Собрали в одном месте самые важные ссылкиконсультируем про IT, Python
How much Python do you need to learn to start creating projects? What's a good balance of information and hands-on practice? This week on the show, Eric Matthes is here to discuss his book Python Crash Course.
This guide provides valuable insights and practical tips for new and experienced developers to leverage async programming in Django for non-blocking operations, improved scalability, and enhanced responsiveness.
Модуль для работы с многомерными массивами. Скачать можно по ссылке: https://pypi.python.org/pypi/numpy/
There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty print JSON. This article introduces you to what is available and how Simon discovered them.
Мощная система логгирования и платформа агрегации ошибок. Скачать можно по ссылке: https://pypi.python.org/pypi/sentry/
Фреймворк для простого создания интерфейсов командной строки.. Скачать можно по ссылке: https://pypi.python.org/pypi/click/
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Валидатор JSON данных. Скачать можно по ссылке: https://pypi.python.org/pypi/jsonschema
Библиотека работы с базами данных. Скачать можно по ссылке: https://pypi.python.org/pypi/SQLAlchemy/
Мощный и быстрый модуль для обработки XML/HTML. Скачать можно по ссылке: https://pypi.python.org/pypi/lxml/
Мощный web-фреймворк. Скачать можно по ссылке: https://pypi.python.org/pypi/Django/
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.