Create normalized dataframe from a nested json

Question

I am trying to create a dataframe from nested json file but running into trouble.

[
    {
        "rec_id": "1",
        "user": {
            "id": "12414",
            "name": "Steve"
        },
        "addresses": [
            {
                "address_type": "Home",
                "street1": "100 Main St",
                "street2": null,
                "city": "Chicago"
            },
            {
                "address_type": "Work",
                "street1": "100 Main St",
                "street2": null,
                "city": "Chicago"
            }
        ],
        "timestamp": "2023-07-28T20:05:14.859000+00:00",
        "edited_timestamp": null
    },
    {
        "rec_id": "2",
        "user": {
            "id": "214521",
            "name": "Tim"
        },
        "addresses": [
            {
                "address_type": "Home",
                "street1": "100 Main St",
                "street2": null,
                "city": "Boston"
            },
            {
                "address_type": "Work",
                "street1": "100 Main St",
                "street2": null,
                "city": "Boston"
            }
        ],
        "timestamp": "2023-07-28T20:05:14.859000+00:00",
        "edited_timestamp": null
    },
    {
        "rec_id": "3",
        "user": {
            "id": "12121",
            "name": "Jack"
        },
        "addresses": [
            {
                "address_type": "Home",
                "street1": "100 Main St",
                "street2": null,
                "city": "Las Vegas"
            } ]
        "timestamp": "2023-07-28T20:05:14.859000+00:00",
        "edited_timestamp": null
    }
]

I tried below:

with open("employee.json") as file:
    data = json.load(file)  

data_df = pd.json_normalize(data)

data_df.columns.values.tolist()

['rec_id',
 'addresses',
 'timestamp',
 'edited_timestamp',
 'user.id',
 'user.name']

display(data_df)

    rec_id  addresses   timestamp   edited_timestamp    user.id user.name
0   1   [{'address_type': 'Home', 'street1': '100 Main St', 'street2': None, 'city': 'Chicago'}, {'address_type': 'Work', 'street1': '100 Main St', 'street2': None, 'city': 'Chicago'}]    2023-07-28T20:05:14.859000+00:00    None    12414   Steve
1   2   [{'address_type': 'Home', 'street1': '100 Main St', 'street2': None, 'city': 'Boston'}, {'address_type': 'Work', 'street1': '100 Main St', 'street2': None, 'city': 'Boston'}]  2023-07-28T20:05:14.859000+00:00    None    214521  Tim
2   3   [{'address_type': 'Home', 'street1': '100 Main St', 'street2': None, 'city': 'Las Vegas'}]  2023-07-28T20:05:14.859000+00:00    None    12121   Jack

How do I get the output as below -

rec_id  addresses.address_type  addresses.street1   addresses.street2   addresses.city  timestamp   edited_timestamp    user.id user.name
0   1   Home    100 Main St None    Chicago 2023-07-28T20:05:14.859000+00:00    None    12414   Steve
1   1   Work    100 Main St None    Chicago 2023-07-28T20:05:14.859000+00:00    None    12414   Steve
2   2   Home    100 Main St None    Boston  2023-07-28T20:05:14.859000+00:00    None    214521  Tim
3   2   Work    100 Main St None    Boston  2023-07-28T20:05:14.859000+00:00    None    214521  Tim
4   3   Home    100 Main St None    Las Vegas   2023-07-28T20:05:14.859000+00:00    None    12121   Jack

Answer 1

Try this, more about .json_normalize

df = pd.json_normalize(data, 
                       record_path='addresses', 
                       meta=['rec_id', ["user", "id"], ["user", "name"], 'timestamp', 'edited_timestamp'])

---

Output:

	address_type	street1	street2	city	rec_id	user.id	user.name	timestamp	edited_timestamp
0	Home	100 Main St		Chicago	1	12414	Steve	2023-07-28T20:05:14.859000+00:00
1	Work	100 Main St		Chicago	1	12414	Steve	2023-07-28T20:05:14.859000+00:00
2	Home	100 Main St		Boston	2	214521	Tim	2023-07-28T20:05:14.859000+00:00
3	Work	100 Main St		Boston	2	214521	Tim	2023-07-28T20:05:14.859000+00:00
4	Home	100 Main St		Las Vegas	3	12121	Jack	2023-07-28T20:05:14.859000+00:00