LEARN COMPLETE PYTHON IN 24 HOURS

14. Popular Libraries & Real-World Tools

14.1 requests – HTTP & APIs

requests is the simplest and most popular library for making HTTP requests — used by almost every Python developer.

Install

Bash

pip install requests

Basic GET request

Python

import requests response = requests.get("https://api.github.com/users/AnshumanKumar07") print(response.status_code) # 200 print(response.json()["name"]) # Your GitHub name print(response.json()["public_repos"]) # Number of public repos

POST with JSON payload

Python

payload = {"title": "My Post", "body": "Hello from Python", "userId": 1} headers = {"Content-Type": "application/json"} response = requests.post( "https://jsonplaceholder.typicode.com/posts", json=payload, # automatically serializes to JSON headers=headers ) print(response.status_code) # 201 Created print(response.json())

Advanced: Sessions, timeouts, authentication

Python

session = requests.Session() session.headers.update({"Authorization": "Bearer your_token"}) try: response = session.get("https://api.example.com/data", timeout=5) response.raise_for_status() # raises exception on 4xx/5xx except requests.exceptions.RequestException as e: print(f"Request failed: {e}")

Best practices (2026):

• Always use timeout=...

• Use response.raise_for_status()

• Prefer json= over data= for JSON

• Use Session() for repeated calls (reuses connections)

14.2 BeautifulSoup & Scrapy – Web Scraping

BeautifulSoup – Best for simple/static scraping Scrapy – Best for large-scale, dynamic, structured scraping

BeautifulSoup (with requests)

Python

pip install beautifulsoup4 lxml

Python

from bs4 import BeautifulSoup import requests url = "https://example.com" response = requests.get(url) soup = BeautifulSoup(response.text, "lxml") # or "html.parser" # Find elements title = soup.find("h1").text print("Page title:", title) # Find all links for link in soup.find_all("a", href=True): print(link["href"]) # CSS selector articles = soup.select("article.news-item") for article in articles: print(article.find("h2").text)

Scrapy (full framework)

Bash

pip install scrapy scrapy startproject my_scraper

Basic spider example

Python

# my_scraper/spiders/news.py import scrapy class NewsSpider(scrapy.Spider): name = "news" start_urls = ["https://news.ycombinator.com/"] def parse(self, response): for post in response.css("tr.athing"): yield { "title": post.css("span.titleline a::text").get(), "link": post.css("span.titleline a::attr(href)").get(), "points": post.xpath("following-sibling::tr/td/span[@class='score']/text()").get() } # Follow next page next_page = response.css("a.morelink::attr(href)").get() if next_page: yield response.follow(next_page, self.parse)

Run:

Bash

scrapy crawl news -o news.json

When to choose:

• Small/static site → BeautifulSoup + requests

• Large/dynamic site, need login/pagination/exports → Scrapy

Legal note: Always respect robots.txt and website terms — scraping public data is usually fine, but avoid aggressive crawling.

14.3 pandas & NumPy – Data Manipulation

NumPy – Foundation for numerical computing (arrays, math) pandas – Excel-like data frames + powerful analysis

Install

Bash

pip install numpy pandas

NumPy basics

Python

import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr * 2) # [ 2 4 6 8 10] print(arr.mean()) # 3.0 # 2D array (matrix) matrix = np.random.rand(3, 4) print(matrix.shape) # (3, 4) print(matrix.sum(axis=0)) # sum each column

pandas basics

Python

import pandas as pd # Read CSV/Excel df = pd.read_csv("sales.csv") # df = pd.read_excel("data.xlsx") print(df.head()) # first 5 rows print(df["revenue"].mean()) # average revenue # Filter & group high_sales = df[df["revenue"] > 10000] print(high_sales.groupby("region")["revenue"].sum()) # New column df["tax"] = df["revenue"] * 0.18 df.to_csv("processed_sales.csv", index=False)

Speed tip: Use vectorized operations — never loop over rows in pandas/NumPy.

14.4 Flask / FastAPI – Web Development

Flask – Lightweight, flexible micro-framework FastAPI – Modern, fast, async-first (most popular in 2026)

Flask example (simple API)

Python

from flask import Flask, jsonify, request app = Flask(__name__) @app.route("/api/greet", methods=["GET"]) def greet(): name = request.args.get("name", "World") return jsonify({"message": f"Hello, {name}!"}) if name == "__main__": app.run(debug=True)

FastAPI example (recommended 2026)

Python

# pip install fastapi uvicorn from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Item(BaseModel): name: str price: float @app.get("/greet") def greet(name: str = "World"): return {"message": f"Hello, {name}!"} @app.post("/items/") def create_item(item: Item): return {"item_name": item.name, "item_price": item.price}

Run:

Bash

uvicorn main:app --reload

Why FastAPI in 2026?

• Automatic OpenAPI docs (Swagger UI)

• Async support

• Type hints → auto-validation

• Very fast (Starlette + Pydantic)

14.5 SQLAlchemy / Django ORM – Databases

SQLAlchemy – Flexible, powerful ORM (used standalone or with FastAPI/Flask) Django ORM – Built into Django, very productive for full-stack apps

SQLAlchemy example (with SQLite)

Python

# pip install sqlalchemy from sqlalchemy import create_engine, Column, Integer, String from sqlalchemy.orm import declarative_base, sessionmaker Base = declarative_base() class User(Base): tablename = "users" id = Column(Integer, primary_key=True) name = Column(String) email = Column(String, unique=True) engine = create_engine("sqlite:///mydb.db") Base.metadata.create_all(engine) Session = sessionmaker(bind=engine) session = Session() # Add user new_user = User(name="Anshuman", email="anshuman@example.com") session.add(new_user) session.commit() # Query users = session.query(User).all() for user in users: print(user.name, user.email)

Django ORM (inside Django project)

Python

# models.py from django.db import models class Book(models.Model): title = models.CharField(max_length=200) author = models.CharField(max_length=100) published_date = models.DateField() # Usage in shell/views Book.objects.create(title="Python Mastery", author="Anshuman", published_date="2026-01-01") books = Book.objects.filter(author="Anshuman")

Comparison (2026):

• Need standalone ORM + async support → SQLAlchemy (with async engine)

• Building full web app with admin, auth → Django ORM

• FastAPI + SQLAlchemy = most modern stack

Mini Project – FastAPI + SQLAlchemy CRUD Create a simple REST API for a to-do list — combine requests, FastAPI, and SQLAlchemy.

This completes the full Popular Libraries & Real-World Tools section — now you have hands-on knowledge of the most important libraries used by Python developers in 2026!

📚 Amazon Book Library

All my books are FREE on Amazon Kindle Unlimited🌍 Exclusive Country-Wise Amazon Book Library – Only Here!

On GlobalCodeMaster.com you’ll find complete, ready-to-use lists of my books with direct Amazon links for every country.

Belong to India, Australia, USA, UK, Canada or any other country? Just click your country’s link and enjoy:

✅ Any eBook FREE on Kindle Unlimited ✅ Or buy at incredibly low prices

400+ fresh books written in 2025-2026 with today’s latest AI, Python, Machine Learning & tech trends – nowhere else will you find this complete country-wise collection on one platform!

Choose your country below and start reading instantly 🚀

BOOK LIBRARY USA 2026 LINK

BOOK LIBRARY INDIA 2026 LINK

BOOK LIBRARY AUSTRALIA 2026 LINK

BOOK LIBRARY CANADA 2026 LINK

BOOK LIBRARY UNITED KINGDOM 2026 LINK

BOOK LIBRARY GERMANY 2026 LINK

BOOK LIBRARY FRANCE 2026 LINK

BOOK LIBRARY ITALY 2026 LINK

BOOK LIBRARY SPAIN 2026 LINK

BOOK LIBRARY NETHERLANDS 2026 LINK

BOOK LIBRARY BRAZIL 2026 LINK

BOOK LIBRARY MEXICO 2026 LINK

BOOK LIBRARY JAPAN 2026 LINK

BOOK LIBRARY POLAND 2026 LINK

BOOK LIBRARY IRELAND 2026 LINK

BOOK LIBRARY SWEDEN 2026 LINK

BOOK LIBRARY BELGIUM 2026 LINK