Grouping csv by name

[terrydidi]

I'm converting the csv format to json format. Now I generated the json structure but I need to group the data by name.

import csv

result = []
with open("student.csv", "r") as csv_ledger:
    for row in csv.DictReader(csv_ledger, skipinitialspace=True):
        result.append({
            "name": row["name"],
            "email": row["email"],
            "items": [{
                "phone": row["phone"],
                "info": {"date": row["date"]},
            }],
        })

[Larz60+]

please post csv file

[terrydidi]

Thanks for reply.

CSV file

name,email,date,phone
john,example.com,26/11/18,123
john,hello.com,12/08/18,123456

[Larz60+]

if you use name for key, there will be a duplicate key conflict since two rows have john as name

[terrydidi]

Thanks for reply

updated CSV file

name,email,date,phone
john,example.com,26/11/18,123
johnny,hello.com,12/08/18,123456

I'm using the defaultdict

import csv
from collections import defaultdict

result2 = defaultdict(list)
result = []
with open("student.csv", "r") as csv_ledger:
    for row in csv.DictReader(csv_ledger, skipinitialspace=True):
        result.append({
            "name": row["name"],
            "email": row["email"],
            "items": [{
                "phone": row["phone"],
                "info": {"date": row["date"]},
            }],
        })
     result2[row['name']].append(row['phone'])<---append whole items list
     result2[row['name'],row['email']].append(row['items'])<---not support

The result above is like

"john": [
        "123"
    ]

[Larz60+]

but the second csv record with name john will overwrite the first.

import csv
import os
 
# I need following for my virtual environment, you can remove if
# running python from same directory as csv file.
os.chdir(os.path.abspath(os.path.dirname(__file__)))

result = []

plain_dict = {}
# To get flat normal dict, can just use dict(ordered_dict)
# but to get name as key:
with open("student.csv", "r") as csv_ledger:
    name = None
    reader = csv.DictReader(csv_ledger)
    for row in csv.DictReader(csv_ledger, skipinitialspace=True):
        for k, v in row.items():
            if k == 'name':
                name = v
                plain_dict[name] = {}
            else:
                plain_dict[name][k] = v
    print(plain_dict)

and since there are two csv rows with sane name, second overwrites first.
result:
{'john': {'email': 'hello.com', 'date': '12/08/18', 'phone': '123456'}}
but can be saved with json.dump

code again with json dump and reread json:

import csv
import json
import os
 
# I need following for my virtual environment, you can remove if
# running python from same directory as csv file.
os.chdir(os.path.abspath(os.path.dirname(__file__)))

result = []

plain_dict = {}
# To get flat normal dict, can just use dict(ordered_dict)
# but to get name as key:
with open("student.csv", "r") as csv_ledger:
    name = None
    reader = csv.DictReader(csv_ledger)
    for row in csv.DictReader(csv_ledger, skipinitialspace=True):
        for k, v in row.items():
            if k == 'name':
                name = v
                plain_dict[name] = {}
            else:
                plain_dict[name][k] = v

    print(f'plain_dict: {plain_dict}')
    with open('student.json', 'w') as jp:
        json.dump(plain_dict, jp)
    
    # reload json to make sure ok
    with open('student.json') as jp:
        new_dict = json.load(jp)
    print(f'new_dict: {new_dict}')

output:

Output:
plain_dict: {'john': {'email': 'hello.com', 'date': '12/08/18', 'phone': '123456'}}
new_dict: {'john': {'email': 'hello.com', 'date': '12/08/18', 'phone': '123456'}

[terrydidi]

Thanks for the reply.

My expected result is like

  {
    "name": "john",
    "email": "example.com",
    "items": [
      {
        "phone": "123",
        "info": {
             "date": "26/11/18",
        }
      },
      {
        "phone": "123456",
        "info": {
             "date": "12/08/18",
        }
      },
    ]
  }

The other value will insert according to the name. I will try to refer your method.
Btw, based on my first code will not group yet, is it possible to transform the top-level array [] to object {}?Or just remove the top-level [].

[scidam]

If this isn't an assignment, I would suggest to use Pandas. Pandas has a lot of useful tool
to handle such problems, look at io submodule for i/o operations and groupby.
If you used Pandas, your code would be significantly shorter.

[perfringo]

According to initial data sample there could be same name and different mail addresses:

Output:
name,email,date,phone
john,example.com,26/11/18,123
john,hello.com,12/08/18,123456

If this is actual case then you should build data structure which supports multiple e-mail addresses under one name.