scrapy - 2.13.4

Гибкий фреймворк для написания web-пауков (парсеров). Скачать можно по ссылке: https://pypi.python.org/pypi/scrapy

numpy - 2.3.5

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

The Real Python Podcast – Episode #274: Preparing Data Science Projects for Production

How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, "Production Ready Data Science."

Python in Visual Studio Code – November 2025 Release

mcp-scanner - Scan MCP servers for potential threats & security findings

A Plan for 5-10%* Faster Free-Threaded JIT by Python 3.16

tiny8: A Tiny CPU Simulator Written in Python

pylint - 4.0.3

Статический анализатор Python-кода. Скачать можно по ссылке: https://pypi.python.org/pypi/pylint/

moneyflow: Personal Finance Data Interface for Power Users

Talk Python to Me: #527: MCP Servers for Python Devs

Audio

Python 3.14 Released and Other Python News for November 2025

Python 3.14 is officially out, Python 3.15 begins, and Python 3.9 reaches end of life. Plus, Django 6.0 first beta released, new PEPs, and more Python news.

django-cms - 5.0.5

Легкая в использовании и удобная для разработки CMS. Скачать можно по ссылке: https://pypi.python.org/pypi/django-cms/

pytest - 9.0.1

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

pymongo - 4.15.4

Python интерфейс для MongoDB. Скачать можно по ссылке: https://pypi.python.org/pypi/pymongo/

valuecell - multi-agent platform for financial applications.

How often does Python allocate?

10 Smart Performance Hacks For Faster Python Code

MarkItDown: Convert Documents Into LLM-Ready Markdown

The MarkItDown library lets you quickly turn PDFs, Office files, images, HTML, audio, and URLs into LLM-ready Markdown. In this tutorial, you’ll compare MarkItDown with Pandoc, run it from the command line, use it in Python code, and integrate conversions into AI-powered workflows.

pytogether: Collaborative Python IDE in the Browser

Regex, Pregex, or Pyparsing?

Parsing messy support tickets? This post walks through real-world examples of Python techniques for extracting structured data from unstructured text. It compares the re module for classic pattern matching, pregex for cleaner and more readable regex construction, and pyparsing for more complex structures.