title: Assign custom categories to json data - pandas
body: <p>Assigning labels to raw data instead of getting new indicator columns from get_dummies. I want something like this :</p>
  
  <p>json_input:</p>
  
  <p>[{id:100,vehicle_type:"Car", time:"2017-04-06 01:39:43", zone="A", type:"Checked"},
  {id:101,vehicle_type:"Truck", time:"2017-04-06 02:35:45", zone="B", type:"Unchecked"},
  {id:102,vehicle_type:"Truck", time:"2017-04-05 03:20:12", zone="A", type:"Checked"},
  {id:103,vehicle_type:"Car", time:"2017-04-04 10:05:04", zone="C", type:"Unchecked"}
  ]</p>
  
  <p>Result:</p>
  
  <ul>
  <li>id , vehicle_type, time_range,  zone, type </li>
  <li>100,     0       ,     1     ,  1   , 1 </li>
  <li>101,     1       ,     1     ,  2   , 0</li>
  <li>102,     1       ,     2     ,   1  ,  1</li>
  <li>103,     0       ,     3     ,   3  ,  0</li>
  </ul>
  
  <p>time stamp- TS
  columns -> vehicle_type, type are binary , time_range (1 -> (TS1-TS2),2 -> (TS3-TS4), 3->(TS5-TS6)), zone-> categorical(1,2 or 3).
  I want to auto assign these labels when I feed the flattened json to dataframe in pandas. Is this possible? ( I do not want zone_1, type_1, vehicle_type_3 indicator columns from get_dummies in pandas). If not possible with pandas, please suggest python lib for this automation.</p>