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Python Guides & Tutorials
Learn Python the right way — syntax, data structures, OOP, libraries, APIs and project ideas that build a hire-ready portfolio.
Python articles
SQLAlchemy Tutorial: Databases in Python
A hands-on SQLAlchemy 2.0 tutorial on an in-memory SQLite database: defining typed models, adding and committing rows with a Session, writing select() queries, joining and aggregating, loading relationships without the N+1 query problem, and handling transactions and schema migrations.
Top Python Libraries Every Developer Should Know
A map of the Python libraries that come up again and again in real projects, grouped by job: standard-library modules to learn first, then HTTP clients, web frameworks, data and machine learning, validation, databases, testing, command-line tools and code-quality tooling, and how to choose.
Web Scraping with Python: BeautifulSoup and Requests
A practical, responsible introduction to web scraping in Python: fetching pages with Requests and a proper session, parsing HTML with Beautiful Soup and CSS selectors, extracting tables and following pagination, checking robots.txt, rate-limiting, and recognising when an API or a browser tool is the better option.
Working with JSON in Python: A Practical Guide
A practical guide to JSON in Python with the json module: how Python types map to JSON and what does not survive a round trip, reading and writing files, pretty and compact output, serialising dates and Decimals, keeping money exact when parsing, understanding decode errors, JSON Lines for large data, and the NaN trap.
Virtual Environments in Python: venv, pip and Poetry
Why every Python project needs its own environment, and how to manage one: creating and activating a venv, installing and pinning packages with pip, reproducing an environment exactly from a requirements file, the difference between direct and locked dependencies, and when a tool such as Poetry or uv is worth adopting.
Unit Testing in Python with pytest: A Beginner's Guide
A beginner's guide to pytest built on a small real module: writing tests as plain functions with assert, reading pytest's failure reports, parametrising edge cases, testing that errors are raised, and using fixtures, tmp_path and monkeypatch so tests never touch real files or settings.