5 Years Podcasting Python With Michael Kennedy: Growth, GIL, Async, and More
Sep 11, 2020
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Michael Kennedy, host of 'Talk Python to Me', reflects on 5 years of podcasting about Python, discussing growth, Async, GIL, desktop apps, type checking, and how podcasts aid language immersion. Python's appeal to new programmers, full-spectrum language usage, enterprise contributions, and stories from Talk Python are explored in depth.
Python's diverse applications, from medieval manuscripts to particle analysis, showcase its full-spectrum capabilities.
Subinterpreters offer a GIL-free parallel processing solution, revolutionizing Python's performance and scalability.
Async programming challenges in Python can be overcome with features like subinterpreters, unlocking efficient parallel processing capabilities.
Deep dives
Python's Diverse Applications: From Medieval Manuscripts to Particle Tracing
Python's versatility shines as seen from Cornelius Van Litt's use of computer vision on medieval manuscripts to identify timeframes based on folding patterns to the large Hadron Collider researchers using machine learning to analyze particle traces, showcasing the broad range of applications Python supports.
The Significance and Future of Subinterpreters in Python
Eric Snow's work on subinterpreters is anticipated to revolutionize the Python space by allowing multiple interpreters to run independently within a single Python process, enabling parallel processing without the constraints of the GIL, promising enhanced performance and scalability.
The Challenge of Async Programming and Python's Performance Optimization
Navigating async programming in Python presents a challenge due to the GIL's limitation on parallelism. However, advancements like subinterpreters offer a pathway to overcome this hurdle, allowing Python developers to leverage parallel processing capabilities efficiently and significantly enhance program performance.
Python's Growth and Potential Contributions from Enterprises
Python's growth in diverse sectors like enterprise usage and astronomy highlights its expanding influence. As enterprises increasingly adopt Python, there's a call for reciprocal support to the Python community, bridging the gap between organizational benefits and community contributions to elevate Python's development and sustainability for the future.
Benefits of using Async I/O in Python
Utilizing async and await features like in Python can significantly enhance performance by allowing other tasks to execute while waiting for callback functions instead of blocking calls. This asynchronous approach makes it easier to manage waiting times and scale efficiently. Performance comparisons often highlight scenarios where excessive strain is put on resources like databases, highlighting the importance of effectively leveraging async functionalities to optimize performance.
Enhancing Programming with Type Annotations and Fast API
Exploring the power of type annotations in Python reveals their value in enhancing code readability and improving documentation. Type annotations at function boundaries facilitate better understanding and utilization of functions, making it easier for developers to interact with libraries and APIs. Additionally, fast API offers a streamlined approach to building APIs by leveraging type annotations and model binding for efficient and professional code development.
Why is Python pulling in so many new programmers? Maybe some of that growth is from Python being a full-spectrum language. This week on the show we have Michael Kennedy, the host of the podcast “Talk Python to Me”. Michael reflects on five years of podcasting about Python, and many of the changes he has seen in the Python landscape.
We discuss several stories about the different ways Python is being used, and how that is drawing in many new programmers. Michael covers some potential Python stumbling blocks of Async, the Python Global Interpreter Lock (GIL), building desktop apps, and type checking. We also talk about how podcasts can act as a form of language immersion.
In this course, you’ll learn how to handle spreadsheets in Python using the openpyxl package. You’ll learn how to manipulate Excel spreadsheets, extract information from spreadsheets, create simple or more complex spreadsheets, including adding styles, charts, and so on.
Topics:
00:00:00 – Introduction
00:01:34 – Changes in the Python landscape over the last 5 years
00:08:09 – Changing the perspective from scripts to applications
00:10:48 – Python as a full-spectrum language
00:17:13 – What are the areas of growth for Python
00:22:09 – Stories highlights from Talk Python
00:27:48 – Stack Overflow Developer Surveys
00:33:22 – Enterprise use and contributions to Python
00:38:04 – Video Course Spotlight
00:39:20 – A monk learns Python and OpenCV
00:48:18 – Things that have been hard to do in Python
00:52:07 – How is the GIL part of the problem?
00:56:37 – Leave a review, it will help the show, Thanks!
00:57:04 – More on Async in Python
01:03:38 – Recent courses developed
01:10:05 – Who listens to a Python podcast?
01:13:06 – What are you excited about in the world of Python?
01:17:46 – What do you want to learn next?
01:23:17 – What is something you thought you knew about Python, but were wrong about it?
