Why Python keeps growing, explained [github.blog]

Pydantic vs Protobuf vs Namedtuples vs Dataclasses

Django: How to profile and improve startup time

Python in Visual Studio Code – March 2023 Release

Pytest tips and tricks

Набор советов по изучению и использованю pytest в тестировании

Как я добавил C-Style for-цикл в Python

История про ast

Build a CRUD Rest API in Python using Flask, SQLAlchemy, Postgres, Docker

How to migrate from Django’s PostgreSQL CI Fields to use a case-insensitive collation

Migrating from Flask to FastAPI, Part 2

Security 101: Securing file downloads

Python 3.11 and Nuitka Progress

Python⇒Speed: Python's multiprocessing performance problem

Securely Deploy a FastAPI App With NGINX and Gunicorn

13 tips and techniques for modern Flask apps

Three Python Trends in 2023

Python/Rust co-projects, web apps, and more typing

Migrating from Flask to FastAPI, Part 1

Python in Visual Studio Code – February 2023 Release

Python⇒Speed: Don't bother trying to estimate Pandas memory usage

You have a file with data you want to process with Pandas, and you want to make sure you won’t run out of memory. How do you estimate memory usage given the file size? At times you may see estimates like these: “Have 5 to 10 times as much RAM as the size of your dataset”, or “several times the size of your dataset”, or 2×-3× the size of the dataset. All of these estimates can both under- and over-estimate memory usage, depending on the situation. In fact, I will go so far as to say that estimating memory usage is just not worth doing. In particular, this article will: Demonstrate the very broad range of memory usage you will see just from loading the data, before any processing is done. Cover alternative approaches to estimation: measurement and streaming.

Python⇒Speed: float64 to float32: Saving memory without losing precision

Быстрые виртуальные машины: ускорение исполнения кода

Как работают интерпретаторы и что такое "виртуальная машина" в этом контексте. И как ускорить исполнение.