.html" rel="nofollow noreferrer">docs</a> covers it.</p>
  
  <p>Thanks</p>