What are the steps to get started building a FastAPI application? What are the different types of concurrency available in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent Real Python step-by-step tutorial about programming a FastAPI example application. You practice installing FastAPI, building your first endpoints, adding path and query parameters, and validating endpoints using Pydantic.

Christopher covers updates to his Real Python video course about concurrency in Python. The course digs into what concurrency means in Python and why you might want to incorporate it in your code. He describes the different methods and demonstrates how to approach coding using threading, asyncio, and multiprocessing.

We also share several other articles and projects from the Python community, including a news roundup, the PSF fundraiser campaign for 2025, where Python stores attributes, performance hacks for faster Python code, a project to transform functions into a web interface, and a Python disk-backed cache.

Course Spotlight: Python Descriptors

Learn what Python descriptors are, how the descriptor protocol works, and when descriptors are useful, with practical, hands-on examples.

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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.”

Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production.

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What are techniques for writing maintainable Python code? How do you make your Python more readable and easier to refactor? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article about writing code that is easy to maintain. We cover writing comments, creating meaningful names, avoiding magic numbers, and preparing code for your future self.

We also share several other articles and projects from the Python community, including release news, modifying the REPL, differences between Polars and pandas, generating realistic test data in Python, investigating quasars with Polars and marimo, creating simple meta tags for Django objects, and a GUI toolkit for grids of buttons.

Course Spotlight: Modern Python Linting With Ruff

Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular.

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How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, “Talk Python in Production.”

Michael runs multiple Python applications online, including a training site, blog, and two podcasts. While searching for the best solution for hosting his business, he documented his findings in a book. We talk about containerizing Python applications, generating static sites, preparing for traffic spikes, and avoiding cloud service lock-in.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

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How does Python 3.14 perform under a few hand-crafted benchmarks? Does the performance of asyncio scale on the free-threaded build? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

At the top of the show, we have a mountain of release news and Python Enhancement Proposals to cover. Then, we dig into a couple of articles covering the performance of Python 3.14. The first is a benchmarking comparison of the last several Python versions and their variations, including JIT and free-threaded mode. The second explores the changes in 3.14 that enable asyncio to scale on CPython’s free-threaded build.

We also share several other articles and projects from the Python community, including an introduction to NiceGUI, a free-threaded Python library compatibility checker, an exploration of what is “good taste” in software engineering, HTML templating with t‑strings, and a tool for testing Sphinx docs instantly in the browser.

Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs

In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read the Docs to automatically build and publish your code documentation.

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How is teaching young students Python changing with the advent of LLMs? This week on the show, Kelly Schuster-Paredes from the Teaching Python podcast joins us to discuss coding and AI in the classroom.

Kelly shares her current thoughts on teaching Python to young students. She stresses that the earliest classes still need to cover the fundamentals. We also discuss how coding instruction is evolving toward reading and reviewing code more than writing it.

Course Spotlight: Building a Python GUI Application With Tkinter

In this video course, you’ll learn the basics of GUI programming with Tkinter, the de facto Python GUI framework. Master GUI programming concepts such as widgets, geometry managers, and event handlers. Then, put it all together by building two applications: a temperature converter and a text editor.

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Python 3.14 is here! Christopher Trudeau returns to discuss the new version with Real Python team member Bartosz Zaczyński. This year, Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.14: Cool New Features for You to Try.” Christopher’s video course, “What’s New in Python 3.14”, covers the topics from the article and shows the new features in action.

Christopher and Bartosz dug into the new release to create code examples showcasing the new features for the tutorial and course. We look at the enhanced and more colorful REPL experience, better error messages, safer hooks for live debugging, and deferred annotation evaluation. We also discuss template strings, Zstandard compression, and multiple performance improvements.

We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.14.

Course Spotlight: What’s New in Python 3.14

Covers Python 3.14’s key changes: free-threading, subinterpreters, t-strings, lazy annotations, new REPL features, and improved error messages.

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What’s changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python.

We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions.

Stephen shares details about our upcoming eight-week live course, Python for Beginners: Code With Confidence. Check out realpython.com/live to learn more and reserve your spot.

Course Spotlight: 11 Beginner Tips for Learning Python

In this course, you’ll see several learning strategies and tips that will help you jumpstart your journey towards becoming a successful Python programmer.

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What’s a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article exploring the use of feature flags. The piece advises targeting specific users, storing schemas, and relying on well-worn code paths. He also discusses the danger of the exponential growth of functionality tests as feature flags are added.

We dig into a comparison of six popular Python visualization libraries. The article provides code examples along with their respective visual output to highlight features and make selecting the optimal library for your work easier.

We also share several other articles and projects from the Python community, including a news roundup, benchmarking MicroPython, simplifying IPs and networks in Python, a “scream” cipher, and a browser-based graphical viewer for the output of Python’s cProfile module.

This episode is sponsored by InfluxData.

Course Spotlight: Exploring Astrophysics in Python With pandas and Matplotlib

This course uses three problems often covered in introductory astrophysics courses to explore in Python. Along the way, you’ll learn some astronomy and how to use a variety of data science libraries like NumPy, Matplotlib, pandas, and pint.

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When converting an existing REST API to the Model Context Protocol, what should you consider? What anti-patterns should you avoid to keep an AI agent’s context clean? This week on the show, Kyle Stratis returns to discuss his upcoming book, “AI Agents with MCP”.

Kyle has been busy since he last appeared on the show in 2020. He’s taken his experience working in machine learning startups and started his own consultancy, Stratis Data Labs. He’s been documenting his explorations working with LLMs and MCP on his blog, The Signal Path.

Kyle is also writing a book about building MCP clients, services, and end-to-end agents. We discuss a recent article he wrote about the hazards of using an automated tool to convert a REST API into an MCP server. He shares his personal experiences with building MCP tools and provides additional resources for you to learn more about the topic.

This episode is sponsored by InfluxData.

Spotlight: Python for Beginners: Code With Confidence – Real Python

Learn Programming Fundamentals and Pythonic Coding in Eight Weeks—With a Structured Course

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