Convert CSV Data to Nested JSON

Question

I have a task to transform data from csv file to nested JSON file using python for web usage. I tried the Python code in this article . The desired output would be one member_id shows one time in the JSON file, same for the tag_name under one member_id . The problem is when I tried groupby with only member_id , the tag_name 'm1' would show multiple times under 'abc123' for example. If I tried groupby with both member_id and tag_name , 'abc123' would show up two times for tag 'm1' and 'm2'. I've been googling for a while but most resolutions only for one nested(not sure if I use the right term). Please let me know if any possible way to do this.

Sample Code:

import json
import pandas as pd
df = pd.read_csv('../detail.csv', sep=',', header=0
                 , index_col=False
                 , dtype = {'member_id':str,'tag_name':str,'detail_name':str,'detail_value':str} )
group = df.groupby(['member_id','tag_name'])

finalList, finalDict = [], {}
for key, value in group:
    dictionary, dictionary1, dictList, dictList1 = {}, {}, [], []
    j = group.get_group(key).reset_index(drop=True)
    dictionary['member_id'] = j.at[0,'member_id']
    dictionary1['tag_name'] = j.at[0,'tag_name']
    
    for i in j.index:
        anotherDict = {}
        anotherDict['detail_name'] = j.at[i,'detail_name']
        anotherDict['detail_value'] = j.at[i,'detail_value']
        dictList1.append(anotherDict.copy())
        dictionary1['detail'] = dictList1 
     
    dictList.append(dictionary1)
    dictionary['tag'] = dictList
    finalList.append(dictionary)

json.dumps(finalList,ensure_ascii = False)

detail.csv:

member_id, tag_name, detail_name, detail_value
-------------------------------------------------------
abc123, m1, Service_A, 20
abc123, m1, Service_B, 20
abc123, m2, Service_C, 10
xyz456, m3, Service A, 5

Desired Output JSON:

{   "member_id": "abc123",
    "tag"：[ {"tag_name": "m1",
            "detail":[{ "detail_name": "Service_A",
                        "detail_value": "20"},
                    {   "detail_name": "Service_B",
                        "detail_value": "20"}]},
            {"tag_name": "m2",
            "detail":[{ "detail_name": "Service_C",
                        "detail_value": "10"}]}]},
{   "member_id": "xyz456",
    "tag"：[{"tag_name": "m3",
            "detail":[{ "detail_name": "Service_A",
                        "detail_value": "5"}]}]}

Answer 1

I'm not aware of pandas functions allowing to do achieve that directly. Moreover, you introduce keys that are not part of your initial dataframe ( tag , detail ). So implementing a generic solution seems difficult.

However, if you don't have more columns than stated in your question, you can go through your dataframe, grouping column after column:

result = []

for member_id, member_df in df.groupby('member_id'):
    member_dict = {'member_id': member_id}
    member_dict['tag'] = []
    for tag_name, tag_df in member_df.groupby('tag_name'):
        tag_dict = {'tag_name': tag_name}
        tag_dict['detail'] = []
        for detail_name, detail_df in tag_df.groupby('detail_name'):
            detail_dict = {'detail_name': detail_name}
            detail_dict['detail_value'] = detail_df.detail_value.mean() # should be only one value, taking 'mean' just in case
            tag_dict['detail'].append(detail_dict)
        member_dict['tag'].append(tag_dict)
    result.append(member_dict)

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

Output:

[
    {
        "member_id": "abc123",
        "tag": [
            {
                "tag_name": "m1",
                "detail": [
                    {
                        "detail_name": "Service_A",
                        "detail_value": 20.0
                    },
                    {
                        "detail_name": "Service_B",
                        "detail_value": 20.0
                    }
                ]
            },
            {
                "tag_name": "m2",
                "detail": [
                    {
                        "detail_name": "Service_C",
                        "detail_value": 10.0
                    }
                ]
            }
        ]
    },
    {
        "member_id": "xyz456",
        "tag": [
            {
                "tag_name": "m3",
                "detail": [
                    {
                        "detail_name": "Service A",
                        "detail_value": 5.0
                    }
                ]
            }
        ]
    }
]