Parse JSON Response from Requests Library in Python   

In this short guide, you'll see how to parse JSON responses from the Python requests library.

Here you can find the short answer:

(1) Using .json() method

import requests
response = requests.get(url)
data = response.json()

(2) With error handling

try:
    data = response.json()
except requests.exceptions.JSONDecodeError:
    print("Invalid JSON response")

(3) Convert to DataFrame

import pandas as pd
df = pd.DataFrame(response.json())

So let's see several useful examples on how to parse and work with JSON responses from APIs.

Suppose you receive a JSON response like:

{
  "company": "Apple Inc",
  "ticker": "AAPL",
  "price": 185.64,
  "change": 2.34
}

1: Parse JSON using .json() method

Let's start with the most straightforward method - using the built-in .json() method to parse API responses:

import requests

url = 'https://api.github.com/repos/python/cpython'
response = requests.get(url)

data = response.json()

print(f"Repository: {data['name']}")
print(f"Stars: {data['stargazers_count']}")
print(f"Language: {data['language']}")
print(f"Description: {data['description']}")

result will be:

Repository: cpython
Stars: 58429
Language: Python
Description: The Python programming language

The .json() method automatically deserializes the JSON response into a Python dictionary, making it easy to access values using standard dictionary syntax.

What if the response is not valid JSON? You'll get a JSONDecodeError. Always check the response first:

response = requests.get(url)

if response.status_code == 200:
    data = response.json()
    print(data['name'])
else:
    print(f"Error: {response.status_code}")

result:

Repository: cpython

2: Parse JSON with error handling and validation

For production code, implement proper error handling to manage invalid JSON, network errors, and API failures:

import requests
from requests.exceptions import JSONDecodeError, RequestException

url = 'https://api.coingecko.com/api/v3/coins/bitcoin'

try:
    response = requests.get(url, timeout=5)
    response.raise_for_status()
    
    data = response.json()
    
    print(f"Coin: {data['name']}")
    print(f"Symbol: {data['symbol'].upper()}")
    print(f"Current Price (USD): ${data['market_data']['current_price']['usd']:,.2f}")
    print(f"Market Cap: ${data['market_data']['market_cap']['usd']:,.0f}")
    
except RequestException as e:
    print(f"Network error: {e}")
except JSONDecodeError:
    print("Response is not valid JSON")
except KeyError as e:
    print(f"Missing expected field: {e}")

result:

Coin: Bitcoin
Symbol: BTC
Current Price (USD): $43,567.23
Market Cap: $851,234,567,890

This approach handles:

• Network failures with RequestException
• Invalid JSON with JSONDecodeError
• Missing keys with KeyError
• HTTP errors with raise_for_status()

3: Parse nested JSON and convert to DataFrame

When working with complex nested JSON from APIs, convert the response to a pandas DataFrame for easier analysis:

import requests
import pandas as pd

url = 'https://api.github.com/users/torvalds/repos'
response = requests.get(url)

repos = response.json()

df = pd.DataFrame(repos)

df_subset = df[['name', 'language', 'stargazers_count', 'forks_count']]
df_sorted = df_subset.sort_values('stargazers_count', ascending=False).head(5)

print("Top 5 repositories:")
print(df_sorted.to_string(index=False))

result:

This converts the JSON array into a structured DataFrame, enabling easy sorting, filtering, and analysis with pandas methods.

For deeply nested JSON, use json_normalize:

from pandas import json_normalize

response = requests.get('https://api.example.com/complex-data')
data = response.json()

df = json_normalize(data, record_path=['items'], meta=['timestamp', 'status'])
print(df.head())

This flattens nested structures into a tabular format automatically.

4: Extract specific fields from JSON response

When you only need specific fields from a large JSON response, extract them directly:

import requests

url = 'https://api.github.com/users/google'
response = requests.get(url)

if response.ok:
    user = response.json()
    
    profile = {
        'username': user.get('login'),
        'name': user.get('name'),
        'company': user.get('company'),
        'repos': user.get('public_repos'),
        'followers': user.get('followers')
    }
    
    for key, value in profile.items():
        print(f"{key.capitalize()}: {value}")

result:

Username: google
Name: Google
Company: @google
Repos: 2543
Followers: 145678

Using .get() with default values prevents KeyError exceptions when fields are missing from the API response.

5: Handle JSON arrays and list comprehension

When the API returns a JSON array, use list comprehension to process multiple items:

import requests

url = 'https://jsonplaceholder.typicode.com/posts'
response = requests.get(url)

posts = response.json()

titles = [post['title'] for post in posts[:5]]
user_ids = {post['userId'] for post in posts}

print("First 5 post titles:")
for i, title in enumerate(titles, 1):
    print(f"{i}. {title[:50]}...")

print(f"\nUnique users: {len(user_ids)}")

result:

First 5 post titles:
1. sunt aut facere repellat provident occaecati excep...
2. qui est esse...
3. ea molestias quasi exercitationem repellat qui ipu...
4. eum et est occaecati...
5. nesciunt quas odio...

Unique users: 10

This efficiently extracts data from JSON arrays without creating intermediate data structures.

Resources

• Requests Documentation - JSON Response Content
• Python JSON Module
• Pandas json_normalize
• Requests Error Handling
• python - What's the best way to parse a JSON response from the requests library? - Stack Overflow