Pandas Dataframe to Nested JSON

Question

I am trying to convert a Pandas Dataframe to a JSON object. My Dataframe contains data in the following format:

         student      date    grade         course
0     Student_1    2017-06-25  93          ENGLISH
1     Student_2    2017-06-25  83          ENGLISH
2     Student_1    2017-06-25  93          MATH
3     Student_2    2017-06-25  83          MATH
4     Student_1    2017-06-26  90          MATH
5     Student_2    2017-06-26  85          MATH
6     Student_1    2017-06-26  96          ENGLISH
7     Student_2    2017-06-26  99          ENGLISH

I want to convert it to a JSON object in the following format:

[
    {'ENGLISH': [
      {
        'date' : '2017-06-25',
        'Student_1' : 93,
        'Student_2' : 83
      },

      {
        'date' : '2017-06-26',
        'Student_1' : 96,
        'Student_2' : 89
      }]
   },

    {'MATH': [
      {
        'date' : '2017-06-25',
        'Student_1' : 93,
        'Student_2' : 83
      },

      {
        'date' : '2017-06-26',
        'Student_1' : 90,
        'Student_2' : 85
      }]
    }
]

A simple .to_json() call did not do the trick for me. Is there anyway I can create the JSON object in the required format in Pandas?

Answer 1

Try that :

file.csv:

student,date,grade,course
0,Student_1,2017-06-25,93,ENGLISH
1,Student_2,2017-06-25,83,ENGLISH
2,Student_1,2017-06-25,93,MATH
3,Student_2,2017-06-25,83,MATH
4,Student_1,2017-06-26,90,MATH
5,Student_2,2017-06-26,85,MATH
6,Student_1,2017-06-26,96,ENGLISH
7,Student_2,2017-06-26,99,ENGLISH

Execute:

from collections import defaultdict

import json
import pandas as pd


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

json_doc = defaultdict(list)
for _id in df.T:
    data = df.T[_id]
    key = data.course
    for elt in json_doc[key]:
        if elt["date"] == data.date:
            elt[data.student] = data.grade
            break
    else:
        values = {'date': data.date, data.student: data.grade}
        json_doc[key].append(values)

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

Output:

{
    "ENGLISH": [
        {
            "date": "2017-06-25",
            "Student_1": 93,
            "Student_2": 83
        },
        {
            "date": "2017-06-26",
            "Student_1": 96,
            "Student_2": 99
        }
    ],
    "MATH": [
        {
            "date": "2017-06-25",
            "Student_1": 93,
            "Student_2": 83
        },
        {
            "date": "2017-06-26",
            "Student_1": 90,
            "Student_2": 85
        }
    ]
}

Answer 2

You can first define a function to convert sub-groups to json, then apply this function to each group, and then merge sub-group jsons to a single json object.

def f(x):
    return (dict({'date':x.date.iloc[0]},**{k:v for k,v in zip(x.student,x.grade)}))

(
    df.groupby(['course','date'])
      .apply(f)
      .groupby(level=0)
      .apply(lambda x: x.tolist())
      .to_dict()
)
Out[1006]: 
{'ENGLISH': [{'Student_1': 93, 'Student_2': 83, 'date': '2017-06-25'},
  {'Student_1': 96, 'Student_2': 99, 'date': '2017-06-26'}],
 'MATH': [{'Student_1': 93, 'Student_2': 83, 'date': '2017-06-25'},
  {'Student_1': 90, 'Student_2': 85, 'date': '2017-06-26'}]}