Python⇒Speed: When NumPy is too slow

If you’re doing numeric calculations, NumPy is a lot faster than than plain Python—but sometimes that’s not enough. What should you do when your NumPy-based code is too slow? Your first thought might be parallelism, but that should probably be the last thing you consider. There are many speedups you can do before parallelism becomes helpful, from algorithmic improvements to working around NumPy’s architectural limitations. Let’s see why NumPy can be slow, and then some solutions to help speed up your code even more.

djangp-waffle - Feature Flags for Django

Bullet Proofing Django Models

Рекомендаци по составлению моделей в DJango

Talk Python to Me: #420: Database Consistency & Isolation for Python Devs

Audio

django-two-factor-auth - 1.15.3

Полноценная двухфакторная аутентификации для Django.. Скачать можно по ссылке: https://pypi.python.org/pypi/django-two-factor-auth/

Stack Abuse: Guide to Interfaces in Python

How to Flatten a List of Lists in Python

Или посмотреть на funcy библиотеку

numpy - 1.24.4

Модуль для работы с многомерными массивами. Скачать можно по ссылке: https://pypi.python.org/pypi/numpy/

redis - 4.6.0

Python клиент для Redis. Скачать можно по ссылке: https://pypi.python.org/pypi/redis/

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The Real Python Podcast – Episode #161: Resources and Advice for Building CircuitPython Projects

OpenBuddy - Open Multilingual Chatbot for Everyone

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Интеграция CkEditor в админ панель Django. Скачать можно по ссылке: https://pypi.python.org/pypi/django-ckeditor/

pytest - 7.4.0

Простой мощный инструмент тестирования в Python. Скачать можно по ссылке: https://pypi.python.org/pypi/pytest/

css-inline - High-performance library for inlining CSS into HTML 'style' attributes

pymg - A Better Display for Stack Traces

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Dependency Tracking for Python Function Calls

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Faster Python 3.13 Plan

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