python

14 Advanced Python Features

Summary of Advanced Python Features Article:

Edward Li's blog lists 14 advanced Python features that enhance coding efficiency and improve type safety across various use cases. Highlights include:

  1. Typing Overloads: Use decorators for multiple function signatures based on input types.
  2. Keyword-only and Positional-only Arguments: Control how arguments can be passed to functions.
  3. Future Annotations: Allows delayed evaluation of type annotations for cleaner code.
  4. Generics: Introduced a new syntax for generics in Python 3.12, enhancing type safety for collections.
  5. Protocols: Facilitate duck typing by defining expected behaviors without inheritance.
  6. Context Managers: Simplify resource management with the with statement.
  7. Structural Pattern Matching: An advanced method of branching logic introduced in Python 3.10.
  8. Python Slots: Speed up attribute access by defining fixed attributes for class instances.
  9. Python Nitpicks: Useful syntax tips like for-else and walrus operator for cleaner code.
  10. Advanced f-string Formatting: Expand usage of f-strings for enhanced string interpolation.
  11. Cache (@cache): Efficiently improve performance with function memoization.
  12. Python Futures: Manage asynchronous tasks, akin to JavaScript Promises.

These features aim to unlock Python's full potential, providing developers with tools for cleaner, more efficient code.

https://blog.edward-li.com/tech/advanced-python-features/

Regex Affordances

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Python regex offers powerful text manipulation features without requiring complex matching. The “substitute_variables” function in Coverage.py demonstrates how to expand environment variables in configuration using a straightforward regex. Key regex features include verbose syntax for clarity, named groups for retrieval, and the ability to use functions for matches in replacements—enhancing readability and maintainability in code. Regex can be powerful and manageable when applied correctly.

https://nedbatchelder.com/blog/202504/regex_affordances.html

Ten Python Datetime Pitfalls, and What Libraries Are (not) Doing About It

Summary: Python's datetime library has several pitfalls, including issues with naïve vs. aware datetimes, DST handling, ambiguities, and equality comparisons. Third-party libraries like Arrow, Pendulum, DateType, and Heliclockter address some of these but not all. A new library has been developed to provide distinct classes for various datetime use cases, improve DST handling, and avoid silent failures caused by non-existent or ambiguous date-times.

https://dev.arie.bovenberg.net/blog/python-datetime-pitfalls/

Preface

TLDR: The book aims to teach Python developers architectural patterns for managing complexity in software development, emphasizing Test-Driven Development (TDD), Domain-Driven Design (DDD), and event-driven architecture. Authors Harry and Bob, experienced in building applications for an ecommerce company, use practical examples to demonstrate these concepts, focusing on maintaining clean code architecture while solving real-world business problems. The book encourages hands-on coding, structured around a single project, using various Python frameworks and aiming to offer insights applicable to both monolithic and microservices architectures.

https://www.cosmicpython.com/book/preface.html

GitHub – PragmaticMachineLearning/probly

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Probly is an AI-powered spreadsheet application that integrates spreadsheet functions with Python data analysis and visualization. It uses a modern architecture with a Next.js frontend and Pyodide for Python execution in the browser. Users can get started quickly with Docker or install manually, setting up an OpenAI API key for advanced features. Key functionalities include intelligent suggestions, local data analysis, and interactive charts, suitable for various applications.

https://github.com/PragmaticMachineLearning/probly

The Xonsh Shell — Python-powered Shell.

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Xonsh is a modern, cross-platform shell that combines Python 3.6+ with shell commands, allowing seamless integration of both. It offers features like shell command combining, extensions (xontribs), customizable prompts, and extensive installation methods (PyPi, Conda, AppImage, Docker). It’s suitable for users of all skill levels and supports major operating systems like Linux, OSX, and Windows.

https://xon.sh/

Scripton Python IDE

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Scripton Python IDE Overview:
Scripton is a Python IDE for macOS that allows real-time visualizations, integrated plotting, and built-in graphics toolkits. It enables dynamic visualizations directly from Python scripts without needing external tools, using libraries like Plotly and Observable Plot. The IDE features an advanced REPL for code editing, debugging tools, and support for various libraries such as Matplotlib, PyTorch, and OpenCV. It aims to cater to both scientific and creative applications, supporting Python versions 3.8 and above. Currently compatible with macOS, Windows and Linux support is forthcoming.

https://scripton.dev/

DeepSeek AI Tools Impersonated by Infostealer Malware on PyPI

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DeepSeek AI tools were impersonated by infostealer malware on PyPI. Two malicious packages, “deepseeek” and “deepseekai,” were discovered, which stole sensitive data from developers who downloaded them. The malware, uploaded from an inactive account, exfiltrated user credentials to a command and control server. Despite being reported and taken down quickly, 222 developers downloaded the packages, mostly from the U.S. Affected users are urged to change their API keys and credentials to prevent further compromise.

https://www.bleepingcomputer.com/news/security/deepseek-ai-tools-impersonated-by-infostealer-malware-on-pypi/

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