import json
import pandas as pd

df = pd.read_csv('txn_data.csv')

def get_nested_rec(key, grp):
    rec = {}
    rec['date'] = key[0]
    rec['name'] = key[1]
    rec['value'] = key[2]

    for field in ['name','value']:
        rec[field] = list(grp[field].unique())

    return rec

records = []
for key, grp in df.groupby(['date']):
    rec = get_nested_rec(key, grp)
    records.append(rec)

records = dict(data = records)

print(json.dumps(records, indent=4))

date,name,value
1/1/13,Quick Serve,304127
1/1/13,Restaurant,1843286
1/1/13,Retail,239675
1/2/13,Quick Serve,422847
1/2/13,Restaurant,1582848
1/2/13,Retail,394358

desired_output = [
   {
      "date":"2017-01-01",
      "details":[
         {
            "name":"Retail",
            "value":9192
         },
         {
            "name":"Restaurant",
            "value":6753
         },
         {
            "name":"Quickserve",
            "value":1219
         }
      ]
   },
   {
      "date":"2017-02-01",
      "details":[
         {
            "name":"Retail",
            "value":9192
         },
         {
            "name":"Restaurant",
            "value":6753
         },
         {
            "name":"Quickserve",
            "value":1219
         }
      ]
   }
]

{
    "data": [
        {
            "date": "1",
            "name": [
                "Automotive",
                "Durable Goods",
                "Entertainment",
                "Food",
                "Lodging",
                "Petroleum",
                "Quick Serve",
                "Restaurant",
                "Retail",
                "Service",
                "Transportation & Utilities",
                "Unknown"
            ],
            "value": [
                91406,
                9889,
                172676,
                358922,
                63502,
                1982048,
                304127,
                1843286,
                239675,
                106462,
                25924,
                909
            ]
        },
        {
            "date": "1",
            "name": [
                "Automotive",
                "Durable Goods",
                "Entertainment",
                "Food",
                "Lodging",
                "Petroleum",
                "Quick Serve",
                "Restaurant",
                "Retail",
                "Service",
                "Transportation & Utilities",
                "Unknown"
            ],
            "value": [
                146041,
                33090,
                103159,
                336956,
                66726,
                2191346,
                422847,
                1582848,
                394358,
                339989,
                49477,
                494
            ]
        }
    ]
}