We are given a nested JSON object and our task is to parse it in Python. In this article, we will discuss multiple ways to parse nested JSON in Python using built-in modules and libraries like json, recursion techniques and even pandas.

What is Nested JSON

Nested JSON refers to a JSON object that contains another JSON object (or an array of objects) inside it.

Example:

{
"name": "John",
"age": 30,
"address": {
"city": "New York",
"zipcode": "10001"
}
}

In the above example, address is a nested JSON object.

In this article, we will discuss multiple ways to parse nested JSON in Python. Let's discuss them one by one:

Using the JSON module

In this example, we use the json module to parse a nested JSON string. Subsequently, we access specific values within the JSON structure using dictionary keys, demonstrating how to retrieve information such as the name, age, city and zipcode.

import json

n_json = '{"name": "Prajjwal", "age": 23, "address": {"city": "Prayagraj", "zipcode": "20210"}}'

data = json.loads(n_json)

name = data['name']
age = data['age']
city = data['address']['city']
zipc = data['address']['zipcode']

print(f"Name: {name}")
print(f"Age: {age}")
print(f"City: {city}")
print(f"Zipcode: {zipc}")

import json

​

n_json = '{"name": "Prajjwal", "age": 23, "address": {"city": "Prayagraj", "zipcode": "20210"}}'

​

data = json.loads(n_json)

​

name = data['name']

age = data['age']

city = data['address']['city']

zipc = data['address']['zipcode']

​

print(f"Name: {name}")

print(f"Age: {age}")

print(f"City: {city}")

print(f"Zipcode: {zipc}")

Name: Prajjwal
Age: 23
City: Prayagraj
Zipcode: 20210

Explanation:

• json.loads() converts the JSON string into a Python dictionary.
• Nested values (like "city") are accessed using key chaining: data['address']['city'].
• This approach is ideal when the structure of JSON is fixed and known in advance.

Using Recursion

In this example, the parse_json function uses recursion to traverse the nested JSON structure and create a flattened dictionary. The parsed data is then accessed using keys to retrieve specific values such as name, age, city and zipcode from the original nested JSON data.

import json

n_json = '{"person": {"name": "Prajjwal", "age": 23, "address": {"city": "Prayagraj", "zipcode": "20210"}}}'

# Define a recursive function to parse nested JSON
def parse_json(data):
    result = {}
    for key, val in data.items():
        if isinstance(val, dict):
            result[key] = parse_json(val)  # Recursive call
        else:
            result[key] = val
    return result

data = parse_json(json.loads(n_json))

name = data['person']['name']
age = data['person']['age']
city = data['person']['address']['city']
zipc = data['person']['address']['zipcode']

print(f"Name: {name}")
print(f"Age: {age}")
print(f"City: {city}")
print(f"Zipcode: {zipc}")

import json

​

n_json = '{"person": {"name": "Prajjwal", "age": 23, "address": {"city": "Prayagraj", "zipcode": "20210"}}}'

​

# Define a recursive function to parse nested JSON

def parse_json(data):

    result = {}

    for key, val in data.items():

        if isinstance(val, dict):

            result[key] = parse_json(val)  # Recursive call

        else:

            result[key] = val

    return result

​

data = parse_json(json.loads(n_json))

​

name = data['person']['name']

age = data['person']['age']

city = data['person']['address']['city']

zipc = data['person']['address']['zipcode']

​

print(f"Name: {name}")

print(f"Age: {age}")

print(f"City: {city}")

print(f"Zipcode: {zipc}")

Name: Prajjwal
Age: 23
City: Prayagraj
Zipcode: 20210

Explanation:

• parse_json() function recursively navigates each level of the JSON structure.
• This method is helpful when we don't know how deep is the nesting.
• It builds a dictionary with the same nested structure, making it easier to access specific values later.

Using the Pandas library

In this example, the pd.json_normalize function from the Pandas library is utilized to flatten the nested JSON data into a Pandas DataFrame. The resulting DataFrame, df, allows easy access to specific columns such as 'name' and 'age.'

import pandas as pd
import json

n_json = '{"employees": [{"name": "Prajjwal", "age": 23}, {"name": "Kareena", "age": 22}]}'

data = json.loads(n_json)

df = pd.json_normalize(data, 'employees')

names = df['name']
ages = df['age']

print("Names:", list(names))
print("Ages:", list(ages))

import pandas as pd

import json

​

n_json = '{"employees": [{"name": "Prajjwal", "age": 23}, {"name": "Kareena", "age": 22}]}'

​

data = json.loads(n_json)

​

df = pd.json_normalize(data, 'employees')

​

names = df['name']

ages = df['age']

​

print("Names:", list(names))

print("Ages:", list(ages))

Names: ['Prajjwal', 'Kareena']
Ages: [23, 22]

Explanation:

• json.loads() first converts the JSON string to a Python object.
• pd.json_normalize() flattens the nested list into a table like structure.
• This is particularly useful for structured data like logs, records or API results with multiple entries.

Also read: Recursion, Python, Pandas, Json.