The Real Python Podcast

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The Real Python Podcast

Real Python

A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community. The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what's new in the world of Python programming and become a more effective Pythonista.

57 minutes 3 seconds

Behavior-Driven vs Test-Driven Development & Using Regex in Python
What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

In this episode, we expand on our software testing discussion from two weeks ago by adding behavior-driven development concepts. Christopher describes how BDD correlates with test-driven development and how it fosters collaboration within a team. We discuss building acceptance tests written in plain language and a handy tool for creating them.

We also share several other articles and projects from the Python community, including a news roundup, using regular expressions in Python, dealing with missing data in Polars, monkey patching in Django, first steps with Playwright, 3D printing giant things with a Python jigsaw generator, and a query language for JSON.

This episode is sponsored by Postman.

Course Spotlight: Regular Expressions and Building Regexes in Python

In this course, you’ll learn how to perform more complex string pattern matching using regular expressions, or regexes, in Python. You’ll also explore more advanced regex tools and techniques that are available in Python.

Topics:
- 00:00:00 – Introduction
- 00:02:21 – PyOhio 2025 July 26-27, 2025 Announced
- 00:02:38 – Python 3.13.2 and 3.12.9 now available!
- 00:02:52 – Django bugfix releases issued: 5.1.6, 5.0.12, and 4.2.19
- 00:03:04 – DjangoCon Europe 2025 - Real Python Podcast
- 00:05:24 – How to Deal With Missing Data in Polars
- 00:10:29 – Monkey Patching Django
- 00:15:50 – Sponsor: Postman
- 00:16:42 – My First Steps With Playwright
- 00:20:48 – How to Use Regular Expressions in Python
- 00:25:55 – Video Course Spotlight
- 00:27:25 – TDD vs. BDD: What’s the Difference?
- 00:50:13 – 3D Printing Giant Things With a Python Jigsaw Generator
- 00:53:58 – jmespath.py: Query Language for JSON
- 00:55:58 – Thanks and goodbye
News:
Topics:
- How to Deal With Missing Data in Polars – In this tutorial, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them.
- Monkey Patching Django – The nanodjango project is a modification to the Django framework that lets you get started with a single file instead of the usual cookie-cutter directory structure. This is a detailed post explaining how nanodjango monkey patches Django to achieve this result.
- Fake Django Objects With Factory Boy – The
- My First Steps With Playwright – Playwright is a browser-based automation tool that can be used for web scraping or testing. This intro article shows you how to use the Python interface to access a page including using cookies.
- How to Use Regular Expressions in Python – This post explores the basics of regular expressions in Python, as well as more advanced techniques. It includes real-world use cases and performance optimization strategies.
Discussion:
- TDD vs. BDD: What’s the Difference? – Discover the key differences between TDD vs BDD, their workflows, tools, and best practices for developers.
- Cucumber
Projects:
- 3D Printing Giant Things With a Python Jigsaw Generator – This is a long, detailed article on 3D printing objects too large for the printer bed. The author has created dovetail joints to assemble pieces together. He wrote a Python program to automatically split up the larger model files into the jigsaw pieces needed to build a final result.
- jmespath.py: Query Language for JSON
Additional Links:
- Polars — DataFrames for the new era
- nanodjango: Full Django in a single file - views, models, API ,with async support. Automatically convert it to a full project.
- factory_boy library is a tool for managing fixtures for your tests. This article shows you how to use it with Django.
- trimesh 4.6.2 documentation
- Email::RFC822::Address - Regex Recipe
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14 February 2025, 12:00 pm
1 hour 30 minutes

Charlie Marsh: Accelerating Python Tooling With Ruff and uv
Are you looking for fast tools to lint your code and manage your projects? How is the Rust programming language being used to speed up Python tools? This week on the show, we speak with Charlie Marsh about his company, Astral, and their tools, uv and Ruff.

Charlie started working on Ruff as a proof of concept, stating that Python tooling could be much faster. He had seen similar gains in JavaScript tools written in Rust. The project started as a speedy linter with a small ruleset. It’s grown to include code formatting and over 800 built-in linting rules.

Last year, the team at Astral started working on a Python package and project manager written in Rust. As a single tool, uv can replace pip, pip-tools, pipx, poetry, pyenv, and more. We discuss how uv can install and manage versions of Python and run scripts without thinking about virtual environments or dependencies.

Charlie talks about growing the team at Astral over the past couple of years. We also discuss the funding model Astral has adopted and sustaining open-source software.

This episode is sponsored by Postman.

Course Spotlight: Python Basics: Installing Packages With pip

Python’s standard library includes a whole buffet of useful packages, but sometimes you need to reach for a third-party library. That’s where pip comes in handy. In this video course, you’ll learn how to pip install packages.

Topics:
- 00:00:00 – Introduction
- 00:03:37 – How did you get involved in open source?
- 00:07:01 – Fostering a community around a project
- 00:11:32 – Python tooling could be much, much faster
- 00:15:45 – Changing the ergonomics of tooling
- 00:19:59 – What is ruff and what jobs can it do?
- 00:22:23 – How do you configure ruff?
- 00:26:02 – Where do the linting rules come from?
- 00:29:29 – Can you build your own rules?
- 00:31:28 – Performance difference for ruff
- 00:36:25 – Installing ruff
- 00:37:34 – The rustification of Python
- 00:40:52 – The initial features and release of uv
- 00:45:07 – Installing Python
- 00:47:50 – Taking over the python-build-standalone project
- 00:53:02 – Installation methods and suggestions
- 00:55:37 – Video Course Spotlight
- 00:57:07 – The project API
- 01:01:57 – Inline script metadata and PEP 723
- 01:06:49 – Installing tools with uvx
- 01:09:37 – Project management
- 01:11:20 – Astral as company and VC funding
- 01:19:23 – New static type checker
- 01:26:15 – What are you excited about in the world of Python?
- 01:27:12 – What do you want to learn next?
- 01:28:52 – How can people follow your work online?
- 01:29:34 – Thanks and goodbye
Show Links:
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7 February 2025, 12:00 pm
54 minutes 14 seconds

Testing Your Python Code Base: Unit vs. Integration
What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems.

We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing.

This episode is sponsored by Postman.

Course Spotlight: Testing Your Code With pytest

In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.

Topics:
- 00:00:00 – Introduction
- 00:02:28 – Python news and releases
- 00:04:02 – From Default Line Charts to Journal-Quality Infographics
- 00:07:25 – PyViz: Python Tools for Data Visualization
- 00:09:25 – Why Is hash(-1) == hash(-2) in Python?
- 00:12:40 – Sponsor: Postman
- 00:13:32 – Data Cleaning in Data Science
- 00:19:29 – 10 Ways to Work With Large Files in Python
- 00:23:40 – Unit Testing vs. Integration Testing
- 00:29:17 – Does university curriculum cover this?
- 00:31:22 – Building tests into smaller projects
- 00:36:04 – Video Course Spotlight
- 00:37:30 – How does the approach differ with clients or larger-scale projects?
- 00:40:45 – How do tests act as documentation?
- 00:42:02 – Difficulties in building integration tests
- 00:45:24 – How do you limit the results of tests?
- 00:47:52 – klp: Lightweight CLI Viewer for Log Files
- 00:50:54 – freezegun: Mocks the datetime Module for Testing
- 00:53:11 – Thanks and goodbye
News:
Topics:
- From Default Line Charts to Journal-Quality Infographics – “Everyone who has used Matplotlib knows how ugly the default charts look like.” In this series of posts, Vladimir shares some tricks to make your visualizations stand out and reflect your individual style.
- PyViz: Python Tools for Data Visualization – This site contains an overview of all the different visualization libraries in the Python ecosystem. If you’re trying to pick a tool, this is a great place to better understand the pros and cons of each.
- Why Is hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in CPython. This post examines how and discovers why this is the case.
- Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.”
- 10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge.
Discussion:
- Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python.
Project:
- klp: Lightweight CLI Viewer for Log Files
- freezegun: Mocks the datetime Module for Testing
Additional Links:
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31 January 2025, 12:00 pm
1 hour 22 minutes

Simon Willison: Using LLMs for Python Development
What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.

Simon has been researching LLMs over the past two and a half years and documenting the results on his blog. He shares which models work best for writing Python versus JavaScript and compares coding tools and environments.

We discuss prompt engineering techniques and the first steps to take. Simon shares his enthusiasm for the usefulness of LLMs but cautions about the potential pitfalls.

Simon also shares how he got involved in open-source development and Django. He’s a proponent of starting a blog and shares how it opened doors for his career.

This episode is sponsored by Postman.

Course Spotlight: Advanced Python import Techniques

The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code.

Topics:
- 00:00:00 – Introduction
- 00:02:38 – How did you get involved in open source?
- 00:04:04 – Writing an XML-RPC library
- 00:04:40 – Working on Django in Lawrence, Kansas
- 00:05:31 – Started building open-source collection
- 00:06:52 – shot-scraper: taking automated screenshots of websites
- 00:08:09 – First experiences with LLMs
- 00:10:08 – 22 years of simonwillison.net
- 00:18:22 – Navigating the hype and criticism of LLMs
- 00:22:14 – Where to start with Python code and LLMs?
- 00:26:22 – Sponsor: Postman
- 00:27:13 – ChatGPT Canvas vs Code Interpreter
- 00:28:23 – Asking nicely, tricking the system, and tipping?
- 00:30:35 – More Code Interpreter and building a C extension
- 00:32:05 – More details on Canvas
- 00:36:55 – What is a workflow for developing using LLMs?
- 00:39:43 – Creating pieces of code vs a system
- 00:42:00 – Workout program for prompting and pitfalls
- 00:53:54 – Video Course Spotlight
- 00:55:14 – Why an SVG of a pelican riding a bicycle?
- 00:57:48 – Repeating a query and refining
- 01:03:00 – Working in an IDE or text editor
- 01:05:45 – David Crawshaw on writing code with LLMs
- 01:08:33 – Running an LLM locally to write code
- 01:14:02 – Staying out of the AGI conversation
- 01:16:07 – What are you excited about in the world of Python?
- 01:18:34 – What do you want to learn next?
- 01:19:53 – How can people follow your work online?
- 01:20:51 – Thanks and goodbye
Show Links:
Level up your Python skills with our expert-led courses:
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24 January 2025, 12:00 pm
46 minutes 47 seconds

Principles for Considering Your Python Tooling
What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? 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 effective Python developer tooling. Instead of digging into a list of current libraries, we talk about the principles you must consider before making decisions for your team. We cover common pitfalls teams get mired in and how to avoid them.

We also share several other articles and projects from the Python community, including a news roundup, a huge collection of the top Python libraries of 2024, programming sockets in Python, merging dictionaries, a Django quiz, mistakes to avoid in production, building a Portal sentry turret, a powerful TUI expense tracker, and a pure-Python async rendering engine.

Course Spotlight: Managing Dependencies With Python Poetry

Learn how Python Poetry can help you start new projects, maintain existing ones, and master dependency management.

Topics:
- 00:00:00 – Introduction
- 00:01:53 – DjangoCon US 2025 (Chicago, Sept 8-12) Announced
- 00:02:38 – Textualize 1.0 Released
- 00:03:15 – Top Python Libraries of 2024
- 00:07:07 – Programming Sockets in Python
- 00:11:56 – Merging Dictionaries in Python
- 00:17:03 – Django Quiz 2024
- 00:17:55 – Confessions of a Django Dev: Mistakes To Avoid in Production
- 00:18:40 – Sentry Turret Straight Out of the ‘Portal’ Franchise
- 00:20:00 – Video Course Spotlight
- 00:21:26 – Effective Python Developer Tooling in December 2024
- 00:41:13 – Bagels: Powerful TUI Expense Tracker
- 00:43:42 – htmy: Async, Pure-Python Rendering Engine
- 00:45:41 – Thanks and goodbye
News:
- DjangoCon US 2025 (Chicago, Sept 8-12) Announced
- Textualize 1.0 Released
Show Links:
- Top Python Libraries of 2024 – For the past ten years, Tyrolabs has put together a list of their favorite Python libraries of the year. This list includes ten general purpose libraries and ten more specific to AI/ML and Data.
- Programming Sockets in Python – In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.
- Merging Dictionaries in Python – There are multiple ways of merging two or more dictionaries in Python. This post teaches you how to do it and how to deal with corner cases like duplicate keys.
- Django Quiz 2024 – Adam runs a quiz on Django at his Django London meetup. He’s shared it so you can try it yourself. Test how much you know about your favorite web framework.
- Confessions of a Django Dev: Mistakes To Avoid in Production – This post covers some of the common mistakes you might make when taking a Django project into production.
- Sentry Turret Straight Out of the ‘Portal’ Franchise – “Reckless_commenter has created a Raspberry Pi-powered sentry turret that looks and sounds just like the creepy machines found in the ‘Portal’ franchise.” Logic and sound effects managed through the PyGame library.
Discussion:
- Effective Python Developer Tooling in December 2024 – This post talks about how tooling doesn’t solve all your problems when you code, especially with a team. It outlines some principles to implement, and bad practices to avoid when writing Python.
- Mistakes engineers make in large established codebases - Sean Goedecke
Projects:
- Bagels: Powerful TUI Expense Tracker
- htmy: Async, Pure-Python Rendering Engine
Additional Links:
Level up your Python skills with our expert-led courses:
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17 January 2025, 12:00 pm
52 minutes 21 seconds

Building New Structures for Learning Python
What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn Python.

Stephen has recently joined the team, bringing years of online training expertise. He discusses our new offering of cohort-based courses, which combine live expert instruction, hands-on exercises, and a supportive community.

Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python.

Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months.

This episode is sponsored by Sentry.

Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP

In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one.

Topics:
- 00:00:00 – Introduction
- 00:02:29 – What Stephen has been up to
- 00:03:31 – What’s new for Martin
- 00:04:07 – Bringing on new team members
- 00:06:09 – Cohort-based courses
- 00:19:25 – Sponsor: Sentry
- 00:20:27 – Restructured and new learning paths
- 00:30:50 – Video Course Spotlight
- 00:32:19 – New Real Python Books
- 00:38:57 – A destination for learning
- 00:40:46 – Quizzes for tutorials and courses
- 00:44:58 – Video courses and updating content
- 00:47:52 – Code Mentor
- 00:49:45 – Code challenges
- 00:51:06 – Thanks and goodbye
Show Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
10 January 2025, 12:00 pm
41 minutes 3 seconds

PyCoder's Weekly 2024 Top Articles & Missing Gems
PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2024. Christopher Trudeau is back on the show this week to help wrap it all up by sharing some highlights and uncovering a few missing gems from the pile.

We share the top links that PyCoder’s readers explored this year and uncover trends across all the articles and stories. We also highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year.

We hope you enjoy this review! We look forward to bringing you another year filled with great Python news, articles, topics, and projects.

Course Spotlight: Programming Sockets in Python

In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.

Topics:
- 00:00:00 – Introduction
- 00:01:47 – New releases and updates
- 00:03:07 – PyCon US 2025 Registration Open
- 00:03:18 – PyCon Austria 2025 Call for Papers
- 00:03:36 – PSF Year End Fundraiser - Membership Drive
- 00:04:31 – Mr. Trudeau on Flying High with Flutter
- 00:05:29 – We’re on Bluesky - follow us!
- 00:07:44 – Build Captivating Display Tables in Python With Great Tables
- 00:08:45 – Overview of the Module itertools
- 00:09:23 – Customize VS Code Settings
- 00:10:34 – Modern Good Practices for Python Development
- 00:11:55 – Asyncio Event Loop in Separate Thread
- 00:12:38 – Python Protocols: Leveraging Structural Subtyping
- 00:13:06 – Thoughts on the top links
- 00:22:29 – Video Course Spotlight
- 00:23:40 – Why I’m Switching From pandas to Polars
- 00:29:29 – Lessons Learned Reinventing the Python Notebook
- 00:32:47 – What’s a Python Hashable Object?
- 00:36:10 – uv: Python Packaging in Rust
- 00:38:26 – CI/CD for Python With GitHub Actions
- 00:40:07 – Thanks and goodbye
News:
Top PyCoders Links 2024:
- Build Captivating Display Tables in Python With Great Tables – Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library.
- Overview of the Module itertools – This article proposes the top three iterators that are most useful from the module itertools, classifies all of the 19 iterators into five categories, and then provides brief usage examples for all the iterators in the module itertools.
- Customize VS Code Settings – In this course, Philipp helps you customize your Visual Studio Code settings to switch from a basic cluttered look to a clean presentable look. This is not just pleasant on the eyes, but also gives you a nice user interface if you want to share on a Zoom call or screen recording.
- Modern Good Practices for Python Development – This is a very detailed list of best practices for developing in Python. It includes tools, language features, application design, which libraries to use and more.
- Asyncio Event Loop in Separate Thread – Typically, the asyncio event loop runs in the main thread, but as that is the one used by the interpreter, sometimes you want the event loop to run in a separate thread. This article talks about why and how to do just that.
- Python Protocols: Leveraging Structural Subtyping – In this tutorial, you’ll learn about Python’s protocols and how they can help you get the most out of using Python’s type hint system and static type checkers.
Featured Links:
- Why I’m Switching From pandas to Polars – Ari is switching from pandas to Polars and surprisingly (even to himself) it isn’t because of the better performance. Read on for the reasons why.
- Lessons Learned Reinventing the Python Notebook – Marimo is an open source alternative to Jupyter notebooks. This article is by one of marimo’s creators, talking about the design decisions made when creating it.
- What’s a Python Hashable Object? – You can ignore reading about hashable objects for quite a bit. But eventually, it’s worth having an idea of what they are. This post follows Winston on his first day at work to understand hashable objects
- uv: Python Packaging in Rust – uv is an extremely fast Python package installer and resolver, designed as a drop-in alternative to pip and pip-tools. This post introduces you to uv and shows some of its performance numbers. Associated HN discussion.
- CI/CD for Python With GitHub Actions – With most software following agile methodologies, it’s essential to have robust DevOps systems in place to manage, maintain, and automate common tasks with a continually changing codebase. By using GitHub Actions, you can automate your workflows efficiently, especially for Python projects.
Additional Links:
- Flying High with Flutter
- The State of Python 2024 – This is a guest post on the PyCharm blog by Talk Python host Michael Kennedy who talks about the current state of Python in 2024. Topics include language usage, web frameworks, uv, and more.
- Django 2024 Year in Review – Carlton is a core contributor to Django and this post talks about what happened in 2024 with your favorite web framework.
- Episode #193: Wes McKinney on Improving the Data Stack & Composable Systems
- Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
- Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python
- Episode #203: Embarking on a Relaxed and Friendly Python Coding Journey
- Ruff: A Modern Python Linter for Error-Free and Maintainable Code
- Rodrigo 🐍🚀: Python folks, here’s an update on all the Python starter packs — Bluesky
- Christopher Bailey (@digiglean.bsky.social) — Bluesky
- Christopher Trudeau (@cltrudeau.bsky.social) — Bluesky
- Stephen Gruppetta (@stephengruppetta.com) — Bluesky
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3 January 2025, 12:00 pm
1 hour 13 minutes

Exploring Modern Sentiment Analysis Approaches in Python
What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.

Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.

In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.

This week’s episode is brought to you by Sentry.

Course Spotlight: Learn Text Classification With Python and Keras

In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.

Topics:
- 00:00:00 – Introduction
- 00:02:31 – Conference talks in 2024
- 00:04:23 – Background on sentiment analysis and studying feelings
- 00:07:09 – What led you to study emotions?
- 00:08:57 – Dimensional emotion classification
- 00:10:42 – Different types of sentiment analysis
- 00:14:28 – Lexicon-based approaches
- 00:17:50 – VADER - Valence Aware Dictionary and sEntiment Reasoner
- 00:19:41 – TextBlob and subjectivity scoring
- 00:21:48 – Sponsor: Sentry
- 00:22:52 – Measuring sentiment of New Year’s resolutions
- 00:27:28 – Lexicon-based approaches links for experimenting
- 00:28:35 – Multiple language support in lexicon-based packages
- 00:35:23 – Machine learning techniques
- 00:39:20 – Tools for this approach
- 00:42:54 – Video Course Spotlight
- 00:44:15 – Advantages to the machine learning models approach
- 00:45:55 – Large language model approach
- 00:48:44 – Encoder vs decoder models
- 00:52:09 – Comparing the concept of fine-tuning
- 00:56:49 – Is this a recent development?
- 00:58:08 – Ways to practice with these techniques
- 01:00:10 – Do you find this to be a promising approach?
- 01:07:45 – Resources to practice with all the techniques
- 01:11:06 – Upcoming conference talks
- 01:11:56 – Thanks and goodbye
Show Links:
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20 December 2024, 12:00 pm
51 minutes 26 seconds

Good Python Programming Practices When New to the Language
What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an older forum post from a new Python user who came from Perl. We suggest checking out PEP 8, or as it’s commonly known, “The Style Guide for Python Code.” We provide advice about installing Python, avoiding common pitfalls, learning how scope is managed, and taking advantage of a collection of Real Python resources.

We share several other articles and projects from the Python community, including a new Python release, practical NumPy examples and exercises, considering targets of for loops, exploring Python dependency management, checking package compatibility with free-threading and subinterpreters, an experimental filesystem navigator in Textual, and a background workers reference implementation in Django.

This episode is sponsored by AssemblyAI.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

Learn how to write high-quality, readable code by using the Python style guidelines laid out in PEP 8. Following these guidelines helps you make a great impression when sharing your work with potential employers and collaborators. This course outlines the key guidelines laid out in PEP 8. It’s aimed at beginner to intermediate programmers.

Topics:
- 00:00:00 – Introduction
- 00:02:17 – Python 3.14.0 Alpha 2 Released
- 00:02:35 – Take the 2024 Django Developers Survey
- 00:03:17 – NumPy Practical Examples: Useful Techniques
- 00:07:09 – Loop Targets
- 00:09:19 – Python Dependency Management Is a Dumpster Fire
- 00:23:15 – Sponsor: AssemblyAI
- 00:24:00 – Package Compatibility With Free-Threading and Subinterpreters
- 00:27:02 – Suggestions for good programming practices?
- 00:37:59 – Video Course Spotlight
- 00:39:24 – terminal-tree: Experimental Filesystem Navigator in Textual
- 00:43:56 – django-tasks: Background Workers Reference Implementation
- 00:49:44 – Thanks and goodbye
News:
- Python 3.14.0 Alpha 2 Released
- Take the 2024 Django Developers Survey
Topics:
- NumPy Practical Examples: Useful Techniques – In this tutorial, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.
- Loop Targets – Loop assignment allows you to assign to a dict item in a for loop. This post covers what that means and that it is no more costly than regular assignment.
- Python Dependency Management Is a Dumpster Fire – Managing dependencies in Python can be a bit of a challenge. This deep dive article shows you all the problems and how the problems are mitigated if not solved.
- Package Compatibility With Free-Threading and Subinterpreters – This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features.
Discussion:
Projects:
- terminal-tree: Experimental Filesystem Navigator in Textual
- django-tasks: Background Workers Reference Implementation
Additional Links:
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6 December 2024, 12:00 pm
1 hour 58 seconds

marimo: Reactive Notebooks and Deployable Web Apps in Python
What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).

Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

Topics:
- 00:00:00 – Introduction
- 00:02:06 – Akshay’s background and studies
- 00:04:14 – Work at Google and PhD program
- 00:06:29 – Sharing notebooks
- 00:08:18 – Starting work on marimo 2 years ago
- 00:12:48 – Avoiding notebook issues and building a DAG
- 00:18:39 – The difference of reactivity
- 00:20:39 – What is a marimo notebook?
- 00:23:39 – Video Course Spotlight
- 00:24:50 – Reproducibility and managing package requirements
- 00:27:49 – Using decorators for cells
- 00:30:23 – Writing a design document before any coding
- 00:34:08 – Interactivity and UI widgets
- 00:38:20 – Design decisions and built-in widgets
- 00:42:05 – Creating a deployable web application
- 00:44:34 – Exploring examples and tutorials
- 00:46:13 – Supporting DataFrame libraries with narwhals
- 00:48:00 – Migrating from a Jupyter notebook
- 00:52:02 – Working with cells and not running code
- 00:54:30 – A couple favorite tutorials
- 00:56:17 – What are you excited about in the world of Python?
- 00:57:39 – What do you want to learn next?
- 00:59:34 – How can people follow the project and yourself?
- 01:00:12 – Thanks and goodbye
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29 November 2024, 12:00 pm
45 minutes 50 seconds

The Joy of Tinkering & Python Free-Threading Performance
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.

This episode is sponsored by Windsurf.

Course Spotlight: Python Inner Functions

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
- 00:44:21 – Thanks and goodbye
News:
Show Links:
- 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).
Discussion:
- Habits of Great Software Engineers - The Joy of Tinkering
Projects:
Additional Projects:
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Support the podcast & join our community of Pythonistas
22 November 2024, 12:00 pm
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