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Anubhav Sharma

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The Anubhav portal was launched in March 2015 at the behest of the Hon'ble Prime Minister for retiring government officials to leave a record of their experiences while in Govt service .

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Ravi Vishwakarma

Ravi Vishwakarma

19-Nov-2025

Python makes JSON parsing easy using the built-in json module and modern HTTP libraries like requests.

Use requests + .json() (Most Common & Efficient)

import requests

response = requests.get("https://api.example.com/data")
data = response.json()

print(data["user"]["name"])

Why it’s efficient?

  • response.json() uses Python’s optimized JSON decoder internally.
  • No need to manually convert bytes → string → JSON.

Validate Success Before Parsing

import requests

r = requests.get("https://api.example.com/data")

if r.status_code == 200:
    data = r.json()
else:
    raise Exception("API failed: " + r.text)

Use json.loads() When You Already Have a JSON String

import json

json_string = '{"name": "John", "age": 30}'
data = json.loads(json_string)

print(data["name"])

Handle Large JSON Efficiently (Streaming)

For large datasets:

Option A: Stream using requests

import requests
import json

with requests.get("https://api.example.com/large", stream=True) as r:
    for line in r.iter_lines():
        if line:
            record = json.loads(line)
            print(record)

Option B: Use ijson (Incremental parser)

Best for HUGE files (100MB+)

import ijson

with open("big.json", "r") as f:
    for obj in ijson.items(f, "item"):
        print(obj)

Convert API Response to Python Dataclass (Clean & Efficient)

import requests
from dataclasses import dataclass

@dataclass
class User:
    id: int
    name: str

response = requests.get("https://api.example.com/user").json()
user = User(**response)

print(user.name)

Advantages:

  • Cleaner code
  • Type checking
  • IDE autocompletion

Use Pydantic for Faster + Validated Parsing (Production-Ready)

from pydantic import BaseModel
import requests

class User(BaseModel):
    id: int
    name: str

data = requests.get("https://api.example.com/user").json()
user = User(**data)

print(user.name)

Pydantic:

  • Automatically validates types
  • Much faster than default JSON parsing
  • Perfect for API-heavy applications

When Performance REALLY Matters (ujson)

import ujson

parsed = ujson.loads('{"a": 1, "b": 2}')

ujson is 2–4× faster than Python’s built-in json.

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