The Joy of Tinkering & Python Free-Threading Performance
Nov 22, 2024
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Christopher Trudeau, a regular contributor from PyCoder’s Weekly, shares his insights on keeping the spark alive in software development. He discusses the joy of tinkering with Python, emphasizing hands-on experimentation to sharpen skills. Trudeau dives into Python 3.13's free-threading performance, showcasing its behavior with large datasets. The conversation also highlights new Python library releases, innovative tools for concurrency challenges, and the significance of closures, wrapped up with thoughts on the balance between personal projects and professional growth.
Tinkering with Python through small projects fosters creativity and maintains enthusiasm in developers' coding skills and learning journey.
The performance analysis of Python 3.13's free threading features highlights both potential improvements and complexities in parallel processing and efficiency.
Deep dives
The Joy of Tinkering with Python
Tinkering with Python is crucial for keeping developer skills sharp and fostering a sense of enjoyment in programming. Engaging in small projects or experimenting with new frameworks allows developers to explore their creativity and problem-solving skills. Examples include creating toy projects or contributing to existing ones, which not only enhance knowledge but also maintain enthusiasm in learning Python. By continually building and learning through tinkering, developers can stay energized and innovative in their approach to coding.
Python 3.13 Performance and Free Threading
The latest performance analysis of Python 3.13 explores the impact of its free threading feature on parallel processing. A study using the PageRank algorithm highlighted the differences in efficiency between single-threaded, multi-threaded, and multi-processor versions of the algorithm, demonstrating that while the removal of the Global Interpreter Lock (GIL) introduced potential improvements, it also resulted in performance drops in certain scenarios. Specifically, the free threaded variant showed significant speed increases when executed under optimal conditions. These findings emphasize the complexity of performance tuning and the necessity for ongoing advancements in future Python versions.
Understanding Python Closures
The concept of closures in Python is integral to the language's functional programming capabilities, allowing for the retention of state between function calls. A closure occurs when an inner function accesses variables from its enclosing function, and the outer function returns this inner function. Examples such as creating factory functions demonstrate how closures can be leveraged to generate functions dynamically and maintain state. Understanding this can lead to more effective and reusable code practices in Python, including the implementation of decorators and managing state in GUI applications.
Redefining Cloud Infrastructure with StackNative
The contrast between traditional cloud-native applications and a new approach called StackNative emphasizes simplicity and efficiency in application infrastructure. The StackNative concept promotes building applications using essential tools without overwhelming complexity, aiming to avoid the pitfalls of high costs associated with extensive cloud service usage. For instance, it is possible to manage a Flask application with a minimal setup, cutting down expenses significantly compared to more elaborate cloud-native configurations. This approach enables developers to focus more on core functionalities while maintaining reliable performance at manageable costs.
What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss the joy of tinkering with Python as a way to keep your developer skills sharp. We dig into our techniques for continuing to learn and build projects.
Christopher shares an article that examines the performance of Python 3.13’s free-threading features. This piece uses a clever example to measure how the new features behave with large datasets and parallelization.
We share several other articles and projects from the Python community, including a group of new releases, common use cases and examples for Python closures, finding the opposite of cloud-native, Python’s soft keywords, a command-line utility for taking automated screenshots of websites, and putting the Django admin in the terminal with Textual.
In this step-by-step course, you’ll learn what inner functions are in Python, how to define them, and what their main use cases are. You’ll see how to write helper functions, create closure factory functions, and how to add behavior to existing functions with decorators.
Topics:
00:00:00 – Introduction
00:02:18 – Django Bugfix Release Issued: 5.1.3
00:02:46 – Pillow Release 11.0.0
00:03:14 – Flask Version 3.1.0
00:03:30 – PyCon US 2025 (Pittsburgh) Call for Proposals
00:03:46 – Python Closures: Common Use Cases and Examples
00:09:20 – State of Python 3.13 Performance: Free-Threading
00:15:42 – Sponsor: Windsurf
00:16:32 – Opposite of Cloud Native Is…?
00:22:36 – Python’s Soft Keywords
00:24:50 – Video Course Spotlight
00:26:11 – The Joy of Tinkering
00:38:33 – shot-scraper: A command-line utility for taking automated screenshots of websites
00:41:13 – django-admin-tui: Django Admin in the Terminal!
00:42:37 – django-admin-dracula: Dracula Themes for the Django Admin
Python Closures: Common Use Cases and Examples – In this tutorial, you’ll learn about Python closures. A closure is a function-like object with an extended scope. You can use closures to create decorators, factory functions, stateful functions, and more.
State of Python 3.13 Performance: Free-Threading – This article does a comparison between code in single threaded, threaded, and multi-process versions under Python 3.12, 3.13, and 3.13 free-threaded with the GIL on and off.
Opposite of Cloud Native Is…? – Michael (from Talk Python fame) introduces the concept of “stack-native” as the opposite of “cloud-native”, and how it applies to Python web apps. Building applications with just enough full-stack building blocks to run reliably with minimal complexity, rather than relying on a multitude of cloud services.
Python’s soft keywords – Python includes soft keywords: tokens that are important to the parser but can also be used as variable names. This article shows you what a soft keyword is and how to find them in Python 3.12 (both the easy and hard way).