In the JSON, I have information within each main object, which includes sub-objects and lists of objects with more details.

    "ns0:sfobject": {
        "@xmlns:ns0": "urn:sfobject.sfapi.api.com",
        "ns0:id": "xxxx",
        "ns0:type": "table",
        "ns0:person": {
            "ns0:country_of_birth": "xxx",
            "ns0:date_of_birth": "xxx",
            "ns0:last_modified_by": "xxx",
            "ns0:last_modified_on": "xxx",
            "ns0:identity_information": {
                "ns0:account_uuid": "xxx",
                "ns0:created_on_timestamp": "xxx",
                "ns0:last_modified_on": "xxx"
            },
            "ns0:personal_information": [
                {
                    "ns0:created_by": "xxx",
                    "ns0:last_name": "xxx",
                    "ns0:nationality": "xxx",
                },
                {
                    "ns0:created_by": "yyy",
                    "ns0:last_name": "yyy",
                    "ns0:nationality": "yyy",
                },

            ],
            "ns0:address_information": [
                {
                    "ns0:address1": "xxx",
                    "ns0:country": "xxx",
                    "ns0:zip_code": "xxx"
                },
                {
                    "ns0:address1": "xxx",
                    "ns0:country": "xxx",
                    "ns0:zip_code": "xxx"
                },
                {
                    "ns0:address1": "xxx",
                    "ns0:country": "xxx",
                    "ns0:zip_code": "xxx"
                },
            ],
            "ns0:employment_information": [
                {
                    "ns0:assignment_class": "xxx",
                    "ns0:assignment_uuid": "xxx",
                    "ns0:user_id": "xxx",
                    "ns0:job_information": [
                        {
                            "ns0:business_unit": "xxx",
                            "ns0:calc_method_indicator": "xxx",
                            "ns0:company": "xxx"
                        },
                        {
                            "ns0:business_unit": "yyy",
                            "ns0:calc_method_indicator": "yyy",
                            "ns0:company": "yyy"
                        },
                        {
                            "ns0:business_unit": "zzz",
                            "ns0:calc_method_indicator": "zzz",
                            "ns0:company": "zzz"
                        },
                        {
                            "ns0:business_unit": "aaa",
                            "ns0:calc_method_indicator": "aaa",
                            "ns0:company": "aaa"
                        },
                    ],
                    "ns0:compensation_information": [
                        {
                            "ns0:created_by": "xxx",
                            "ns0:created_on_timestamp": "xxx",
                            "ns0:paycompensation_recurring": [
                                {
                                    "ns0:annualizationFactor": "xxx",
                                    "ns0:calculated_amount": "xxx"
                                },
                                {
                                    "ns0:annualizationFactor": "yyy",
                                    "ns0:calculated_amount": "yyy"
                                },
                                {
                                    "ns0:annualizationFactor": "zzz",
                                    "ns0:calculated_amount": "zzz"
                                }
                            ]
                        },
                        {
 
                            "ns0:created_by": "xxx",
                            "ns0:created_on_timestamp": "xxx",
                            "ns0:paycompensation_recurring": [
                                {
                                    "ns0:annualizationFactor": "xxx",
                                    "ns0:calculated_amount": "xxx"
                                },
                                {
                                    "ns0:annualizationFactor": "yyy",
                                    "ns0:calculated_amount": "yyy"
                                },
                                {
                                    "ns0:annualizationFactor": "zzz",
                                    "ns0:calculated_amount": "zzz"
                                }
                            ]
                        },
                    ],
                    "ns0:associated_employee_information": [
                        {
                            "ns0:country_of_birth": "xxx",
                            "ns0:created_by": "xxx",
                            "ns0:associated_employee_employment_information": {
                                "ns0:assignment_class": "xxx",
                                "ns0:user_id": "xxx"
                            }
                        },
                        {
                            "ns0:country_of_birth": "yyy",
                            "ns0:created_by": "yyy",
                            "ns0:associated_employee_employment_information": {
                                "ns0:assignment_class": "yyy",
                                "ns0:user_id": "yyy"
                            }
                        }
                    ]
                }

So what I want is a Python code that, in an automated way (i.e., by simply instructing it to search for objects and sub-objects), extracts multiple dataframes. This will allow me to later create a database. The expected result is:

Is it possible to reach it?

Thank you so much.

I have try this

import json
import pandas as pd

# Ruta al archivo JSON
json_file_path = r'json_rout'

# Cargar el JSON desde el archivo
with open(json_file_path, 'r') as file:
    data = json.load(file)

# Extraer datos
person_data = data["ns0:sfobject"]["ns0:person"]
personal_information = person_data["ns0:personal_information"]
address_information = person_data["ns0:address_information"]
phone_information = person_data["ns0:phone_information"]
email_information = person_data["ns0:email_information"]
employment_information = person_data.get("ns0:employment_information", [])
#job_information = person_data["ns0:job_information"]

# Convertir a DataFrames
person_df = pd.json_normalize(person_data)
personal_information_df = pd.DataFrame(personal_information)
address_information_df = pd.DataFrame(address_information)
phone_information_df = pd.json_normalize(phone_information)
email_information_df = pd.json_normalize(email_information)
employment_information_df = pd.json_normalize(employment_information)
#job_information_df = pd.json_normalize(job_information)

# Guardar los DataFrames en archivos CSV
person_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedataperson_data.csv', index=False)
personal_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedatapersonal_information.csv', index=False)
address_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedataaddress_information.csv', index=False)
phone_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedataphone_information.csv', index=False)
email_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedataemail_information.csv', index=False)
employment_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedataemployment_information.csv', index=False)
#job_information_df.to_csv(r'C:UsersMariaGironaOrtsPycharmProjectsvy-data-corporate-people-employeedatajob_information.csv', index=False)

But it does not work. And I expect different dataframes