Bookmarks (449)

Pyrefly vs. ty: Comparing Python’s New Type Checkers A deep dive on Python’s two newest, Rust-based type checkers. This post includes both performance and capability comparisons. EDWARD LI

Journey Into Real Python Amanda has recently written her first article for Real Python and this post talks about her experience doing so. If you want to check out the article, it is on Nested Loops. AMANDA ENE ADOYI

Ways to Optimize Your Code in Python Learn four ways to optimize your Python project and improve performance, including choosing the right data structures, variable scopes, list comprehensions and generators, and how to leverage built-in libraries. FEDERICO TROTTA • Shared by AppSignal

DumPy: NumPy Except It’s OK if You’re Dum In a follow-up to the article I Don’t Like NumPy, this post proposes a way of writing code that looks like loops but use vector operations instead. DYNOMIGHT.NET

How to Split Up a Django Monolith Without Microservices Facing too much complexity in your Django application as a developer? This articles introduces django-queuebie, a synchronous message queue working with commands and events. RONNY VEDRILLA • Shared by Ronny Vedrilla

Control Flow Structures in Python Take control of your code with Python control flow structures. You’ll learn with real examples using loops, conditionals, try-except blocks, and pattern matching. REAL PYTHON

Python pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data. JOAB JACKSON

Writing Portable DataFrame Code for Lazy Execution Learn one simple concept, and you can rid your code of row-ordering assumptions - it will then work seamlessly across eager and lazy DataFrames MARCO GORELLI • Shared by Marco Gorelli

Marimo: A Reactive, Reproducible Notebook Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks. REAL PYTHON

Another Great PyCon A post about the highlights of PyCon US 2025 from one of the volunteers. GEORGI KER

NumPy Array Type Hints Thanks to improvements in NumPy typing support, NumPy generic arrays can now type both shape and dtype, offering opportunities for improved static analysis and run-time validation. CHRISTOPHER ARIZA • Shared by Christopher Ariza

The LEGB Rule & Understanding Python Scope In this step-by-step video course, you’ll learn what scopes are, how they work, and how to use them effectively to avoid name collisions in your code. REAL PYTHON course

Prevent Postgres Slowdowns on Python Apps with this Check List Avoid performance regressions in your Python app by staying on top of Postgres maintenance. This monthly check list outlines what to monitor, how to catch slow queries early, and ways to ensure indexes, autovacuum, and I/O are performing as expected → PGANALYZE sponsor

ty: Astral’s New Type Checker (Formerly Red-Knot) The folks over at Astral are back with another amazing project: ty, formerly known as Red-Knot, it is a new type checker. Talk Python interviews Charlie Marsh and Carl Meyer about this new tool. KENNEDY ET AL podcast

First Steps With LangChain Large language models (LLMs) have taken the world by storm. In this step-by-step video course, you’ll learn to use the LangChain library to build LLM-assisted applications. REAL PYTHON course

Understanding Random Forest Using Python A Random Forest is a powerful machine learning algorithm that can be used for classification and regression, is interpretable, and doesn’t require feature scaling. Here’s how to apply it with scikit-learn. MICHAEL GALARNYK • Shared by Michael Galarnyk

Understanding Python Web Deployment Trying to deploy server-side Python web applications may be overwhelming. This tutorial breaks down the why of things, rather than the what, and makes recommendations on how to approach the problem. MIREK DŁUGOSZ

Meta Announces Pyrefly Pyrefly is an open souce Python type checker and IDE extension built in Rust. This post announces its availability, tells you why they built it, and how you can use it. META

How to Group Data Using Polars .group_by() Start using Polars .group_by() to make sense of your data. This tutorial shows you how to group, aggregate, and reveal hidden insights with hands-on examples. REAL PYTHON

Nested Loops in Python Learn how to use nested loops in Python to iterate over multiple sequences and perform repeated actions efficiently in your programs. REAL PYTHON

Machine Learning With DuckDB and scikit-learn Learn how to prototype a machine learning workflow using DuckDB for data handling and scikit-learn for modeling. PETRICA LEUCA

Python: The Documentary There’s a new documentary in the works on Python and its popularity. This is the official trailer. YOUTUBE.COM video

The Guide to Hashing I Wish I Had When I Started Learn the basics of hashing in this beginner-friendly guide. Discover what hashing is, how it works, its key principles, common algorithms, and practical uses for password storage and file integrity. (Code examples aren’t in Python, but most of the explanation is in text). ANTON ÖDMAN

Loading Pydantic Models From JSON Pydantic’s JSON loading uses a huge amount of memory; here’s how to reduce it. ITAMAR TURNER-TRAURING

Narwhals: Unified DataFrame Functions Narwhals is a lightweight compatibility layer between DataFrame libraries. You can use it as a common interface to write reproducible and maintainable data science code which supports pandas, Polars, DuckDB, PySpark, PyArrow, and more CODECUT.AI • Shared by Marco Gorelli