The Real Python Podcast

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.

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:

• Episode #97: Improving Your Django and Python Developer Experience
• Deployment checklist - Django documentation
• Portal (video game) - Wikipedia
• PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link

Level up your Python skills with our expert-led courses:

• How to Set Up a Django Project
• Managing Dependencies With Python Poetry
• Exploring Scopes and Closures in Python

Support the podcast & join our community of Pythonistas

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.

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:

• Cohort Course - Intermediate Python Deep Dive
• Python Learning Paths
• Python Books by Real Python
• Python Quizzes
• Join the Real Python Community Chat
• Code Mentor: Intelligent Learning Tools
• Office Hours – Real Python
• Debugging Python with VS Code and Sentry - Product Blog - Sentry
• About Martin Breuss – Real Python
• About Stephen Gruppetta – Real Python

Level up your Python skills with our expert-led courses:

• Handling or Preventing Errors in Python: LBYL vs EAFP
• Using raise for Effective Exceptions
• Python Basics Exercises: Scopes

Support the podcast & join our community of Pythonistas

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.

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:

• NumPy Release 2.2.0
• Django Security Releases Issued: 5.1.4, 5.0.10, and 4.2.17
• Python 3.13.1, 3.12.8, 3.11.11, 3.10.16, and 3.9.21 Released
• Python Insider: Python 3.14.0 alpha 3 is out
• PyCon US 2025 (Pittsburgh, PA) Registration Open
• PyCon Austria 2025 (Eisenstadt) Call for Papers
• PSF Year End Fundraiser - Membership Drive

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

Level up your Python skills with our expert-led courses:

• Building HTTP APIs With Django REST Framework
• HTML and CSS Foundations for Python Developers
• Programming Sockets in Python

Support the podcast & join our community of Pythonistas

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.

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:

• Introduction to Sentiment Analysis in Python - The PyCharm Blog
• How to Do Sentiment Analysis With Large Language Models - The PyCharm Blog
• Talks - Jodie Burchell: Lies, damned lies and large language models - YouTube
• Mirror, mirror: LLMs and the illusion of humanity - Jodie Burchell - YouTube
• Separating fact from fiction in a world of AI fairytales - Jodie Burchell - NDC London 2024 - YouTube
• Hurt Feelings (Rap Version) - Flight Of The Conchords (Lyrics) - YouTube
• Universal Emotions - What are Emotions? - Paul Ekman Group
• VADER - nltk.sentiment.vader module
• clips/pattern: Web mining module for Python, with tools for scraping, natural language processing, machine learning
• TextBlob: Simplified Text Processing — TextBlob documentation
• Power vs. Force: The Hidden Determinants of Human Behavior by David R. Hawkins - Goodreads
• Episode #36: Sentiment Analysis, Fourier Transforms, and More Python Data Science – The Real Python Podcast
• Use Sentiment Analysis With Python to Classify Movie Reviews – Real Python
• Sentiment Analysis: First Steps With Python’s NLTK Library – Real Python
• Sentiment Analysis in DataSpell with @JetBrainsTV - YouTube
• Episode #119: Natural Language Processing and How ML Models Understand Text – The Real Python Podcast
• spaCy - Industrial-strength Natural Language Processing in Python
• amazon_polarity - Datasets at Hugging Face
• Introduction to Sentiment Analysis in Python - The PyCharm Blog
• Kaggle: Your Machine Learning and Data Science Community
• ZS BIT
• AI Community Day - 10 December 2024
• Jodie Burchell - The JetBrains Blog
• Jodie Burchell’s Blog - Standard error
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) - Twitter
• Jodie Burchell (@t-redactyl.bsky.social) — Bluesky
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl@fosstodon.org) - Fosstodon
• JetBrains: Essential tools for software developers and teams

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Learn Text Classification With Python and Keras
• Exploring Astrophysics in Python With pandas and Matplotlib

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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.

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:

• Suggestions for good programming practices?
• Python Best Practices – Real Python
• PEP 8 – Style Guide for Python Code

Projects:

• terminal-tree: Experimental Filesystem Navigator in Textual
• django-tasks: Background Workers Reference Implementation

Additional Links:

• Episode #146: Using NumPy and Linear Algebra for Faster Python Code – The Real Python Podcast
• How to Write Beautiful Python Code With PEP 8 – Real Python
• Writing Idiomatic Python – Real Python
• Namespaces and Scope in Python – Real Python
• How to Install Python on Your System: A Guide – Real Python
• Python Virtual Environments: A Primer – Real Python
• Sourcery - Instant Code Review for Faster Velocity
• Episode #183: Exploring Code Reviews in Python and Automating the Process
• Textual
• uv - An extremely fast Python package and project manager, written in Rust.
• DEP 0014: Background workers - GitHub
• PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link

Level up your Python skills with our expert-led courses:

• Navigating Namespaces and Scope in Python
• Writing Idiomatic Python
• Writing Beautiful Pythonic Code With PEP 8

Support the podcast & join our community of Pythonistas

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.

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

Show Links:

• marimo - a next-generation Python notebook
• marimo: an open-source reactive notebook for Python - Akshay Agrawal (Nbpy2024) - YouTube
• TensorFlow
• Made with marimo - marimo
• FAQ - marimo
• Pluto.jl — interactive Julia programming environment
• Observable: Build expressive charts and dashboards with code
• We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned - The Datalore Blog
• A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks
• Lessons learned reinventing the Python notebook - marimo
• Episode #226: PySheets: Spreadsheets in the Browser Using PyScript
• PEP 723 – Inline script metadata
• Inline script metadata - Python Packaging User Guide
• Serializing package requirements in marimo notebooks - marimo
• uv: Unified Python packaging
• marimo Newsletter 7 - Jupyter to marimo
• Custom UI elements - marimo
• anywidget - anywidget
• Interactive elements - marimo
• Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries
• Calmcode - marimo: Introduction
• Join the marimo Discord
• marimo newsletter
• marimo on Twitter
• marimo on LinkedIn
• Akshay Agrawal’s website
• Aksahy on Twitter

Level up your Python skills with our expert-led courses:

• Navigating Namespaces and Scope in Python
• Python Decorators 101
• Using Jupyter Notebooks

Support the podcast & join our community of Pythonistas

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.

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:

• Django Bugfix Release Issued: 5.1.3
• Pillow Release 11.0.0
• Flask Version 3.1.0
• PyCon US 2025 (Pittsburgh) Call for Proposals

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:

• shot-scraper: A command-line utility for taking automated screenshots of websites
• django-admin-tui: Django admin in the terminal!
• django-admin-dracula: 🦇 Dracula themes for the Django admin

Additional Projects:

• Primer on Python Decorators – Real Python
• We’ve moved to Hetzner - Talk Python Blog
• Talk Python rewritten in Quart (async Flask) - Talk Python Blog
• PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link

Level up your Python skills with our expert-led courses:

• Python Decorators 101
• Python Inner Functions
• Defining and Calling Python Functions

Support the podcast & join our community of Pythonistas

How do you build a sustainable open-source project and community? What lessons can be learned from Python’s history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

Paul has been an organizer in the Python community almost from the beginning. He shares how the project has navigated through multiple sponsors. We talk about the early governance models and the formation of the Python Software Foundation.

We contrast this journey with the current drama unfolding in the WordPress community. We discuss the potential problems of having a benevolent dictator for life. We also dig into sponsorship models and ways to get companies to give back to the open-source projects they rely on.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:01:55 – Meeting Jodie Burchell at PyCon 2022
• 00:02:51 – A non-traditional path into open-source
• 00:07:09 – The current turmoil around WordPress
• 00:13:49 – Keeping things fair in the age of extraction
• 00:16:03 – Sponsor: Sentry
• 00:17:07 – Early Python organizing history and conservation
• 00:20:41 – The Python Software Activity precursor to PSF
• 00:24:14 – Creating the Python Software Foundation
• 00:27:24 – Keeping the perfect distance of business and project
• 00:28:13 – Who gets to capture the value from open-source?
• 00:31:07 – Sponsorships becoming more common
• 00:33:24 – BDFL to a steering council
• 00:34:58 – Video Course Spotlight
• 00:36:16 – What is Plone?
• 00:38:11 – Starting in Python and finding community
• 00:50:07 – Companies contributing
• 00:53:16 – Examples of how JetBrains contributes back
• 00:55:41 – Understanding the support system
• 00:58:09 – Talking to decision makers
• 01:00:07 – Python 1994 talk and continuation
• 01:01:49 – What are you excited about in the world of Python?
• 01:03:06 – What do you want to learn next?
• 01:04:17 – How can people follow your work online?
• 01:07:16 – Thanks and goodbye

Show Links:

• JetBrains: Essential tools for software developers and teams
• PyCharm: the Python IDE for data science and web development
• PyCon - Join us at PyCon
• Benevolent dictator for life - Wikipedia
• The messy WordPress drama, explained - The Verge
• WordPress.org’s latest move involves taking control of a WP Engine plugin - The Verge
• WP Engine asks court to stop Matt Mullenweg from blocking access to WordPress resources - The Verge
• Podcast: Why the WordPress Chaos Matters - 404 Media
• Zope - Wikipedia
• Python Software Foundation
• PyLadies – Women Who Love Coding in Python
• Django Software Foundation - Django
• OpenCV - About Page
• Plone Foundation
• FastHTML - Modern web applications in pure Python
• Paul Everitt - Python 1994 - YouTube
• A Team at Microsoft is Helping Make Python Faster - Python
• Velda Kiara
• JetBrains Blog: The Drive to Develop
• Paul Everitt (@pauleveritt@fosstodon.org) - Fosstodon
• Guido van Rossum - Wikipedia
• The History of Python: Personal History - part 1, CWI
• Oral History of Guido van Rossum, part 1 - YouTube

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• The pandas DataFrame: Working With Data Efficiently
• Using pandas to Make a Gradebook in Python

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Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a set of recent Python Enhancement Proposals (PEPs). The idea of template strings has been under consideration for a while, and PEP 750 describes a new way forward. PEP 759 proposes a way for projects on PyPI to safely host resources on external sites using a new package upload format called a .rim file.

We share several other articles and projects from the Python community, including what didn’t make the headlines about Python 3.13, solving Sudoku with Python packaging, what’s sweet about Python’s syntactic sugar, creating database-generated columns using SQLite and Django, a discussion about mentoring, an adaptive web scraper, and a debugging tool for HTTP(S) client requests.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:08 – Python 3.14.0 Alpha 1 Released
• 00:02:38 – Python 3.13, What Didn’t Make the Headlines
• 00:05:23 – What’s up Python? 3.13 is out, t-strings look awesome
• 00:10:21 – Sponsor: Sentry
• 00:11:25 – Sudoku in Python Packaging
• 00:14:29 – Syntactic Sugar: Why Python Is Sweet and Pythonic
• 00:22:31 – Database generated columns: Django & SQLite
• 00:27:14 – Video Course Spotlight
• 00:28:39 – Mentors
• 00:42:23 – Scrapling: Lightning-Fast, Adaptive Web Scraping for Python
• 00:44:14 – httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests
• 00:46:04 – Request for project submissions to PyCoders
• 00:46:59 – Thanks and goodbye

News:

• Python 3.14.0 Alpha 1 Released

Show Links:

• Python 3.13, What Didn’t Make the Headlines – Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb, improvements to shutil, and small additions to the asyncio library.
• What’s up Python? 3.13 is out, t-strings look awesome, dep groups come in handy…
• Sudoku in Python Packaging – Simon writes about a Sudoku solver written by Konstin that uses the Python packaging mechanisms to do Sudoku puzzles. The results are output using a requirements.txt file, where sudoku-0-3==5 represents the (0,3) cell’s answer of 5.
• Syntactic Sugar: Why Python Is Sweet and Pythonic – In this tutorial, you’ll learn what syntactic sugar is and how Python uses it to help you create more readable, descriptive, clean, and Pythonic code. You’ll also learn how to replace a given piece of syntactic sugar with another syntax construct.
• Database generated columns: Django & SQLite – An introduction to database generated columns, using SQLite and the new GeneratedField added in Django 5.0

Discussion:

• Mentors – Ryan just finished his second round of mentoring with the Djangonaut.Space program. This post talks about how you can help your mentor help you and how to be a good mentor.

Projects:

• Scrapling: Lightning-Fast, Adaptive Web Scraping for Python
• httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests in a Python program

Additional Links:

• PEP 750 – Template Strings
• PEP 735 – Dependency Groups in pyproject.toml
• PEP 759 – External Wheel Hosting
• Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon – The Real Python Podcast
• Episode #92: Continuing to Unravel Python’s Syntactic Sugar With Brett Cannon – The Real Python Podcast
• Episode #4: Learning Python Through Errors – The Real Python Podcast
• PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link

Level up your Python skills with our expert-led courses:

• Using Pydantic to Simplify Python Data Validation
• Python Type Checking
• Using Type Hints for Multiple Return Types in Python

Support the podcast & join our community of Pythonistas

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book “Communication for Engineers.”

As a software engineer, Chris has worked at IBM, Google, Uber, and several financial institutions. He speaks about developer productivity and communication skills as an engineer. We begin our conversation by digging into his background, his approach to building engineering teams, and strategies for improving communication.

Chris’ idea for PySheets is to have Excel inside Python with everything running locally in your browser. He was inspired by the success of Jupyter Notebooks but wanted to develop a tool more suited to a spreadsheet’s non-linear graph structure.

PySheets is built to run locally in the user’s browser, taking advantage of PyScript. We discuss finding the right solution for storing data in the browser and developing a graphic toolkit to create the UI. Chris also shares the novel method he found to get the interface up and running while the larger assets are loading.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:25 – Background with building engineering teams
• 00:08:43 – Communication for Engineers book
• 00:16:17 – What do customers want and experiences at IBM
• 00:24:28 – Starting the development of PySheets
• 00:27:19 – Working with the DOM
• 00:29:41 – Success of Jupyter notebooks
• 00:35:46 – Sponsor: Sentry
• 00:36:52 – Little Toolkit for PyScript
• 00:43:24 – Finding funding
• 00:46:58 – Building a product before selling
• 00:52:27 – Video Course Spotlight
• 00:53:46 – Finding the right data storage in IndexedDB
• 01:01:57 – Exploring the trial page and extensibility
• 01:08:26 – Contributing to the project or forking
• 01:11:56 – What are you excited about in the world of Python?
• 01:16:20 – What do you want to learn next?
• 01:17:25 – How can people follow your work online?
• 01:18:05 – Thanks and goodbye

Show Links:

• Chris Laffra
• C4E - Communication for Engineers (ePUB)
• PySheets - Spreadsheet UI for Python
• PySheets: Source for PySheets
• PyScript - Python in the browser - Chris Laffra - YouTube
• Python in Excel - Microsoft 365
• pyscript/ltk: LTK is a little toolkit for writing UIs in PyScript
• LTK - Little Toolkit
• PROCOL: a parallel object language with protocols - ACM SIGPLAN
• IndexedDB API - MDN
• First steps - PyScript
• Pyodide — Version 0.26.3
• PyScript Updates: Bytecode Alliance, Pyodide, and MicroPython
• MicroPython - Python for microcontrollers
• FreeCAD: Your own 3D parametric modeler
• Chris Laffra - How to become a Happy and Productive Engineer - YouTube

Level up your Python skills with our expert-led courses:

• What's New in Python 3.13
• Python Plotting With Matplotlib
• Understanding Python's Global Interpreter Lock (GIL)

Support the podcast & join our community of Pythonistas

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article about Python’s recent performance improvements. The piece covers the specialized adaptive interpreter and explains what those terms mean. It also includes details about the experimental feature of the Just-In-Time (JIT) compiler added in 3.13.

We dig into a collection of Django projects you can use to practice and develop your skills. The projects ramp up from detailed beginner tutorials to more advanced projects with guidelines on how to get started. We also discuss a collection of popular websites that use Django.

We share several other articles and projects from the Python community, including a batch of recent Python Enhancement Protocols (PEPs), a couple of Python releases, using DuckDB in the browser with Pyodide, building a contact book app with Textual, generating a tiny status page with a Python script, and a grep-like tool that understands code.

This episode is sponsored by AssemblyAI.

Topics:

• 00:00:00 – Introduction
• 00:03:11 – PEP 777: How to Re-Invent the Wheel
• 00:04:22 – PEP 758: Allow except and except* Expressions Without Parentheses
• 00:04:51 – PEP 760: No More Bare Excepts (Withdrawn)
• 00:05:42 – PEP 735: Dependency Groups in pyproject.toml
• 00:06:29 – PEP 761: Deprecating PGP Signatures for CPython Artifacts
• 00:06:59 – Python 3.12.7 Released
• 00:07:12 – Incremental GC and Pushing Back the 3.13.0 Release
• 00:09:10 – DuckDB in the Browser With Pyodide
• 00:15:35 – Sponsor: AssemblyAI
• 00:16:18 – Build a Contact Book App With Python, Textual, and SQLite
• 00:21:55 – Django Project Ideas
• 00:28:42 – Video Course Spotlight
• 00:30:00 – In the Making of Python Fitter and Faster
• 00:35:13 – tinystatus: Tiny Status Page Generated by a Python Script
• 00:38:06 – srgn: Grep-Like Tool That Understands Code
• 00:42:01 – Thanks and goodbye

News:

• PEP 777: How to Re-Invent the Wheel – “The current wheel 1.0 specification was written over a decade ago, and has been extremely robust to changes in the Python packaging ecosystem… this PEP prescribes compatibility requirements on future wheel revisions.”
• PEP 758: Allow except and except* Expressions Without Parentheses – “This PEP proposes to allow unparenthesized except and except* blocks in Python’s exception handling syntax. Currently, when catching multiple exceptions, parentheses are required around the exception types.”
• PEP 760: No More Bare Excepts (Withdrawn)
• PEP 735: Dependency Groups in pyproject.toml (Accepted)
• PEP 761: Deprecating PGP Signatures for CPython Artifacts – Since Python 3.11.0, CPython has provided two verifiable digital signatures for all CPython artifacts: PGP and sigstore. This PEP proposes moving to sigstore as the only way of signing artifacts.
• Python 3.12.7 Released
• Python 3.13.0 Released
• Incremental GC and Pushing Back the 3.13.0 Release – Some last minute performance considerations delayed the release of Python 3.13 with one of the features being backed out.

Show Links:

• DuckDB in the Browser With Pyodide – Learn how to run DuckDB in an in-browser Python environment to enable simple querying on remote files, interactive documentation, and easy to use training materials.
• Build a Contact Book App With Python, Textual, and SQLite – In this tutorial, you’ll be guided step by step through the process of building a basic contact book application. You’ll use Python and Textual to build the application’s text-based user interface (TUI), and then use SQLite to manage the database.
• Django Project Ideas – Looking to experiment or build your portfolio? Discover creative Django project ideas for all skill levels, from beginner apps to advanced full-stack projects.
• In the Making of Python Fitter and Faster – This post details how Python’s recent performance improvements work under the hood. It covers changes to the interpreter, better memory management, and the newly experimental JIT compiler.

Projects:

• tinystatus: Tiny Status Page Generated by a Python Script
• srgn: Grep-Like Tool That Understands Code

Additional Links:

• What Are Python Wheels and Why Should You Care? – Real Python
• Deploy your first JupyterLite website on GitHub Pages — JupyterLite 0.4.3 documentation
• rich: Python library for rich text and beautiful formatting in the terminal
• The 10 Most Popular Websites Using Django
• Django in Action
• Django and htmx Tutorial: Easier Web Development - YouTube
• Build a Site Connectivity Checker in Python – Real Python
• Refactoring Python with 🌳 Tree-sitter & Jedi | Jack’s blog

Level up your Python skills with our expert-led courses:

• Building a Site Connectivity Checker
• How to Set Up a Django Project
• Building Command Line Interfaces With argparse

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How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

Narwhals is a project aimed at library maintainers rather than end users. We discuss how the added compatibility benefits users by supporting modern features like lazy evaluation. We cover several projects Marco has been working with to implement Narwhals, including Altair, scikit-lego, and Ibis.

We also discuss how Marco started contributing to open-source projects. Marco has contributed to both pandas and Polars, which helps explain his interest in growing compatibility between libraries. He also offers advice on making your first contribution.

This episode is sponsored by CodeRabbit.

Topics:

• 00:00:00 – Introduction
• 00:02:02 – Euro SciPy 2024 and sprints
• 00:04:04 – How did you get involved in open source?
• 00:07:18 – Finding a good issue to get started
• 00:09:25 – Discord and open-source projects
• 00:11:12 – Who would you describe Narwhals?
• 00:16:47 – Working on Polars
• 00:19:17 – Apache Arrow and a data interchange protocol
• 00:22:55 – Sponsor: CodeRabbit
• 00:23:55 – Digging into eager vs lazy
• 00:27:04 – Ibis DataFrame library
• 00:28:57 – What do libraries need from Narwhals?
• 00:34:57 – The scikit-lego library
• 00:37:15 – Video Course Spotlight
• 00:38:45 – Other libraries interested in Narwhals
• 00:41:56 – Compatibility policy
• 00:45:18 – What should an end user expect?
• 00:46:32 – Have other projects that attempted this?
• 00:47:54 – Keeping the project light and pure Python
• 00:49:32 – Contributors and how to get involved
• 00:54:42 – What are you excited about in the world of Python?
• 00:57:18 – What do you want to learn next?
• 00:59:05 – How can people follow your work online?
• 00:59:27 – Thanks and goodbye

Show Links:

• Narwhals
• EuroSciPy
• narwhals: Lightweight and Extensible Compatibility Layer Between DataFrame Libraries! - GitHub
• DataFrame Interoperability - What’s Been Achieved, and What Comes Next? - PyCon Lithuania - YouTube
• How Narwhals Has Many End Users … That Never Use It Directly - YouTube
• Polars Has a New Lightweight Plotting Backend - Altair
• pandas - Python Data Analysis Library
• Polars — DataFrames for the new era
• great-tables - PyPI
• Episode #214: Build Captivating Display Tables in Python With Great Tables
• Ibis
• Episode #201: Decoupling Systems to Get Closer to the Data
• Great Tables is Now BYODF (Bring Your Own DataFrame)
• How Narwhals and scikit-lego Came Together to Achieve DataFrame-Agnosticism
• Explore Using Narwhals in Plotly Express · Issue #4749 - GitHub
• Fairlearn
• Perfect Backwards Compatibility Policy - Narwhals
• uv: Unified Python packaging
• pixi - Powerful Development Environments
• Narwhals - Discord
• marcogorelli (@marcogorelli@fosstodon.org) - Fosstodon
• Marco Gorelli - Quansight - LinkedIn

Level up your Python skills with our expert-led courses:

• What's New in Python 3.13
• pandas GroupBy: Grouping Real World Data in Python
• The pandas DataFrame: Working With Data Efficiently

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Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, “Python 3.13: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.13” covers the topics from the article and shows the new features in action.

Geir Arne and Christopher dug into the release to create code examples of the new features for the tutorial and course. We look at the options for disabling the Global Interpreter Lock (GIL) and enabling the Just-in-Time (JIT) compiler. We also discuss the new interactive interpreter, better error messages, multiple improvements to static typing, and additional 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 3.13.

This is episode is sponsored by Nvidia.

Topics:

• 00:00:00 – Introduction
• 00:03:14 – A Modern REPL
• 00:08:54 – Making the Global Interpreter Lock Optional in CPython
• 00:11:33 – JIT Compilation
• 00:15:48 – More improved error messages
• 00:18:30 – Sponsor: NVIDIA
• 00:19:13 – Marking deprecations using the type system
• 00:21:09 – Type Defaults for Type Parameters
• 00:22:44 – Narrowing types with TypeIs
• 00:25:24 – TypedDict: Read-only items
• 00:27:50 – Random command line interface
• 00:29:54 – New copy.replace()
• 00:33:43 – Video Course Spotlight
• 00:34:55 – Pathlib and globbing
• 00:39:33 – Stripping docstrings
• 00:41:28 – Import improvements
• 00:41:56 – Dynamically import non-code files
• 00:42:23 – Adding iOS as a supported platform
• 00:43:32 – More consistency with local namespace
• 00:44:30 – Support for deprecation in argparse
• 00:45:00 – Better entry points for breakpoint or set_trace
• 00:46:08 – Removing dead batteries
• 00:47:43 – When to upgrade to 3.13?
• 00:53:19 – core.py podcast
• 00:54:14 – Thanks and goodbye

Show Links:

• Python 3.13: Cool New Features for You to Try
• What’s New in Python 3.13
• Python 3.13 Preview: A Modern REPL
• Python 3.13 Preview: Free Threading and a JIT Compiler
• What’s New In Python 3.13 — Python 3.13.0rc2 documentation
• PEP 703 – Making the Global Interpreter Lock Optional in CPython
• PEP 744 – JIT Compilation
• PEP 702 – Marking deprecations using the type system
• PEP 696 – Type Defaults for Type Parameters
• PEP 742 – Narrowing types with TypeIs
• PEP 705 – TypedDict: Read-only items
• PEP 730 – Adding iOS as a supported platform
• PEP 738 – Adding Android as a supported platform
• PEP 667 – Consistent views of namespaces
• PEP 594 – Removing dead batteries from the standard library
• The Python Standard REPL: Try Out Code and Ideas Quickly
• Unlock IPython’s Magical Toolbox for Your Coding Journey
• core.py Podcast - Episode 14: Integration Events

Level up your Python skills with our expert-led courses:

• What's New in Python 3.13
• Python Type Checking
• What's New in Python 3.12

Support the podcast & join our community of Pythonistas

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent post by Hynek Schlawack about building Python projects using Docker containers. Hynek argues for using virtual environments for these projects, like developing a local one. He’s found that keeping your code in an isolated, well-defined location and structure avoids confusion and complexity.

We also discuss our development setups, including Python versions, code editors, virtual environment practices, terminals, and customizations. We dig into how your programming history affects the tools you use.

We share several other articles and projects from the Python community, including a group of new releases, addressing the “why” in comments, comparing a data science workflow in Python and R, removing common problems from CSV files, and a project for creating HTML tables in Django.

This episode is sponsored by InfluxData.

Topics:

• 00:00:00 – Introduction
• 00:02:55 – Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20
• 00:03:26 – Python Release Python 3.13.0rc2
• 00:04:07 – Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16
• 00:04:36 – Polars Has a New Lightweight Plotting Backend
• 00:05:49 – Why I Still Use Python Virtual Environments in Docker
• 00:11:37 – How to Use Conditional Expressions With NumPy where()
• 00:15:55 – Sponsor: InfluxData
• 00:16:39 – PythonistR: A Match Made in Data Heaven
• 00:23:44 – Why Not Comments
• 00:26:48 – Video Course Spotlight
• 00:28:10 – Discussion: Personal development setups
• 00:51:01 – csv_trimming: Remove Common Ugliness From CSV Files
• 00:53:01 – django-tables2: Create HTML Tables in Django
• 00:54:39 – Thanks and goodbye

News:

• Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20
• Python Release Python 3.13.0rc2
• Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16

Show Links:

• Polars Has a New Lightweight Plotting Backend – Polars 1.6 allows you to natively create beautiful plots without pandas, NumPy, or PyArrow. This is enabled by Narwhals, a lightweight compatibility layer between dataframe libraries.
• Why I Still Use Python Virtual Environments in Docker – Hynek often gets challenged when he suggests the use of virtual environments within Docker containers, and this post explains why he still does.
• How to Use Conditional Expressions With NumPy where() – This tutorial teaches you how to use the where() function to select elements from your NumPy arrays based on a condition. You’ll learn how to perform various operations on those elements and even replace them with elements from a separate array or arrays.
• PythonistR: A Match Made in Data Heaven – In data science you’ll sometimes hear a debate between R and Python. Cosima says ‘why not choose both?’ She outlines a data pipeline that uses the best tool for each job.
• Why Not Comments – This post talks about why you might want to include information in your code comments about why you didn’t take a particular approach.

Discussion:

• Editors & IDEs – Real Python
• Visual Studio Code - Code Editing. Redefined
• Project Jupyter - Home
• vim online: welcome home
• iTerm2 - macOS Terminal Replacement

Projects:

• csv_trimming: Remove Common Ugliness From CSV Files
• django-tables2: Create HTML Tables in Django

Additional Links:

• virtualenv Lives! - Hynek Schlawack - 2014
• Production-ready Python Docker Containers with uv - Hynek Schlawack
• r-python-talk: 🦸🏼‍♀️ Contains material for talk on how to use Python and R together
• Download RStudio - Posit
• Logic for Programmers by Hillel Wayne - Leanpub

Level up your Python skills with our expert-led courses:

• Using Jupyter Notebooks
• Absolute vs Relative Imports in Python
• Advanced Python import Techniques

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What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast “ADHD For the Win!”

Chris struggled with productivity early in his career as a developer. He shares systems and strategies he’s discovered to harness the focusing power of ADHD.

We discuss time management, meetings, and maintaining productivity in a hectic world. Chris also shares resources for learning more about defining ADHD, self-evaluation, and how to keep getting things done.

This episode is sponsored by InfluxData.

Topics:

• 00:00:00 – Introduction
• 00:02:30 – Defining ADHD and how it aligns with coding
• 00:05:47 – Analogy for focus
• 00:06:51 – Can you sense the change in focus?
• 00:07:46 – The challenge of meetings
• 00:11:45 – Tips for managing time
• 00:15:44 – Capturing notes and defragging
• 00:18:48 – Sponsor: InfluxData
• 00:19:33 – Downtime and interruptions
• 00:25:26 – Remote work and focus
• 00:33:16 – Sitting still and meetings
• 00:37:39 – Video Course Spotlight
• 00:39:13 – Anything worth doing is worth doing poorly
• 00:47:36 – Prototypes and working on interesting things
• 00:50:26 – Deadlines and pomodoro timers
• 00:54:21 – Have your symptoms changed over time?
• 00:56:18 – Starting ADHDftw.com
• 00:59:12 – Decision to keep podcast episodes short
• 01:00:01 – Deciding on medication
• 01:02:02 – Resources available
• 01:03:29 – What motivates you to continue to learn programming?
• 01:04:06 – What do you want to learn next?
• 01:04:55 – What are other ways to follow your work online?
• 01:05:28 – Thanks and goodbye

Show Links:

• ADHD ftw! - Resources
• ADHD isn’t a deficit of attention (and doesn’t necessarily mean you’re hyperactive)
• Do I have ADHD?
• Snoot - Wikipedia
• Anything worth doing is worth doing poorly
• Getting stuff done with ADHD: defrag your notebook - ADHD ftw!
• Go Make Things - About
• Chris Ferdinandi ⚓️ (@cferdinandi@mastodon.social) - Fosstodon

Level up your Python skills with our expert-led courses:

• Creating PyQt Layouts for GUI Applications
• Build a GUI Calculator With PyQt and Python
• HTML and CSS Foundations for Python Developers

Support the podcast & join our community of Pythonistas

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a trio of articles by previous guest Stefanie Molin about Git pre-commit hooks. Across the series, she provides step-by-step instructions for building your own hooks, managing them, and learning how they operate.

We discuss the process of estimating software development projects. We dig into the art of “guesstimation,” rough calculation, and napkin math. Christopher shares his experience in agile scenarios and measuring projects by story counts.

We share several other articles and projects from the Python community, including a news roundup, 10 Python programming optimization techniques, and building a blog in Django using GraphQL & Vue. We also explore experimenting with Python’s preprocessor, a toolkit for writing UIs in PyScript, and a couple of projects for working with Django Admin.

This episode is sponsored by InfluxData.

Topics:

• 00:00:00 – Introduction
• 00:02:28 – Python Top Language of 2024
• 00:02:59 – Python Developers Survey 2023 Results
• 00:03:46 – How Pre-Commit Works
• 00:10:00 – Build a Blog Using Django, GraphQL, and Vue
• 00:13:38 – Sponsor: InfluxData
• 00:14:23 – 10 Python Programming Optimization Techniques
• 00:22:45 – Python’s Preprocessor
• 00:26:42 – Video Course Spotlight
• 00:28:16 – You don’t have to guess to estimate
• 00:44:18 – LTK is a little toolkit for writing UIs in PyScript
• 00:49:06 – django-admin-action-forms: Forms for Django Admin
• 00:52:09 – django-public-admin: A Public and Read-Only Django Admin
• 00:53:24 – Thanks and goodbye

News:

• Python Top Language of 2024 – “Python continues to cement its overall dominance, buoyed by things like popular libraries for hot fields such as A.I.” Read the article to see where other languages have placed.
• Python Developers Survey 2023 Results – Official Python Developers Survey 2023 Results by Python Software Foundation and JetBrains: more than 25k responses from almost 200 countries.

Show Links:

• How Pre-Commit Works – As a user of pre-commit hooks, do you know what happens when you run pre-commit install or why you have to run it in the first place? How does pre-commit actually work with Git? In this article, Stefanie takes you behind the scenes of how your pre-commit setup works.
• How to Set Up Pre-Commit Hooks - Stefanie Molin
• How to Create a Pre-Commit Hook - Stefanie Molin
• Build a Blog Using Django, GraphQL, and Vue – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use.
• 10 Python Programming Optimization Techniques – Optimization should be your last step, but once you’re there, just what can you do? This article covers ten different techniques that address memory size and code performance.
• Python’s Preprocessor – Every now and then you hear outrageous claims such as “Python has no preprocessor,” well it is there if you’re willing to dig deep enough. Learn how to hack Python’s compile step.

Discussion

• You Don’t Have to Guess to Estimate
• Habits of Great Software Engineers

Projects:

• pyscript/ltk: LTK Is a Little Toolkit for Writing UIs in PyScript
• django-admin-action-forms: Forms for Django Admin
• django-public-admin: A Public and Read-Only Django Admin

Additional Links:

• Python mmap: Doing File I/O With Memory Mapping – Real Python
• Caching in Python With lru_cache – Real Python
• Episode #128: Using a Memory Profiler in Python & What It Can Teach You
• Episode #172: Measuring Multiple Facets of Python Performance With Scalene – The Real Python Podcast
• software development - Why are estimates treated like deadlines? - Project Management Stack Exchange
• Software Estimation Without Guessing: Effective Planning in an Imperfect World by George Dinwiddie
• Guesstimation - Princeton University Press
• Anaconda Toolbox for Excel — Anaconda documentation
• Anaconda Code — Anaconda documentation
• PySheets - Spreadsheet UI for Python

Level up your Python skills with our expert-led courses:

• Python mmap: Doing File I/O With Memory Mapping
• Caching in Python With lru_cache
• How Python Manages Memory

Support the podcast & join our community of Pythonistas

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a pair of his recent Real Python video courses about exploring astronomy and astrophysics with Python. Throughout the courses, you’ll get to practice using a variety of data science libraries, such as NumPy, Matplotlib, pandas, pint, and Astropy.

We speak with Mannie Young who is the Organizing Committee Chair of PyCon Africa. Real Python is excited to be a contributing sponsor of this year’s conference. Mannie discusses reinvigorating a continent-spanning conference after a multiyear hiatus. He also talks about introducing Python to students and new developers across Africa through PyClubs, PyLadies, and PyData programs.

We share several other articles and projects from the Python community, including a news roundup, logging in Python, understanding operator precedence, reconciling why it only works on your machine, a fast way to create an HTML app, and a tool for deep inspection of Python objects.

This episode is sponsored by InfluxData.

Topics:

• 00:00:00 – Introduction
• 00:03:05 – PEP 750: Tag Strings for Domain-Specific Languages
• 00:05:19 – PEP 752: Package Repository Namespaces
• 00:07:32 – PyCon US 2024 Recap and Recording Release
• 00:08:01 – Logging in Python
• 00:14:57 – It Works on My Machine. Why?
• 00:17:33 – Python’s Operator Precedence
• 00:20:54 – Exploring Astrophysics in Python With pandas and Matplotlib
• 00:24:03 – Using Astropy for Astronomy With Python
• 00:26:37 – Sponsor: InfluxData
• 00:27:22 – fasthtml: The Fastest Way to Create an HTML App
• 00:32:50 – wat: Deep Inspection of Python Objects
• 00:38:08 – PyCon Africa 2024
• 00:40:47 – What goes into re-energizing a conference?
• 00:44:20 – Talks and speakers
• 00:46:58 – Video Course Spotlight
• 00:48:29 – How did you get involved?
• 00:52:19 – PyClubs and growing Python education
• 00:58:41 – What industries are using Python in Ghana?
• 01:00:20 – Sponsorship and support
• 01:01:51 – Travel in and outside the continent
• 01:04:23 – Call to action
• 01:05:05 – Thanks and goodbye

News:

• PEP 750: Tag Strings for Domain-Specific Languages (Added)
• PEP 752: Package Repository Namespaces (Added)
• PyCon US 2024 Recap and Recording Release – PyCon US 2024 had a record breaking attendance with over 2,700 in-person tickets sold. This article is a recap from the conference runners and links to all the available recordings.

Show Links:

• Logging in Python – If you use Python’s print() function to get information about the flow of your programs, then logging is the natural next step for you. This tutorial will guide you through creating your first logs and show you ways to curate them to grow with your projects.
• It Works on My Machine. Why? – A list of things to check when something works on your computer but not on someone else’s.
• Python’s Operator Precedence – Stephen uses a story-telling style to explain how operator precedence works in Python.
• Exploring Astrophysics in Python With pandas and Matplotlib – This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint.
• Using Astropy for Astronomy With Python – This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star.

Projects:

• fasthtml: The Fastest Way to Create an HTML App
• wat: Deep Inspection of Python Objects

Additional Links:

• PyCon Africa 2024 - Home
• Announcing PyCon Africa 2024 Blog: A Return to Accra and a Look Ahead
• Episode #65: Expanding the International Python Community With the PSF
• Django Girls - Start your journey with programming
• PyLadies Ghana - Python Ghana’s Blog
• PyData Ghana - Python Ghana’s Blog
• PyClubs - Home
• Quiz: Logging in Python
• Mannie Young - LinkedIn
• Mannie Young (@mawy_7) - X

Level up your Python skills with our expert-led courses:

• Using Astropy for Astronomy With Python
• Sorting Dictionaries in Python: Keys, Values, and More
• Exploring Astrophysics in Python With pandas and Matplotlib

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Are you interested in learning robotics with Python? Can physical electronics-based projects grow a child’s interest in coding? This week on the show, we speak with author Marwan Alsabbagh about his book “Build Your Own Robot - Using Python, CRICKIT, and Raspberry Pi.”

Marwan discusses his two conferences talks about building electronics projects with his children. He provides advice on equipment and techniques to make learning Python engaging.

We explore his robotics project and the literal balancing act of designing a robot around the Raspberry Pi. Marwan shares his successes and disappointments while working to incorporate computer vision, joystick controls, and voice commands.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:14 – How did you get started with Python and electronics?
• 00:04:27 – Snow globe intruder alert system
• 00:06:57 – Things to keep in mind with a child
• 00:12:50 – Challenges in teaching a child Python
• 00:16:34 – Sponsor: Mailtrap
• 00:17:11 – What are other projects you’ve tried?
• 00:21:12 – Powering the robot project
• 00:24:56 – Putting together the robot librarian talk
• 00:29:47 – Was there any friction teaching kids robotics?
• 00:32:47 – Adding the complexity of a Raspberry Pi
• 00:38:27 – Video Course Spotlight
• 00:39:48 – Hardware components of the robot
• 00:41:51 – Thinking about access to the equipment
• 00:45:37 – Assembling the robot project?
• 00:49:14 – Various control systems
• 00:54:42 – What experience level is required with Python?
• 00:55:40 – What concepts were you excited to share?
• 00:57:59 – Do you think Python is a good language for robotics?
• 00:59:21 – MicroPython Cookbook
• 01:00:07 – What are projects you tried that didn’t work out?
• 01:03:01 – What are you excited about in the world of Python?
• 01:04:04 – What do you want to learn next?
• 01:04:56 – How can people follow your work online?
• 01:05:19 – Thanks and goodbye

Show Links:

• Build Your Own Robot - Using Python, CRICKIT, and Raspberry PI
• Snow globe intruder alert system
• Snow globe intruder alert system - Marwan Alsabbagh - PyLondinium18 - YouTube
• Adafruit Industries, Unique & fun DIY electronics and kits
• Nina Zakharenko - Keynote - PyCon 2019 - YouTube
• Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming
• Episode #161: Resources and Advice for Building CircuitPython Projects
• Episode #75: Building With CircuitPython & Constraints of Python for Microcontrollers
• MicroPython Cookbook: Marwan Alsabbagh - Amazon.com: Books
• WebAssembly
• </> htmx - high power tools for html
• Marwan Alsabbagh - personal website
• marwano (Marwan Alsabbagh) - GitHub

Level up your Python skills with our expert-led courses:

• Using Pygame to Build an Asteroids Game in Python
• Debugging in Python With pdb
• Using Python's assert to Debug and Test Your Code

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What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss Joshua Cook’s recent article “How I Use Python to Organize My Data Analyses.” The article covers how his process for building data analysis projects has evolved and now incorporates modern Python packaging techniques.

Christopher shares his recent video course on grouping real-world data with pandas. The course offers a quick refresher before digging into how to use pandas GroupBy to manipulate, transform, and summarize data.

We also share several other articles and projects from the Python community, including a news roundup, working with JSON data in Python, running an Asyncio event loop in a separate thread, knowing the why behind a system’s code, a retro game engine for Python, and a project for vendorizing packages from PyPI.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – Setuptools Breaks Things, Then Fixes Them
• 00:04:57 – PEP 751: A File Format to List Python Dependencies
• 00:07:04 – Python 3.13.0 Release Candidate 1 Released
• 00:07:15 – Python Insider: Python 3.12.5 released
• 00:07:22 – Django 5.1 released - Django Weblog
• 00:07:27 – Django security releases issued: 5.0.8 and 4.2.15
• 00:07:49 – How I Use Python to Organize My Data Analyses
• 00:13:45 – Sponsor: Mailtrap
• 00:14:21 – pandas GroupBy: Grouping Real World Data in Python
• 00:20:33 – Working With JSON Data in Python
• 00:25:01 – Asyncio Event Loop in Separate Thread
• 00:30:33 – Video Course Spotlight
• 00:31:47 – Habits of great software engineers
• 00:49:17 – pyxel: A Retro Game Engine for Python
• 00:52:36 – python-vendorize: Vendorize Packages From PyPI
• 00:54:18 – Thanks and goodbye

News:

• Setuptools Breaks Things, Then Fixes Them – This post is Bite Code’s monthly summary, but the lead story happened just days ago. In line with a 7 year old deprecation, setuptools finally removed the ability to call its test command. Many packages promptly broke. The following day the change was undone.
• PEP 751: A File Format to List Python Dependencies for Installation Reproducibility (New) – This PEP proposes a new file format for dependency specification to enable reproducible installation in a Python environment.
• Python 3.13.0 Release Candidate 1 Released
• Python Insider: Python 3.12.5 released
• Django 5.1 released - Django Weblog
• Django security releases issued: 5.0.8 and 4.2.15 - Django Weblog

Show Links:

• How I Use Python to Organize My Data Analyses – This is a description of how Joshua uses Python in a package-centric way to organize his approach to data analyses. This is a system he has evolved while working on his computational biology Ph.D. and working in industry.
• pandas GroupBy: Grouping Real World Data in Python – In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs.
• Working With JSON Data in Python – In this tutorial, you’ll learn how to read and write JSON-encoded data in Python. You’ll begin with practical examples that show how to use Python’s built-in “json” module and then move on to learn how to serialize and deserialize custom data.
• 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.

Discussion:

• Habits of great software engineers

Projects:

• pyxel: A Retro Game Engine for Python
• python-vendorize: Vendorize Packages From PyPI

Additional Links:

• Everyday Project Packaging With pyproject.toml – Real Python
• Packaging Your Python Code With pyproject.toml - Complete Code Conversation - YouTube
• Episode #197: Using Python in Bioinformatics and the Laboratory – The Real Python Podcast

Level up your Python skills with our expert-led courses:

• Everyday Project Packaging With pyproject.toml
• Working With JSON in Python
• pandas GroupBy: Grouping Real World Data in Python

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What hurdles must be cleared when starting an international organization? How do you empower others in a community by sharing responsibilities? This week on the show, we speak with Jay Miller about Black Python Devs.

Jay shares how the idea of forming a community began through attending conferences. They wanted to welcome more black developers into the Python community. We discuss the introduction of Black Python Devs as part of their PyCon 2024 keynote presentation.

Jay explains working with a few key people to build the group’s foundations. They talk about the difficulty of letting other people share in the responsibilities and ownership as the membership grew. We also discuss the advantages of partnering with a non-profit organization.

This episode is sponsored by InfluxData.

Topics:

• 00:00:00 – Introduction
• 00:02:50 – PyCon 2024 Keynote
• 00:06:02 – New role at Aiven
• 00:11:32 – Nobody knows what Dev Rel is
• 00:19:43 – Podcasting about productivity
• 00:24:12 – Sponsor: InfluxData
• 00:24:57 – Starting Black Python Devs
• 00:33:11 – Distinct perspectives and problems
• 00:37:10 – Partnering with Gnome Foundation
• 00:40:31 – What were hurdles in starting Black Python Devs?
• 00:45:31 – Video Course Spotlight
• 00:47:01 – What do you wish you knew before you started?
• 00:50:56 – What’s your latest win?
• 00:53:28 – Helping people prepare for jobs and new roles
• 00:58:03 – What’s your call to action?
• 01:00:26 – What are you excited about in the world of Python?
• 01:03:48 – How do you stay motivated to keep learning Python?
• 01:06:19 – What do you want to learn next?
• 01:09:02 – How can people follow your work online?
• 01:11:02 – Thanks and goodbye

Show Links:

• Black Python Devs - Home
• PyCon 2024 Keynote Speaker - Jay Miller - YouTube
• PyCon 2024 Keynote Speaker - Sumana Harihareswara - YouTube
• Conduit - Relay FM
• Aiven - Your Trusted Data & AI Platform
• Abigail Mesrenyame Dogbe Honored with Inaugural Outstanding PyLady Award
• Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming – The Real Python Podcast
• Black Python Devs Join the GNOME Foundation Nonprofit Umbrella – The GNOME Foundation
• Applying for a Hacker Initiative Grant With Bill Pollock of No Starch Press – The Real Python Podcast
• Jay Miller - Personal Website
• kjaymiller - Jay Miller’s GitHub
• Jay Miller (@kjaymiller@mastodon.social) - Fosstodon
• Jay Miller - LinkedIn
• Render Engine - read the docs

Level up your Python skills with our expert-led courses:

• Sneaky REST APIs With Django Ninja
• Unleashing the Power of the Console With Rich
• Interacting With REST APIs and Python

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How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django’s battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent tutorial for building GraphQL APIs in Django using the Python library Strawberry. The tutorial digs into creating a project, defining models, and creating GraphQL queries and mutations using Strawberry.

We discuss a blog post from Nat Bennet titled “Why do prototypes suck?” We dig into the common pitfalls of building prototypes and the misconceptions between developers and end users.

We also share several other articles and projects from the Python community, including a news roundup, using HTMX with FastAPI, creating an unbelievably stupid airline Wi-Fi package, extracting wisdom from conference videos, writing pixel images to the terminal, and a macOS app for Jupyter Notebooks.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:27 – Python 3.13.0 Beta 4 Released
• 00:03:15 – Using HTMX With FastAPI
• 00:09:51 – Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights
• 00:13:37 – Sponsor: Mailtrap
• 00:14:13 – “Extracting Wisdom” From Conference Videos
• 00:22:34 – Developing GraphQL APIs in Django With Strawberry
• 00:30:01 – Video Course Spotlight
• 00:31:33 – Why do prototypes suck?
• 00:42:53 – Satyrn: macOS App for Jupyter Notebooks
• 00:46:41 – rich-pixels: A Rich-compatible library for writing pixel images
• 00:48:23 – Thanks and goodbye

News:

• Python 3.13.0 Beta 4 Released

Topics:

• Using HTMX With FastAPI – This tutorial looks at how use HTMX with FastAPI by creating a simple todo web app and deploying it on Render.
• Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights – Deep in a need to procrastinate on a flight between London and San Francisco, Robert discovered that changing his name on an airline’s frequent flyer account was free over the plane’s WiFi. What’s a developer to do? Work on their tickets? No, create an entire TCP/IP protocol using this loophole. The result is the PySkyWiFi package.
• “Extracting Wisdom” From Conference Videos – There are so many conferences and so many videos, you can’t possibly watch them all. This post shows you how to extract information to summarize a talk so you can quickly decide what you want to watch.
• Developing GraphQL APIs in Django With Strawberry – This tutorial details how to integrate GraphQL with Django using Strawberry.

Discussion:

• Why do prototypes suck?

Project:

• Satyrn: macOS App for Jupyter Notebooks
• darrenburns/rich-pixels: A Rich-compatible library for writing pixel images and ASCII art to the terminal.

Additional Links:

• </> htmx - high power tools for html
• Using FastAPI to Build Python Web APIs – Real Python
• Ollama
• fabric: An open-source framework for augmenting humans using AI
• A modern GraphQL library for Python - 🍓 Strawberry GraphQL
• Satyrn Discord

Level up your Python skills with our expert-led courses:

• Sneaky REST APIs With Django Ninja
• Building a URL Shortener With FastAPI and Python
• Python REST APIs With FastAPI

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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.

Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library.

We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:00 – Michael’s background in open source
• 00:04:07 – Rich’s background in open source
• 00:05:27 – Advice for someone starting out
• 00:08:55 – What do you mean by the term “display” table
• 00:11:32 – What components were missing from other tables?
• 00:13:31 – Using examples to explain features
• 00:16:09 – Why was there an absence of this functionality in Python?
• 00:19:35 – A progressive approach and the grammar of tables
• 00:21:26 – Sponsor: Mailtrap
• 00:22:01 – The design philosophy of great tables
• 00:25:31 – Nanoplots, spark lines, and column spanners
• 00:27:06 – Building a gallery of examples
• 00:28:56 – Heat mapping cells and automatically adjusting text color
• 00:32:54 – Output formats for the tables
• 00:34:46 – Building in accessibility
• 00:36:55 – Dependencies
• 00:37:42 – What is the common workflow?
• 00:41:39 – Video Course Spotlight
• 00:43:15 – Adding graphics
• 00:46:41 – Using a table contest to get examples
• 00:49:47 – quartodoc and documenting the project
• 00:55:00 – Tidy Tuesday and data science community
• 01:00:29 – What are you excited about in the world of Python?
• 01:03:46 – What do you want to learn next?
• 01:08:05 – How can people follow the work you do online?
• 01:09:57 – Thanks and goodbye

Show Links:

• Great Tables - Intro
• Examples – great_tables
• great-tables: Make awesome display tables using Python. - GitHub
• siuba: Python library for using dplyr like syntax with pandas and SQL
• The Design Philosophy of Great Tables – great_tables
• Richard Iannone - Using Great Tables to Make Presentable Tables in Python - YouTube
• Evaluation of the players of #LigaEndesa this week in Europe - Great Tables Example - X
• quartodoc: Generate API documentation with quarto
• Tidy Tuesday R Screencasts - YouTube
• Polars — DataFrames for the new era
• narwhals-dev/narwhals: Lightweight and extensible compatibility layer between dataframe libraries!
• A Grammar of Graphics for Python – plotnine 0.13.6
• Richard Iannone - GitHub
• Michael Chow - GitHub)
• Richard Iannone - LinkedIn
• Michael Chow - LinkedIn

Level up your Python skills with our expert-led courses:

• Using Jupyter Notebooks
• pandas GroupBy: Grouping Real World Data in Python
• Graph Your Data With Python and ggplot

Support the podcast & join our community of Pythonistas

What are discrete optimization problems? How do you solve them with constraint programming in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects

Christopher discusses an article about constraint programming using Python. He describes the fundamentals and how the problems resemble logic problems you may have experienced in school. The article shows how to solve a weekly work scheduling problem using the open-source CP-SAT package.

We discuss Leodanis Pozo Ramos’s recent tutorial, “Python’s Built-in Functions: A Complete Exploration.” These functions are available for use directly in your code without importing.

We also share several other articles and projects from the Python community, including a news roundup, spotting ships with satellites, grappling with Apple’s App Store rejecting Python applications, considering changes to Python’s security model, discussing pivoting from one development path to another, prettifying Jinja and Django templates, and generating static sites with Python.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:35 – Polars 1.0 Released
• 00:03:26 – Psycopg 3.2 Released
• 00:04:06 – Django security releases issued: 5.0.7 and 4.2.14
• 00:04:40 – PyBay 2024 Call for Proposals
• 00:05:16 – Python’s Built-in Functions: A Complete Exploration
• 00:12:10 – Satellites Spotting Ships
• 00:16:02 – Sponsor: Sentry
• 00:17:09 – Python Grapples With Apple App Store Rejections
• 00:20:27 – Python’s Security Model After the xz-utils Backdoor
• 00:25:38 – Video Course Spotlight
• 00:26:56 – Constraint Programming Using CP-SAT and Python
• 00:31:40 – Any Web Devs Successfully Pivoted to AI/ML Development?
• 00:43:12 – aurora: Static Site Generator Implemented in Python
• 00:45:14 – Running Prettier Against Django or Jinja Templates
• 00:46:58 – Thanks and goodbye

News:

• Polars 1.0 Released
• Psycopg 3.2 Released
• Django security releases issued: 5.0.7 and 4.2.14
• PyBay 2024 Call for Proposals

Show Links:

• Python’s Built-in Functions: A Complete Exploration – In this tutorial, you’ll learn the basics of working with Python’s numerous built-in functions. You’ll explore how you can use these predefined functions to perform common tasks and operations, such as mathematical calculations, data type conversions, and string manipulations.
• Satellites Spotting Ships – Umbra Space has released a data set consisting of satellite based radar images of shipping. This article from Mark shows you how to grab the data, visualize, and annotate it.
• Python Grapples With Apple App Store Rejections – A string that is part of the urllib parser module in Python references a scheme for apps that use the iTunes feature to install other apps, which is disallowed. Auto scanning by Apple is rejecting any app that uses Python 3.12 underneath. A solution has been proposed for Python 3.13.
• Python’s Security Model After the xz-utils Backdoor – The backdoor introduced to the xz-utils compression project through social engineering was one of the topics at the Python Language Summit. Participants discussed what can be done to prevent similar social engineering attacks on the Python source.
• Constraint Programming Using CP-SAT and Python – Constraint programming is the process of looking for solutions based on a series of restrictions, like employees over 18 who have worked the cash before. This article introduces the concept and shows you how to use open source libraries to write constraint solving code.

Discussion:

• Any Web Devs Successfully Pivoted to AI/ML Development?

Projects:

• aurora: Static Site Generator Implemented in Python
• Running Prettier Against Django or Jinja Templates – “Prettier” is a JavaScript based linting tool for templates. For folks not familiar with the world of npm, it can be a bit daunting to get it going. Simon fiddled with it so you don’t have to and posted how he got it working on his system.

Additional Links:

• Episode #209: Python’s Command-Line Utilities & Music Information Retrieval Tools – The Real Python Podcast
• Python Module Index — Python 3.12.4 documentation
• Built-in Functions — Python 3.12.4 documentation
• Briefcase— BeeWare
• Ask HN: What’s Prolog like in 2024? - Hacker News
• Episode #199: Leveraging Documents and Data to Create a Custom LLM Chatbot – The Real Python Podcast
• PEP 730 – Adding iOS as a supported platform | peps.python.org

Level up your Python skills with our expert-led courses:

• Parallel Iteration With Python's zip() Function
• Python Inner Functions
• Jinja Templating

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Have you wondered about graph theory and how to start exploring it in Python? What resources and Python libraries can you use to experiment and learn more? This week on the show, former co-host David Amos returns to talk about what he’s been up to and share his knowledge about graph theory in Python.

David started a Ph.D. program studying mathematics, with a focus on graph theory. Though life interrupted his pursuit after three years, he is still passionate about the subject. He’s been using these skills to create documentation and teach users as part of RelationalAI’s education team.

David has also been exploring the Julia programming language. He wrote about it on his blog, created videos, and started a podcast on the topic. He shares his excitement about learning new techniques from different languages and how these ideas can enhance your coding in Python.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:09 – What have you been up to?
• 00:03:31 – Exploring the Julia language
• 00:07:24 – Where do you see Julia being used?
• 00:10:17 – Cross pollination of languages
• 00:12:45 – Connecting with RelationalAI
• 00:16:33 – Sponsor: Sentry
• 00:17:42 – Digging into graph theory
• 00:21:54 – Edges as connections
• 00:24:55 – Defining terms
• 00:31:30 – Storing graph information
• 00:41:55 – Applications once the graph is built
• 00:49:07 – Video Course Spotlight
• 00:50:40 – Additional resources to learn more
• 00:53:59 – What are you excited about in the world of Python?
• 00:58:05 – What do you want to learn next?
• 01:00:25 – How can people follow your work online?
• 01:02:55 – Thanks and goodbye

Show Links:

• RelationalAI - a knowledge graph coprocessor for your data cloud.
• The Julia Programming Language
• Talk Julia - YouTube
• Five Minutes To Julia. David Amos - Medium Member Link
• Five Minutes To Julia. David Amos - Medium Friend Link
• Getting Started with RelationalAI - RelationalAI Docs
• Example Notebooks - RelationalAI Docs
• The Hunt for the Missing Data Type - Hillel Wayne
• Introduction to Graph Theory: Richard J Trudeau
• The Fascinating World of Graph Theory: Arthur Benjamin, Gary Chartrand, Ping Zhang
• NetworkX — NetworkX documentation
• Embeddings and Vector Databases With ChromaDB – Real Python
• Episode #199: Leveraging Documents and Data to Create a Custom LLM Chatbot – The Real Python Podcast
• Graph Theory With Python - David’s YouTube Playlist
• David Amos (@somacdivad@hachyderm.io) - Hachyderm.io
• David Amos - LinkedIn
• David Amos – Medium

Level up your Python skills with our expert-led courses:

• Using Jupyter Notebooks
• Defining Python Constants for Code Maintainability
• Python Basics: Setting Up Python

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Does Python round numbers the same way you learned back in math class? You might be surprised by the default method Python uses and the variety of ways to round numbers in Python. Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his recent video course, “Rounding Numbers in Python.” He covers rounding bias and how to avoid introducing it into your dataset. We dig into the various rounding strategies and how to implement them in Python.

We also share several other articles and projects from the Python community, including a news roundup, a fast Python linter for error-free and maintainable code, the decline of the user interface, more thoughts on Python in Excel, a discussion about calendar versioning for Python, a financial database as a Python module, and a project to prettify the colors of your terminal user interfaces.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:06 – NumPy 2.0.0 Release Notes
• 00:02:52 – Python 3.13.0 beta 3 released
• 00:03:05 – Announcing the PSF Board Candidates for 2024!
• 00:03:27 – Prohibiting Outlook Email Domains
• 00:04:31 – Ruff: A Python Linter for Error-Free and Maintainable Code
• 00:09:31 – Sponsor: Sentry
• 00:10:35 – The Decline of the User Interface
• 00:19:14 – My Thoughts on Python in Excel
• 00:26:30 – Rounding Numbers in Python
• 00:30:53 – Video Course Spotlight
• 00:32:13 – PEP 2026: Calendar Versioning for Python
• 00:42:37 – Financial Database as a Python Module
• 00:45:34 – prettypretty: Build Awesome Terminal User Interfaces
• 00:47:48 – Thanks and goodbye

News:

• NumPy 2.0.0 Release Notes — NumPy v2.0 Manual – The long awaited 2.0 release of NumPy landed this week. Not all the docs are up to date yet, but this final draft of the release notes shows you what is included.
• Python Insider: Python 3.13.0 beta 3 released
• Python Software Foundation News: Announcing the PSF Board Candidates for 2024!
• Prohibiting Outlook Email Domains – Due to an inordinate amount of bot accounts coming from outlook.com and hotmail.com, PyPI has disallowed new account sign-ups with email addresses from these domains.

Show Links:

• Ruff: A Python Linter for Error-Free and Maintainable Code – Ruff is an extremely fast, modern linter with a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for other linting and formatting tools, like Pylint, isort, and Black. It’s no surprise it’s quickly becoming one of the most popular Python linters.
• The Decline of the User Interface – “Software has never looked cooler, but user interface design and user experience have taken a sharp turn for the worse.”
• My Thoughts on Python in Excel – Microsoft’s new Python in Excel functionality was released almost a year ago. Having now had time to play with it, Felix gives his take.
• Rounding Numbers in Python – In this video course, you’ll learn about the mistakes you might make when rounding numbers and how to best manage or avoid them. It’s a great place to start for the early to intermediate Python developer who’s interested in using Python for finance, data science, or scientific computing.

Discussion:

• PEP 2026: Calendar Versioning for Python – This PEP proposes updating the versioning scheme for Python to include the calendar year. This aims to make the support lifecycle clear by making it easy to see when a version was first released, and easier to work out when it will reach end of life (EOL).
• Associated discussion
• Semantic Versioning 2.0.0 - Semantic Versioning
• Calendar Versioning — CalVer

Projects:

• FinanceDatabase: Financial Database as a Python Module
• prettypretty: Build Awesome Terminal User Interfaces

Additional Links:

• The Humane Interface - Wikipedia
• Python Resources for working with Excel - Working with Excel Files in Python
• Episode #186: Exploring Python in Excel – The Real Python Podcast
• Cash rounding - Wikipedia

Level up your Python skills with our expert-led courses:

• Python Basics: Numbers and Math
• Rounding Numbers in Python
• Editing Excel Spreadsheets in Python With openpyxl

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What techniques go into synthesizing a guitar sound in Python? What higher-level programming and Python concepts can you practice while building advanced projects? This week on the show, we talk with Real Python author and core team member Bartosz Zaczyński about his recent step-by-step project, Build a Guitar Synthesizer: Play Musical Tablature in Python.

Bartosz shares how he had built an early prototype of the guitar synth using a different language. He describes recreating the Karplus-Strong algorithm in Python to create the plucked guitar sounds. We discuss teaching advanced Python concepts while guiding readers through constructing a complete project.

We also dig into reading and writing WAV files in Python, using Spotify’s pedalboard library, implementing Poetry in a project, and designing a file format for guitar tablature.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:04 – Reading and writing WAV files in Python
• 00:04:48 – What did you want to learn about the topic?
• 00:08:02 – Finding new ways to explain Python concepts
• 00:09:52 – Turning audio into plots and visualizations
• 00:12:11 – Using Python for synthesis and the guitar synth project
• 00:13:19 – What is your music background?
• 00:18:36 – Sponsor: Mailtrap
• 00:19:11 – First prototype of the project
• 00:22:23 – What does the project cover?
• 00:25:08 – Audio examples of the output
• 00:26:31 – Digging into the algorithm
• 00:31:40 – Convolution reverb
• 00:34:15 – Physical modeling and pedalboard
• 00:38:08 – Video Course Spotlight
• 00:39:31 – What Python concepts are you practicing?
• 00:41:11 – Using Python poetry
• 00:43:00 – Why use YAML for the TAB files?
• 00:46:13 – What does it mean to be a polyglot programmer?
• 00:48:37 – Potential upcoming Real Python resources
• 00:52:10 – How can people follow your work online?
• 00:52:30 – What do you want to learn next?
• 00:54:56 – Thanks and goodbye

Show Links:

• Build a Guitar Synthesizer: Play Musical Tablature in Python – Real Python
• Reading and Writing WAV Files in Python – Real Python
• Episode #200: Avoiding Error Culture and Getting Help Inside Python – The Real Python Podcast
• ActionScript - Wikipedia
• Apache Flex® - Home Page
• Too Many Zooz - Official Website
• Watch Reverb - Watch the Sound With Mark Ronson (Season 1, Episode 3) - Apple TV+
• Longest Reverb In the World - Inchindown - YouTube
• Karplus–Strong string synthesis - Wikipedia
• Physical modeling synthesis - Wikipedia
• pedalboard: 🎛 🔊 A Python library for audio - Spotify
• Poetry - Python dependency management and packaging made easy
• Dependency Management With Python Poetry – Real Python
• YAML: The Missing Battery in Python – Real Python
• Guitar Tabs with Rhythm - Songsterr
• It Starts with Food - The Whole30® Program
• Bartosz Zaczyński - LinkedIn

Level up your Python skills with our expert-led courses:

• Simulating Real-World Processes in Python With SimPy
• Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn
• Playing and Recording Sound in Python

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What are the built-in Python modules that can work as useful command-line tools? How can these tools add more functionality to Windows machines? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article by Trey Hunner about Python’s extensive collection of command-line utilities? The piece digs into general-purpose tools that format JSON data or start a simple web server and additional utilities for working with your Python code from the terminal.

We cover a set of Jupyter Notebooks for teaching and learning the art of music processing and Music Information Retrieval (MIR). The notebooks are resources for working through the textbook, “Fundamentals of Music Processing: Audio, Analysis, Algorithms, Applications.”

We also share several other articles and projects from the Python community, including a news roundup, a discussion of CRUD operations, a description of Python’s built-in bytes sequence, favorite essays on development and programming, Python resources for working with Excel, and a project for creating finite state machines in Python.

This episode is sponsored by APILayer.

Topics:

• 00:00:00 – Introduction
• 00:02:21 – Python 3.12.4 Released
• 00:02:52 – Python 3.13.0 Beta 2 Released
• 00:03:01 – PEP 712 Rejected
• 00:04:18 – Django in Action - Mr. Trudeau’s Book has Launched!
• 00:06:23 – What Are CRUD Operations?
• 00:10:12 – Python’s Many Command-Line Utilities
• 00:14:04 – Sponsor: APILayer
• 00:14:55 – Notebooks for Fundamentals of Music Processing
• 00:22:55 – bytes: The Lesser-Known Python Built-in Sequence
• 00:26:57 – Video Course Spotlight
• 00:28:34 – Essays on Programming I Think About a Lot
• 00:41:28 – Python Resources for Working With Excel
• 00:46:13 – Python Finite State Machines Made Easy
• 00:50:10 – Thanks and goodbye

News:

• Python 3.12.4 Released – See the full list of changes in this release
• Python 3.13.0 Beta 2 Released
• PEP 712 Rejected – This Python Enhancement Proposal “Adding a ‘converter’ parameter to dataclasses.field” was determined to have an insufficient number of use cases.
• Django in Action

Show Links:

• What Are CRUD Operations? – CRUD operations are the cornerstone of application functionality. Whether you access a database or interact with a REST API, you usually want to create, retrieve, update, and delete data. In this tutorial, you’ll explore how CRUD operations work in practice.
• Python’s Many Command-Line Utilities – This article describes every command-line tool included with Python, each of which can be run with python -m module_name.
• Notebooks for Fundamentals of Music Processing – This is a collection of Python Notebooks for teaching and learning the fundamentals of music processing. Examples include illustrations, sound samples, math, and more.
• bytes: The Lesser-Known Python Built-in Sequence – The bytes data type looks a bit like a string, but it isn’t a string. This article explores it and also looks at the main Unicode encoding, UTF-8

Discussion:

• Essays on Programming I Think About a Lot – A collection of essays on software from a variety of sources. Content includes how to choose your tech stack, products, abstractions, and more.
• Falsehoods programmers believe about time - Infinite Undo
• Falsehoods programmers believe about email
• Falsehoods programmers believe about geography – Thias の blog
• awesome-falsehood: 😱 Falsehoods Programmers Believe In

Projects:

• Python Resources for Working With Excel
• Python Finite State Machines Made Easy

Additional Links:

• SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files – Real Python
• Unicode in Python: Working With Character Encodings – Real Python
• Fundamentals of Music Processing - Editions of the Book
• Editing Excel Spreadsheets in Python With openpyxl – Real Python
• My thoughts on Python in Excel
• The Microsoft Excel superstars throw down in Vegas

Level up your Python skills with our expert-led courses:

• Playing and Recording Sound in Python
• Binary, Bytes, and Bitwise Operators in Python
• Unicode in Python: Working With Character Encodings

Support the podcast & join our community of Pythonistas

How do you find the most interesting or suspicious points within your data? What libraries and techniques can you use to detect these anomalies with Python? This week on the show, we speak with author Brett Kennedy about his book “Outlier Detection in Python.”

Brett describes initially getting involved with detecting outliers in financial data. He discusses various applications and techniques in security, manufacturing, quality assurance, and fraud. We also dig into the concept of explainable AI and the differences between supervised and unsupervised learning.

This episode is sponsored by APILayer.

Topics:

• 00:00:00 – Introduction
• 00:01:56 – Describing the book
• 00:03:22 – How did you get involved in outlier detection?
• 00:06:50 – Initially looking at the data to spot errors
• 00:08:22 – Amount of fraud and financial errors
• 00:09:50 – Understanding the nature of the outliers
• 00:12:15 – Industries that would be interested in detection
• 00:18:21 – Sponsor: APILayer.com
• 00:19:15 – Who is the intended audience for the book?
• 00:22:16 – Differences between supervised vs unsupervised learning
• 00:25:48 – Autonomous vehicles detecting anomalous imagery
• 00:29:08 – What is explainable AI?
• 00:36:21 – Video Course Spotlight
• 00:37:43 – Detecting an outlier across multiple columns
• 00:44:32 – Detection of LLM and bot activity
• 00:49:49 – Proving you are a human checkbox
• 00:52:25 – What are Python libraries for outlier detection?
• 00:53:57 – Creating synthetic data to work through examples
• 00:57:10 – Tools developed and described in the book
• 01:01:29 – How to find the book
• 01:02:27 – What are you excited about in the world of Python?
• 01:04:55 – What do you want to learn next?
• 01:05:52 – How can people follow your work online?
• 01:06:16 – Thanks and goodbye

Show Links:

• Outlier Detection in Python
• Episode #169: Improving Classification Models With XGBoost – The Real Python Podcast
• XGBoost Documentation — xgboost 1.7.6 documentation
• SHAP (SHapley Additive exPlanations) Documentation
• I’m a teacher and this is the simple way I can tell if students have used AI to cheat in their essays - Daily Mail Online
• pyod: A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
• DeepOD: Deep learning-based outlier/anomaly detection
• scikit-learn: machine learning in Python — scikit-learn 1.5.0 documentation
• DataConsistencyChecker: A Python tool to examine datasets for consistency
• Brett Kennedy - LinkedIn
• Brett-Kennedy - GitHub

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Using k-Nearest Neighbors (kNN) in Python
• Starting With Linear Regression in Python

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How do you effectively break a software problem into individual steps? What are signs you’re writing overly clever code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an article about de-warping images of book pages. We both found the piece a good study on decomposing a complex software problem.

Christopher discusses an article titled “Clever code is probably the worst code you could write.” Early in a programming career, it’s easier to write complex and difficult-to-read code. The real challenge is progressing towards writing clearer and readable code.

We also share several other articles and projects from the Python community, including a news roundup, what the __pycache__ folder is for in Python, what’s new in Django 5.1, a discussion about software engineering hiring and firing, a project for setting up repeated tasks, and a simple way to create reusable template components in Django.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – PEP 667: Consistent Views of Namespaces (Accepted)
• 00:03:08 – PEP 649 Re-targeted to 3.14
• 00:03:50 – Untold Stories From 6 Years Working on Python Packaging
• 00:04:38 – What Is the __pycache__ Folder in Python?
• 00:09:57 – Sponsor: Sentry
• 00:11:04 – What’s New in Django 5.1
• 00:17:48 – Page Dewarping
• 00:26:55 – Video Course Spotlight
• 00:28:26 – Clever Code Is Probably the Worst Code You Could Write
• 00:33:19 – Software Engineering Hiring and Firing
• 00:51:22 – Metronomes: An Easy Way to Set Up Regular Tasks
• 00:52:26 – django-web-components: Create reusable template components in Django
• 00:54:03 – Thanks and goodbye

News:

• PEP 667: Consistent Views of Namespaces (Accepted)
• PEP 649 Re-targeted to 3.14 – Python Enhancement Proposal 649: Deferred Evaluation Of Annotations Using Descriptors has been re-targeted to the Python 3.14 release
• Untold Stories From 6 Years Working on Python Packaging – Sumana gave the closing keynote address at PyCon US this year and this posting shares all the links and references from the talk.

Show Links:

• What Is the __pycache__ Folder in Python? – In this tutorial, you’ll explore Python’s __pycache__ folder. You’ll learn about when and why the interpreter creates these folders, and you’ll customize their default behavior. Finally, you’ll take a look under the hood of the cached .pyc files.
• What’s New in Django 5.1 – Django 5.1 has gone alpha so the list of features targeting this release has more or less solidified. This article introduces you to what is coming in Django 5.1.
• Page Dewarping – This article shows the techniques behind a page flattening algorithm. It starts with images of a book’s page which are curled from the spine of the book, and creates a resulting PDF that is a flat version.
• Clever Code Is Probably the Worst Code You Could Write – When you come across a clever bit of code, it is hard not to admire it, but often times, clear, readable code is the hardest code to write.

Discussion:

• Software Engineering Hiring and Firing – This article is a deep dive on the hiring and firing practices in the software field, and unlike most articles focuses on senior engineering roles. It isn’t a “first job” post, but a “how the decision process works” article.

Projects:

• Metronomes: An Easy Way to Set Up Regular Tasks
• django-web-components: A simple way to create reusable template components in Django

Level up your Python skills with our expert-led courses:

• Getting Started With Django: Building a Portfolio App
• Efficient Iterations With Python Iterators and Iterables
• For Loops in Python (Definite Iteration)

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How do you start adding unit tests to your Python code? Can the built-in unittest framework cover most or all of your needs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We dig into a recent tutorial by Leodanis Pozo Ramos about writing unit tests using Python’s unittest. The tutorial covers organizing your tests, exploring assert methods, creating test fixtures, and debugging failing tests.

We explore a collection of Python data visualizations and tutorials from the Python Graph Gallery. The website features hundreds of charts and graphs built using popular plotting libraries. Each chart type features a foundational tutorial that introduces the structure and application.

We also share several other articles and projects from the Python community, including a news roundup, the new REPL coming in Python 3.13, a pytest daemon to 10X test iteration speed, a discussion about software friction, a Raspberry Pi document scanner, and a project for controlling time per iteration loop.

Topics:

• 00:00:00 - Introduction
• 00:02:08 - Python Software Foundation Board Election Dates for 2024
• 00:02:35 - 2023 PSF Annual Impact Report
• 00:03:03 - Python’s unittest: Writing Unit Tests for Your Code
• 00:09:41 - What’s New in Python 3.13
• 00:10:38 - The New REPL in Python 3.13
• 00:13:39 - Best Python Chart Examples
• 00:15:27 - Animation with text that highlights important events
• 00:16:39 - Sankey Diagram with Python and Plotly
• 00:18:55 - Video Course Spotlight
• 00:20:25 - Pytest Daemon: 10X Local Test Iteration Speed
• 00:23:58 - Software Friction
• 00:35:41 - A Raspberry Pi Document Scanner
• 00:39:00 - pacemaker: For Controlling Time Per Iteration Loop in Python
• 00:41:55 - Thanks and goodbye

News:

• Python Software Foundation Board Election Dates for 2024
• 2023 PSF Annual Impact Report

Show Links:

• Python’s unittest: Writing Unit Tests for Your Code – In this tutorial, you’ll learn how to use the unittest framework to create unit tests for your Python code. Along the way, you’ll also learn how to create test cases, fixtures, test suites, and more.
• What’s New in Python 3.13 – Python 3.13 has gone into beta, which means the feature freeze is now in place. This is the official listing of the new features in 3.13. This release includes changes to the REPL, new typing features, experimental support for disabling the GIL, dead battery removal, and more.
• The New REPL in Python 3.13 – Python 3.13 just hit feature freeze with the first beta release, and it includes a host of improvements to the REPL. Automatic indenting, block-level editing, and more make the built-in REPL more powerful and easier to use.
• Best Python Chart Examples
• Animation with text that highlights important events - Python Graph Gallery
• Sankey Diagram with Python and Plotly - Python Graph Gallery
• Pytest Daemon: 10X Local Test Iteration Speed – Discord has a large Python monolith with lots of imports, which now takes 13 seconds to start up. On the server that’s not a problem but to run a test it is. Ruby’s solution is to have a daemon that hot loads a test on a process that already has the imports completed.

Discussion:

• Software Friction – Friction is everywhere in software development. Two setbacks are more than twice as bad as one setback. This article discusses the sources of software friction and what you can do about it.

Projects:

• A Raspberry Pi Document Scanner
• pacemaker: For Controlling Time Per Iteration Loop in Python

Additional Links:

• unittest — Unit testing framework — Python 3.12.3 documentation
• Testing with Python (part 1): the basics - Bite code!
• Sankey Diagrams – A Sankey diagram says more than 1000 pie charts
• tidytuesday: Official repo for the #tidytuesday project
• tidytuesday - dataset_announcements
• Chaos Monkey
• OpenCV: OpenCV modules
• How to Train Your Robot

Level up your Python skills with our expert-led courses:

• Building a Python GUI Application With Tkinter
• Testing Your Code With pytest
• Creating PyQt Layouts for GUI Applications

Support the podcast & join our community of Pythonistas

What’s it like to learn Python as a visually impaired or blind developer? How can you improve the accessibility of your Python web applications and learn current guidelines? This week on the show, Real Python community member Audrey van Breederode discusses her programming journey, web accessibility, and assistive technology.

Audrey shares her background as a system administrator and instructor. While she was learning Python, she discovered Real Python. Audrey provided some feedback about the built-in video player’s accessibility. Dan reached out and worked with Audrey on some website improvements for the visually impaired.

We discuss navigating modern websites and using assistive technology. Audrey also provides resources Python developers can use to improve the accessibility of their applications.

Topics:

• 00:00:00 – Introduction
• 00:03:12 – Work background
• 00:08:30 – Language for assistive tools and programming
• 00:10:30 – What led you to learning Python?
• 00:13:48 – Screen readers, braille display, and white space
• 00:17:22 – Discovering Real Python
• 00:22:41 – Accessibility survey and navigating websites
• 00:30:04 – Digging deeper into learning Python
• 00:35:42 – Video Course Spotlight
• 00:37:03 – Navigating changes in code
• 00:39:53 – Working with the terminal
• 00:42:14 – Accessibility of Python GUI libraries
• 00:44:22 – Django framework
• 00:47:11 – Screen readers and JAWS
• 00:53:19 – What are you excited about in the world of assistive technology?
• 00:57:03 – What are you excited about in the world of Python?
• 00:59:11 – What do you want to learn next?
• 01:00:09 – Thanks and goodbye

Show Links:

• JAWS® – Freedom Scientific
• What is JAWS? - YouTube
• Retinal detachment - Wikipedia
• Focus 80 Blue 5th Gen – Freedom Scientific
• WebAIM: Screen Reader User Survey #10 Results
• Web Content Accessibility Guidelines (WCAG) 2.2
• SecureCRT - The rock-solid Telnet and SSH client for Windows, macOS, and Linux
• P1 Monitor docker container for smart meters - Marcel Claassen
• Python Basics: Introduction to Python (Learning Path) – Real Python
• The web framework for perfectionists with deadlines - Django
• Surf’s Up! Surfing the Internet with JAWS
• NV Access - Download NVDA
• Picture Smart Challenges – Freedom Scientific

Level up your Python skills with our expert-led courses:

• Getting Started With Django: Building a Portfolio App
• Python Basics: Dictionaries
• HTML and CSS Foundations for Python Developers

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Would you like to get more practice working with APIs in Python? How about exploring the globe using the data from OpenStreetMap? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share an article from the Pybites blog about building queries using the Overpass API for OpenStreetMap. The post explores the data structures, tags, query formats, and how to use Overpass in Python.

Christopher discusses a Real Python article by recent guest Stephen Gruppetta about lazy evaluation in Python. The piece covers the advantages of generator expressions or functions and the potential disadvantages of using lazy versus eager evaluation methods.

We also share several other articles and projects from the Python community, including a news roundup, handling control-c in asyncio, preventing data leakage in pandas and scikit-learn, discussing the Django developer survey results, asking developers why they aren’t shipping faster, using UV to install into isolated environments, and a couple of tools for retrying in Python.

This episode is sponsored by Sentry.

Topics:

• 00:00:00 – Introduction
• 00:02:33 – PyPy v7.3.16 Release
• 00:02:54 – PEP 745: Python 3.14 Release Schedule
• 00:03:31 – The BASIC programming language turns 60
• 00:05:24 – Asyncio Handle Control-C (SIGINT)
• 00:07:37 – OpenStreetMaps, Overpass API and Python
• 00:11:53 – What’s Lazy Evaluation in Python?
• 00:16:10 – Sponsor: Sentry
• 00:17:17 – How to Prevent Data Leakage in pandas & scikit-learn
• 00:24:09 – Django Developers Survey 2023 Results
• 00:33:07 – Video Course Spotlight
• 00:34:21 – I Asked 100 Devs Why They Aren’t Shipping Faster?
• 00:47:03 – pipxu: Install in Isolated Environments Using UV
• 00:49:05 – tenacity: Retrying Library for Python
• 00:50:04 – stamina: Production-Grade Retries for Python
• 00:52:00 – Thanks and goodbye

News:

• PyPy v7.3.16 Release
• PEP 745: Python 3.14 Release Schedule
• The BASIC programming language turns 60 - Ars Technica

Show Links:

• Asyncio Handle Control-C (SIGINT) – When the user presses CTRL-C on the keyboard, the OS raises an interrupt signal to your program. When writing concurrent code this can get complicated as the signal goes to the process. This article shows you how to handle capturing CTRL-C elegantly when using asyncio.
• OpenStreetMaps, Overpass API and Python – OpenStreetMaps (OSM) is an open source mapping project that allows people to browse the world map and to plan routes. Not only does it have the expected web interface, but it also has an API known as Overpass. This article shows you two ways to use Python to query Overpass.
• What’s Lazy Evaluation in Python? – This tutorial explores lazy evaluation in Python and looks at the advantages and disadvantages of using lazy and eager evaluation methods. By the end of this tutorial, you’ll clearly understand which approach is best for you, depending on your needs.
• How to Prevent Data Leakage in pandas & scikit-learn – How you impute missing values in machine learning data sets can affect the quality of your training. This article teaches you what data leakage is and what steps you should take to avoid it.

Discussion

• Django Developers Survey 2023 Results
• I Asked 100 Devs Why They Aren’t Shipping Faster? – Daksh asked 100 developers why they aren’t shipping faster and this blog post shares what he learned. Problems include dependency bugs, overly complicated code bases, waiting on requirements, and more.

Projects:

• pipxu: Install in Isolated Environments Using UV
• tenacity: Retrying Library for Python
• stamina: Production-Grade Retries for Python

Additional Links:

• NASA’s Voyager 1 spacecraft finally phones home after 5 months of no contact - Space
• Python & APIs: A Winning Combo for Reading Public Data – Real Python
• Loading Data from OpenStreetMap with Python and the Overpass API - Nikolai Janakiev
• Leakage (machine learning) - Wikipedia
• Djangonaut Space - Where Contributors launch!
• Debris That Fell Off a Boeing 767 Found Outside the House of Lawyer

Level up your Python skills with our expert-led courses:

• Getting Started With Django: Building a Portfolio App
• Python Generators 101
• How to Set Up a Django Project

Support the podcast & join our community of Pythonistas

Do you get stressed while trying to learn Python? Do you prefer to build small programs or projects as you continue your coding journey? This week on the show, Real Python author Stephen Gruppetta is here to talk about his new book, “The Python Coding Book.”

Stephen has been teaching Python to adults and children for many years. With his new book, he wants to share the relaxed and friendly learning environment he’s developed. We discuss using analogies to explain programming concepts and constructing complete programs as chapter goals.

Over the last year, Stephen focused on writing. He started his newsletter, The Python Coding Stack, wrote more tutorials for Real Python and authored the book.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:32 – Education and programming background
• 00:05:50 – Developing a teaching style
• 00:10:36 – A friendly and relaxed programming book
• 00:14:31 – Making mistakes
• 00:18:29 – Sponsor: Mailtrap
• 00:19:03 – What was your curation process like?
• 00:21:22 – First chapter building an actual program
• 00:25:08 – Glossary terms and exercises
• 00:27:48 – Why feature an IDE?
• 00:34:07 – Monty and the White Room analogy
• 00:37:46 – What, no turtle?
• 00:42:21 – Video Course Spotlight
• 00:44:00 – Shift toward teaching
• 00:46:50 – Teaching adults and children
• 00:51:23 – Python sequences tutorial
• 00:53:48 – Building community and social media
• 00:58:12 – What are you excited about in the world of Python?
• 01:00:49 – What do you want to learn next?
• 01:03:07 – Thanks and goodbye

Show Links:

• Learn Python Coding - The Python Coding Book
• Rambling Reflections - Twelve Months of The Python Coding Stack
• On Writing: A Memoir of the Craft - Stephen King - Wikipedia
• Episode #4: Learning Python Through Errors – The Real Python Podcast
• Build a Python Turtle Game: Space Invaders Clone – Real Python
• Python Sequences: A Comprehensive Guide – Real Python
• Django in Action
• The Python Coding Place – The Place to Learn Python
• Stephen Gruppetta (@s_gruppetta_ct) / X

Level up your Python skills with our expert-led courses:

• Python Basics: Setting Up Python
• Python Turtle for Beginners
• Python Basics: Code Your First Python Program

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How do you verify and validate the data coming into your Python web application? What tools and security best practices should you consider as a developer? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the recent Real Python tutorial “Pydantic: Simplifying Data Validation in Python.” The piece covers installing the library with optional dependencies, working with base models, validating functions, and managing environment variables.

We continue our conversation about web development with another article about Python security best practices. This article covers several Python libraries and crucial steps you can take to help make your web-based applications more secure.

We also share several other articles and projects from the Python community, including a news roundup, why Python lists multiply oddly, inline run dependencies in pipx, a discussion about open-source contribution assignments, playing sounds in Python, and a Python library to access ISO country definitions.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:40 – Python 3.12.3, Python 3.11.9, and 3.13.0a6 Released
• 00:03:43 – Django Bugfix Release Issued: 5.0.4
• 00:04:48 – PEP 738 Accepted: Adding Android as a Supported Platform
• 00:05:53 – EuroPython Tickets on Sale: Prague/Remote July 8-14
• 00:06:38 – PyCon Portugal 2024
• 00:07:17 – Pydantic: Simplifying Data Validation in Python
• 00:15:24 – Sponsor: Mailtrap
• 00:15:58 – Why Do Python Lists Multiply Oddly?
• 00:22:21 – Best Python Security Practices for Web Developers
• 00:34:13 – Video Course Spotlight
• 00:35:38 – Inline Run Dependencies in pipx 1.4.2
• 00:40:16 – So Your Teacher Wants You to Do Open Source
• 00:54:49 – nava: Play Sounds in Python
• 00:56:25 – pycountry: A Python library to access ISO country definitions
• 00:58:18 – Thanks and goodbye

News:

• Python 3.12.3 and 3.13.0a6 Released
• Python 3.11.9 Released
• Django Bugfix Release Issued: 5.0.4
• PEP 738 Accepted: Adding Android as a Supported Platform
• PEP 742 Accepted: Narrowing Types With TypeIs
• EuroPython Tickets on Sale: Prague/Remote July 8-14
• PyCon Portugal 2024

Show Links:

• Pydantic: Simplifying Data Validation in Python – Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on tutorial, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.
• Why Do Python Lists Multiply Oddly? – In Python you can use the multiplication operator on sequences to return a repeated version of the value. When you do this with a list containing an empty list you get what might be unexpected behavior. This article explains what happens and why.
• Best Python Security Practices for Web Developers – Coding on the web means you have to be more security conscious as everyone has access to your software. This article discusses key steps you can take to help make your code more secure.
• Inline Run Dependencies in pipx 1.4.2 – PEP 723 adds the ability to specify dependencies within a Python script itself. The folks who write pipx have added an experimental feature that takes advantage of this future language change. This article shows you how the new feature looks and what pipx does with it.
• Install and Execute Python Applications Using pipx – In this tutorial, you’ll learn about a tool called pipx, which lets you conveniently install and run Python packages as standalone command-line applications in isolated environments. In a way, pipx turns the Python Package Index (PyPI) into an app marketplace for Python programmers.

Discussion:

• So Your Teacher Wants You to Do Open Source – Sometimes teachers or mentors ask students to contribute to an open source project, without the context of what that entails. This opinion piece covers just how much noise that causes for the projects and why you shouldn’t do it unless you truly mean to contribute.
• 503 Days Working Full-Time on FOSS: Lessons Learned – For a year and a half, Rodrigo worked at Textualize the company behind the popular open source Python projects Rich and Textual. This blog post talks about what he learned while he was there.

Projects:

• nava: Play Sounds in Python
• pycountry: A Python library to access ISO country, subdivision, language, currency and script definitions and their translations

Additional Links:

• Pydantic
• François Fleuret on X: “2h of debugging. Whatever you say, that’s counter intuitive.”
• bandit: Security oriented static analyzer for Python code - PyPI
• Dependency Management With Python Poetry – Real Python
• OWASP Top Ten - OWASP Foundation
• pipx
• Governance - The Pallets Projects
• Textual
• How to Contribute to Open Source - Open Source Guides
• Djangonaut Space - Where contributors launch!

Level up your Python skills with our expert-led courses:

• Lists and Tuples in Python
• Using raise for Effective Exceptions
• Sorting Data in Python With pandas

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What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.

Phillip contrasts Ibis’s workflow with other Python dataframe libraries. We discuss how “getting close to the data” speeds things up and conserves memory.

He describes the different approaches Ibis provides for querying data and how to select a specific backend. We discuss ways to get started with the library and how to access example data sets to experiment with the platform.

Phillip discovered Ibis while looking for a tool that allowed him to reuse SQL queries written for a specific data platform on a different one. He recounts how he got involved with the Ibis project, sharing his background in open source and learning how to contribute to a first project.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – How did you get started with Ibis?
• 00:08:10 – First contribution to open source
• 00:13:46 – Comparing Ibis to other dataframe libraries
• 00:20:09 – Sponsor: Mailtrap
• 00:20:43 – What goes into the selection of backend?
• 00:27:07 – Database connections vs SQL compilers
• 00:30:03 – Raw SQL approach
• 00:34:06 – Dataframe approach
• 00:38:31 – What does “getting close to the data” mean?
• 00:41:52 – Video Course Spotlight
• 00:43:24 – Phillip in the cloud - YouTube channel
• 00:44:56 – Access to sample data sets
• 00:50:11 – Additional resources
• 00:52:50 – What are some of the backends Ibis supports?
• 00:54:13 – Entry points to the platform
• 00:55:00 – How are you supported?
• 00:57:10 – Exporting a SQL query
• 00:59:23 – What are you excited about in the world of Python?
• 01:04:28 – What do you want to learn next?
• 01:07:12 – How can people follow your work online?
• 01:08:00 – Thanks and goodbye

Show Links:

• Ibis - the portable Python dataframe library
• The Leading Designer and Builder of Enterprise Data Systems - Voltron Data
• PEP 249 – Python Database API Specification v2.0
• sqlglot: Python SQL Parser and Transpiler - GitHub
• Ibis – getting_started
• ibis-examples: A repository of runnable examples using ibis
• Ibis – Reference Documentation
• PyScript - Run Python in your HTML
• pixi - Prefix.dev
• uv: An extremely fast Python package installer and resolver, written in Rust
• PyCon US 2024
• LearnCraft Spanish – Fluency for Serious Learners
• ibis: the portable Python dataframe library - GitHub
• Ibis – Blog Posts
• Phillip in the Cloud - YouTube
• Phillip Cloud (@cpcloudy) / X
• cpcloud (Phillip Cloud) · GitHub

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Creating Web Maps From Your Data With Python Folium
• Using raise for Effective Exceptions

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What is error culture, and how do you avoid it within your organization? How do you navigate alert and notification fatigue? Hey, it’s episode #200! Real Python’s editor-in-chief, Dan Bader, joins us this week to celebrate. Christopher Trudeau also returns to bring another batch of PyCoder’s Weekly articles and projects.

We discuss an article series about error culture. We dig into false positives, hero culture, and the tendency to start ignoring alerts. We contrast our personal experiences and propose possible remedies. Dan describes configuring Real Python’s alert system.

We also share several other articles and projects from the Python community, including a news roundup, reading and writing WAV files in Python, moving beyond flat files toward SQLite and SQLAlchemy, getting help in Python, exploring four kinds of optimization, a framework for building web scrapers, and a project to simply subprocesses.

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

Topics:

• 00:00:00 – Introduction
• 00:02:42 – PyPI Hiring a Support Specialist
• 00:03:19 – PyPI Temporarily Halted New Users and Projects
• 00:04:14 – What we know about the xz Utils backdoor
• 00:05:38 – CPython, PyPI, and the backdoor of xz
• 00:07:18 – Episode 200 appreciation and the journey
• 00:09:18 – A visit from Dan
• 00:14:14 – Reading and Writing WAV Files in Python
• 00:19:56 – Sponsor: Sentry
• 00:21:03 – SQLite and SQLAlchemy in Python
• 00:27:36 – Getting Help (In Python)
• 00:32:49 – Laurence Tratt: Four Kinds of Optimization
• 00:40:54 – Video Course Spotlight
• 00:42:26 – Discussion: Error Culture
• 00:58:03 – botasaurus: The All in One Framework to Build Awesome Scrapers
• 01:01:04 – suby: Slightly Simplified Subprocesses
• 01:02:50 – Thanks and goodbye

News:

• PyPI Hiring a Support Specialist (Remote)
• PyPI Temporarily Halted New Users and Projects – To fend off a supply-chain attack, PyPI temporarily halted new users and projects for about 10 hours last week. This article discusses why, and the scourge of supply-chain attacks.
• What we know about the xz Utils backdoor that almost infected the world - Ars Technica
• CPython, PyPI, and many Python packages are not affected by the backdoor of xz - Core Development - Discussions on Python.org

Topics:

• Reading and Writing WAV Files in Python – In this tutorial, you’ll learn how to work with WAV audio files in Python using the standard-library wave module. Along the way, you’ll synthesize sounds from scratch, visualize waveforms in the time domain, animate real-time spectrograms, and apply special effects to widen the stereo field.
• SQLite and SQLAlchemy in Python: Beyond Flat Files – In this video course, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.
• How SQLite Is Tested – The page describes how SQLite is rigorously tested using four test harnesses, fuzz testing, anomaly testing like crash and I/O error simulations, and other techniques to ensure reliability.
• Getting Help (In Python) – When trying to remember just where sleep() was in the Python standard library, Ishaan stumbled through the built-in help and learned how to use it to answer just these kinds of questions.
• Laurence Tratt: Four Kinds of Optimization – “Premature optimization might be the root of all evil, but overdue optimization is the root of all frustration. No matter how fast hardware becomes, we find it easy to write programs which run too slow.” Read on to learn what to do about it.

Discussion:

• Error Culture
• Error Culture Part II
• Error Culture Part III

Projects:

• botasaurus: The All in One Framework to Build Awesome Scrapers
• suby: Slightly Simplified Subprocesses

Additional Links:

• xkcd: Exploits of a Mom
• How SQLite Is Tested
• SettingWithCopyWarning in pandas: Views vs Copies – Real Python
• 99% Invisible - Mini-Stories: Volume 4

Level up your Python skills with our expert-led courses:

• Playing and Recording Sound in Python
• Exploring Scopes and Closures in Python
• SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files

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How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.

Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares a recent project for a family-owned seed company that wanted to build a tool for customers to access years of farm research. These documents were stored as brochure-style PDFs and spanned 50 years.

We discuss several of the tools used to augment a LLM. Calvin covers working with LangChain and vectorizing data with ChromaDB. We talk about the obstacles and limitations of capturing documentation.

Calvin also shares a smaller project that you can try out yourself. It takes the information from a conference website and creates a chatbot using Django and Python prompt-toolkit.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:21 – Background on the project
• 00:03:51 – Complexity of adding documents
• 00:09:01 – Retrieval-augmented generation and providing links
• 00:13:46 – Updating information and larger conversation context
• 00:18:08 – Sponsor: Mailtrap
• 00:18:43 – Working with context
• 00:21:02 – Temperature adjustment
• 00:22:07 – Rally Conference Chatbot Project
• 00:26:20 – Vectorization using ChromaDB
• 00:32:49 – Employing Python prompt-toolkit
• 00:35:07 – Learning libraries on the fly
• 00:37:38 – Video Course Spotlight
• 00:39:00 – Problems with tables in documents
• 00:42:30 – Everything looks like a chat box
• 00:44:26 – Finding the right fit for a client and customer
• 00:49:05 – What are questions you ask a new client now?
• 00:51:54 – Canada Air anecdote
• 00:56:20 – How do you stay up to date on these topics?
• 01:01:03 – What are you excited about in the world of Python?
• 01:03:22 – What do you want to learn next?
• 01:04:58 – How can people follow your work online?
• 01:05:31 – IndyPy
• 01:07:13 – Thanks and goodbye

Show Links:

• Transforming Agricultural Data with AI — Six Feet Up
• Build ChatGPT-like Apps with AI — Six Feet Up
• Innovate with AI: Build ChatGPT-like Apps - YouTube
• What is retrieval-augmented generation? - IBM Research Blog
• rally-llm-presentation - sixfeetup - GitHub
• Python Prompt Toolkit 3.0 — Documentation
• Chroma - the AI-native open-source embedding database
• Embeddings and Vector Databases With ChromaDB – Real Python
• LangChain
• Build an LLM RAG Chatbot With LangChain – Real Python
• Air Canada must pay after chatbot lies to grieving passenger - The Register
• I’d Buy That for a Dollar: Chevy Dealership’s AI Chatbot Goes Rogue
• Omnivore
• TLDR AI - Get smarter about AI in 5 minutes
• Tech Brew
• Simon Willison’s Weblog
• llm: Access large language models from the command-line - simonw - GitHub
• PyCon US 2024
• Syntorial: The Ultimate Synthesizer Tutorial
• Blog — Six Feet Up
• Calvin Hendryx-Parker - LinkedIn
• Eclipse Insights: How AI is Transforming Solar Astronomy - YouTube

Level up your Python skills with our expert-led courses:

• Sneaky REST APIs With Django Ninja
• How to Work With a PDF in Python
• Command Line Interfaces in Python

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Can you build a Space Invaders clone using Python’s built-in turtle module? What advantages does the Seaborn data visualization library provide compared to Matplotlib? Christopher Trudeau is back on the show this week, along with special guest Real Python core team member Bartosz Zaczyński. We’re sharing another batch of PyCoder’s Weekly articles and projects.

Bartosz shares a Real Python step-by-step project for building a video game using the Python turtle module. The turtle module provides an interactive environment that lets users create pictures and shapes on a virtual canvas. The project takes you through game design concepts such as animating sprites, detecting a collision, and building a game loop.

We discuss another Real Python resource, “Visualizing Data in Python With Seaborn.” Seaborn is a significant next step if you’ve already been working with Matplotlib. It produces impressive visualizations and offers a functional or object-based approach to designing plots.

We also share several other articles and projects from the Python community, including a news roundup, finding Python Easter eggs, exploring whether Python has pointers, styling Excel cells with OpenPyXL, using weird tests to capture tacit knowledge, inspecting and running Django commands in a TUI, building reactive web UIs in Python, and a project for predictable Python datetimes.

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

Topics:

• 00:00:00 – Introduction
• 00:03:06 – Python 3.13.0 alpha 5 is now available
• 00:03:38 – Allow disabling the GIL with flags
• 00:04:17 – Django security releases issued: 5.0.3, 4.2.11, and 3.2.25
• 00:04:32 – The Python Coding Book
• 00:05:03 – Finding Python Easter Eggs – Code Conversation
• 00:12:48 – Sponsor: Posit
• 00:13:34 – Visualizing Data in Python With Seaborn
• 00:18:18 – Does Python have pointers?
• 00:21:43 – Build a Python Turtle Game: Space Invaders Clone
• 00:30:42 – Video Course Spotlight
• 00:32:04 – Styling Excel Cells with OpenPyXL and Python
• 00:35:22 – Use weird tests to capture tacit knowledge
• 00:37:09 – whenever: Strict, predictable, and typed datetimes
• 00:42:25 – hyperdiv: Build Reactive Web UIs in Python
• 00:46:19 – django-tui: Inspect and run Django Commands in a TUI
• 00:48:40 – Thanks and goodbye

News:

• Python Insider: Python 3.13.0 alpha 5 is now available
• Allow disabling the GIL with Flags - cpython - GitHub
• Django security releases issued: 5.0.3, 4.2.11, and 3.2.25 - Weblog - Django
• The Python Coding Book – The Python Coding Place

Show Links:

• Finding Python Easter Eggs – Code Conversation - Video Course – Python has its fair share of hidden surprises, commonly known as Easter eggs. From clever jokes to secret messages, these little mysteries are often meant to be discovered by curious developers like you!
• Visualizing Data in Python With Seaborn – In this tutorial, you’ll learn how to use the Python seaborn library to produce statistical data analysis plots to allow you to better visualize your data. You’ll learn how to use both its traditional classic interface and more modern objects interface.
• Does Python have pointers? - Ned Batchelder – Depending on how you’re using the term “pointer” changes the answer to the question. Read on to better understand the programming terminology and whether Python has pointers.
• Build a Python Turtle Game: Space Invaders Clone – In this step-by-step tutorial, you’ll use Python’s turtle module to write a Space Invaders clone. You’ll learn about techniques used in animations and games, and consolidate your knowledge of key Python topics.
• Styling Excel Cells with OpenPyXL and Python - Many Python libraries that deal with Excel only handle data, but OpenPyXL gives you the ability to style your cells in many different ways. Learn how to give your spreadsheets pizazz!
• Use weird tests to capture tacit knowledge - Applied Cartography – Sometimes adding code in one place means configuration elsewhere also needs to be updated. One way of ensuring this is happening properly in a large project is to use unit tests. This post covers a few examples, complete with pytest code.

Projects:

• whenever: ⏰ Strict, predictable, and typed datetimes
• hyperdiv: Build Reactive Web UIs in Python
• django-tui: Inspect and run Django Commands in a text-based user interface (TUI)

Additional Links:

• What’s the Zen of Python? – Real Python
• Using Python for Data Analysis – Real Python
• The Beginner’s Guide to Python Turtle – Real Python
• Roamer - The History of Turtle Robots
• Tutorial — openpyxl 3.1.2 documentation
• Ten Python datetime pitfalls, and what libraries are (not) doing about it - Arie Bovenberg
• The science behind why people hate Daylight Saving Time so much - Ars Technica
• Textual

Level up your Python skills with our expert-led courses:

• Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn
• Python Turtle for Beginners
• Pointers and Objects in Python

Support the podcast & join our community of Pythonistas

How is Python being used to automate processes in the laboratory? How can it speed up scientific work with DNA sequencing? This week on the show, Chemical Engineering PhD Student Parsa Ghadermazi is here to discuss Python in bioinformatics.

Parsa provides background on his research and the bioinformatic techniques used to discover gut microbes’ role in human health and diseases. We talk about automating lab experiments with liquid handling robots and Python.

We dig into libraries to shatter and reassemble DNA sequences. Parsa also shares current projects from the Chan Lab at Colorado State University and his GitHub repository.

Topics:

• 00:00:00 – Introduction
• 00:01:51 – Engineering Background and Current PhD Program
• 00:05:52 – What is Bioinformatics?
• 00:08:11 – Where do you use Python in the lab?
• 00:10:35 – Using lab robotics
• 00:15:22 – Python development environment
• 00:16:33 – Lab robotics allow for precision
• 00:19:03 – How are using these tools for research?
• 00:22:14 – What are the techniques for measurements?
• 00:26:17 – Video Course Spotlight
• 00:27:33 – How is the data output from the machine?
• 00:29:20 – Moving into DNA sequencing and extraction
• 00:32:08 – Shattering to work with smaller DNA sequences
• 00:34:34 – Python libraries for DNA re-assembly
• 00:36:28 – Building ADToolbox
• 00:40:24 – How do you store the data?
• 00:41:32 – Inferring microbial interactions
• 00:44:02 – Types of hardware used for these projects
• 00:47:07 – What are you excited about in the world of Python?
• 00:48:09 – What do you want to learn next?
• 00:49:16 – How can people follow your work online?
• 00:50:00 – Thanks and goodbye

Show Links:

• ParsaGhadermazi - GitHub
• Bioinformatics - Wikipedia
• Opentrons - Lab Automation - Lab Robots for Life Scientists
• Tutorial — Opentrons Python API V2 Documentation
• Serial Dilutions and Plating: Microbial Enumeration - Microbiology - JoVE
• What is Chromatogram & How to Read a Chromatogram?
• Episode #186: Exploring Python in Excel
• megahit: Ultra-fast and memory-efficient (meta-)genome assembler
• DRAM: Distilled and Refined Annotation of Metabolism - GitHub
• humann: HUMAnN 3.0 - HMP Unified Metabolic Analysis Network
• MetaPhlAn4 – The Huttenhower Lab
• ADToolbox - Tools for modeling and optimizing the anaerobic digestion process
• SPAM-DFBA - Algoritm for inferring microbial interactions
• Microbial interactions from a new perspective - Bioinformatics - Oxford Academic
• Alpine — Research Computing University of Colorado Boulder documentation
• MkDocs
• Build Your Python Project Documentation With MkDocs
• scikit-bio
• Episode #190: Great Starting Points for Contributing to Open Source
• Chan Lab at Colorado State University - GitHub
• ParsaGhadermazi - GitHub
• Parsa Ghadermazi - LinkedIn

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Data Cleaning With pandas and NumPy
• Reading and Writing CSV Files

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What are the advantages of determining the type of an object by how it behaves? What coding circumstances are not a good fit for duck typing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher covers a recent Real Python tutorial by Leodanis Pozo Ramos titled Duck Typing in Python: Writing Flexible and Decoupled Code. The tutorial explains the concepts of duck typing within object-oriented programming and its use within Python’s built-in tools.

We discuss a recent article on monkey patching in Python. This practice of dynamically modifying a class or module’s behavior at runtime allows for testing, debugging, and experimentation.

We also share several other articles and projects from the Python community, including a news roundup, why names are not the same as objects in Python, using IPython Jupyter magic commands, a discussion about becoming a senior developer, a data exploration challenge, a Python evaluation game, and a terminal UI for regex testing.

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

Topics:

• 00:00:00 – Introduction
• 00:02:38 – Listener feedback
• 00:04:02 – DjangoCon US Call for Proposals
• 00:04:38 – White House Recommends Use of Python
• 00:05:41 – JupyterLab 4.1 and Notebook 7.1 Released
• 00:06:05 – What’s in a Name?
• 00:11:52 – Duck Typing in Python: Writing Flexible and Decoupled Code
• 00:15:07 – Sponsor: Sentry
• 00:16:11 – Using IPython Jupyter Magic Commands
• 00:22:31 – A Guide to Monkey Patching
• 00:25:27 – Falsehoods Junior Developers Believe About Becoming Senior
• 00:33:01 – Video Course Spotlight
• 00:34:11 – Falsehoods continued
• 00:43:43 – Where in the data?
• 00:46:55 – the eval game
• 00:48:03 – rexi: Terminal UI for Regex Testing
• 00:49:43 – Thanks and goodbye

News:

• DjangoCon US Call for Proposals
• White House Recommends Use of Python
• JupyterLab 4.1 and Notebook 7.1 Released

Show Links:

• What’s in a Name? – An article about names in Python, and why they’re not the same as objects. The article discusses reference counts and namespaces.
• Duck Typing in Python: Writing Flexible and Decoupled Code – In this tutorial, you’ll learn about duck typing in Python. It’s a typing system based on objects’ behaviors rather than on inheritance. By taking advantage of duck typing, you can create flexible and decoupled sets of Python classes that you can use together or individually.
• Using IPython Jupyter Magic Commands – “IPython Jupyter Magic commands (e.g. lines in notebook cells starting with % or %%) can decorate a notebook cell, or line, to modify its behavior.” This article shows you how to define them and where they can be useful.
• Monkeying Around With Python: A Guide to Monkey Patching – Monkey patching is the practice of modifying live code. This article shows you how it’s done and why and when to use the practice.

Discussion:

• Falsehoods Junior Developers Believe About Becoming Senior – This opinion piece by Vadim discusses how newer developers perceive what it means to be a senior developer, and how they’re often wrong.

Projects:

• Where in the data?
• the eval game
• rexi: Terminal UI for Regex Testing

Additional Links:

• Pointers in Python: What’s the Point? – Real Python
• Unlock IPython’s Magical Toolbox for Your Coding Journey – Real Python
• Episode #88: Discussing Type Hints, Protocols, and Ducks in Python – The Real Python Podcast
• saul.pw
• BlueBird Shell

Level up your Python skills with our expert-led courses:

• Python Type Checking
• Testing Your Code With pytest
• Pointers and Objects in Python

Support the podcast & join our community of Pythonistas

How do you get yourself unstuck when facing a programming problem? How do you develop a positive developer mindset while learning Python? This week on the show, Bob Belderbos from Pybites is here to talk about learning Python and building healthy developer habits.

Bob created the Pybites learning platform with his friend Julian Sequeira. They initially focused on exercises and coding challenges to motivate new Python students. As they grew their community, they created a podcast and moved into coaching.

They noticed that most new developers share common struggles of tutorial paralysis, imposter syndrome, and motivation. Bob discusses techniques for developing a positive mindset, overcoming coding blocks, and delivering projects.

Topics:

• 0:00:00 – Introduction
• 0:02:06 – How did you start Pybites?
• 0:04:32 – Building a community through challenges
• 0:06:17 – When did you start your podcast?
• 0:08:22 – Defining developer mindset
• 0:11:31 – Learning Python outside of a classroom
• 0:16:15 – Podcast is a good place to discuss mindset
• 0:19:37 – Video Course Spotlight
• 0:20:56 – Sharing Python tips
• 0:30:14 – Sharing content as a creator
• 0:35:03 – Writing cleaner code
• 0:40:20 – Moving from challenges to projects
• 0:47:08 – Helping yourself when you’re stuck
• 0:51:31 – Dealing with imposter syndrome
• 0:55:50 – What are you excited about in the world of Python?
• 0:57:43 – What do you want to learn next?
• 0:59:15 – How can people follow your work online?
• 0:59:51 – Thanks and goodbye

Show Links:

• Pybites - We Create Python Developers
• Pybites Podcast
• PyBites Platform - Real World Python Exercises
• Pybites Community
• Pybites Python Tips Book - Pybites
• itertools — Functions creating iterators for efficient looping - Python documentation
• string — Common string operations - Python documentation
• Python enumerate(): Simplify Loops That Need Counters – Real Python
• ast — Abstract Syntax Trees - Python documentation
• Building Maintainable Software, Java Edition
• Refactoring - Improving the Design of Existing Code by Martin Fowler
• A Mind For Numbers - Barbara Oakley
• Real Imposters Don’t Experience Imposter Syndrome
• Rust Programming Language
• Bob Belderbos (@bbelderbos) - X
• Bob Belderbos - LinkedIn

Level up your Python skills with our expert-led courses:

• Grow Your Python Portfolio With 13 Intermediate Project Ideas
• Python Basics: Code Your First Python Program
• Building Python Project Documentation With MkDocs

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What are the typical computer tasks you do manually every week? Could you automate those tasks with a Python script? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent Hacker News thread about frequently used automation scripts. We share the kinds of tasks we’ve automated with Python in our work and personal lives.

Christopher shares a tutorial about building a micro-search engine from scratch using Python. The post takes you through coding the components of a crawler, index, and ranker. The finished engine is designed to search the posts of the blogs you follow.

We also share several other articles and projects from the Python community, including a news roundup, how a Polars query works under the hood, using Python for data analysis, understanding open-source licensing, summarizing the significant changes between Python versions, a robust TUI hex editor, and a lightweight dataframe library with a universal interface for data wrangling.

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

Topics:

• 00:00:00 – Introduction
• 00:02:23 – uv: Python Packaging in Rust
• 00:02:43 – Rye Grows With uv
• 00:03:20 – Python 3.13.0 Alpha 4 Is Now Available
• 00:03:45 – A Bird’s Eye View of Polars
• 00:07:28 – Polars: Why We Have Rewritten the String Data Type
• 00:09:33 – A Search Engine in 80 Lines of Python
• 00:13:14 – Using Python for Data Analysis
• 00:18:22 – Sponsor: Intel
• 00:18:53 – Understanding Open Source Licensing
• 00:21:54 – Summary of Major Changes Between Python Versions
• 00:23:19 – What Python automation scripts do you reuse frequently at work?
• 00:34:21 – Video Course Spotlight
• 00:35:52 – hexabyte: A modern, modular, and robust TUI hex editor
• 00:39:56 – ibis: The Flexibility of Python With the Scale of Modern SQL
• 00:43:31 – Thanks and goodbye

News:

• 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.
• Rye Grows With uv - Armin Ronacher’s Thoughts and Writings
• Python 3.13.0 Alpha 4 Is Now Available

Show Links:

• A Bird’s Eye View of Polars – This post on the Polars blog introduces you to how Polars works, showing the steps from queries, plans, optimizations, and then the final execution.
• Polars: Why We Have Rewritten the String Data Type – A large refactor on the string data type is underway in Polars. This deep dive explains why and what is changing.
• A Search Engine in 80 Lines of Python – In this post Alex explains how he built a micro-search engine from scratch using Python. The resulting search engine is used to search in the posts of the blogs he follows.
• Using Python for Data Analysis – In this tutorial, you’ll learn the importance of having a structured data analysis workflow, and you’ll get the opportunity to practice using Python for data analysis while following a common workflow process.
• Understanding Open Source Licensing – This article discusses the importance of open-source licensing in software development and its implications for stakeholders.
• Summary of Major Changes Between Python Versions – This article is a quick reference covering the major changes introduced with each new version of Python. Can’t remember when the walrus operator was introduced? This is the place to look that up.

Discussion:

• What Python automation scripts do you reuse frequently at work? - Hacker News

Projects

• hexabyte: A modern, modular, and robust TUI hex editor
• ibis: The Flexibility of Python With the Scale of Modern SQL

Additional Links:

• Episode #193: Wes McKinney on Improving the Data Stack & Composable Systems – The Real Python Podcast
• Open Source Licensing: Software Freedom and Intellectual Property Law
• What’s in which Python - Ned Batchelder
• Automate the Boring Stuff with Python - Al Sweigart
• Working With Files in Python – Real Python
• Build Command-Line Interfaces With Python’s argparse – Real Python

Level up your Python skills with our expert-led courses:

• Defining Python Functions With Optional Arguments
• Practical Recipes for Working With Files in Python
• Building Command Line Interfaces With argparse

Support the podcast & join our community of Pythonistas

How do you avoid the bottlenecks of data processing systems? Is it possible to build tools that decouple storage and computation? This week on the show, creator of the pandas library Wes McKinney is here to discuss Apache Arrow, composable data systems, and community collaboration.

Wes briefly describes the humble beginnings of the pandas project in 2008 and moving the project to open source in 2011. Since then, he’s been thinking about improvements across the data processing ecosystem.

Wes collaborated with members of the broader data science community to build the in-memory analytics infrastructure of Apache Arrow. Arrow avoids the bottlenecks of repeated data serialization and format conversion. He shares examples of Arrow’s use across the spectrum in tools like Polars and DuckDB.

Wes advocates moving from vertically integrated tools toward composable data systems. We discuss his work on Ibis, a portable dataframe API for data manipulation and exploration in Python. Ibis supports multiple backends by decoupling the API from the execution engine.

This week’s episode is brought to you by Posit Connect.

Topics:

• 00:00:00 – Introduction
• 00:02:26 – Dealing with limitations in early data science
• 00:04:53 – Making pandas open source
• 00:07:10 – Making changes to an existing platform
• 00:12:34 – Decoupling storage and computation
• 00:23:04 – Sponsor: Posit Connect
• 00:23:54 – Apache Arrow solving multiple issues
• 00:27:40 – DuckDB efficient analytic SQL database
• 00:30:24 – Polars dataframe library
• 00:31:04 – pandas 2.0 adding Arrow
• 00:35:56 – Video Course Spotlight
• 00:37:20 – Apache Software Foundation background
• 00:41:29 – Shifting from developer to organizer and collaborator
• 00:45:56 – Creating a portable query layer with Ibis
• 00:55:34 – Casualties of the language wars
• 00:57:57 – What’s your role at Posit?
• 01:01:23 – What are you excited about in the world of Python?
• 01:04:52 – What do you want to learn next?
• 01:06:21 – How can people follow your work online?
• 01:08:20 – Thanks and goodbye

Show Links:

• Wes McKinney - Personal Website
• Wes McKinney - The Road to Composable Data Systems: Thoughts on the Last 15 Years and the Future
• Wes McKinney - Leveling Up the Data Stack: Thoughts on the Last 15 Years - YouTube
• Apache Hadoop
• Cloudera - The hybrid data company
• Wes McKinney - Apache Arrow and the “10 Things I Hate About pandas”
• Voltron Data - The Leading Designer and Builder of Enterprise Data Systems
• Apache Arrow
• DuckDB - An in-process SQL OLAP database management system
• DuckDB-Wasm - Efficient Analytical SQL in the Browser
• Polars - Dataframes for the new era
• pandas 2.2.0 documentation
• Episode #167: Exploring pandas 2.0 & Targets for Apache Arrow – The Real Python Podcast
• ASF - Welcome to The Apache Software Foundation!
• Ursa Labs Blog
• Ibis - The Portable Python dataframe Library
• Python dataframe interchange protocol
• Hadley Wickham
• Rust Programming Language
• italki - Best language learning app with certificated tutors
• Wes McKinney - LinkedIn
• Wes McKinney (@wesmckinn) - X
• Posit - The Open-Source Data Science Company

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Unleashing the Power of the Console With Rich
• The pandas DataFrame: Working With Data Efficiently

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What are real-life examples of using Python decorators? How can you harness their power in your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article series that digs into Python decorators. The first two articles discuss the basics of constructing decorators. The third part describes how popular Python libraries use decorators with call interception, function registration, and enriching the behavior of a function.

Christopher shares a piece about the common pitfalls of working with the Pythondatetime library. The article considers how current third-party libraries don’t address most of these quirks and offers a potential solution with a new library.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about the popularity of the Rust language, handling unset values in FastAPI with Pydantic, working with Python’s mini-language for formatting strings, mocking Django queryset functions, and a modern replacement for the Requests library.

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

Topics:

• 00:00:00 – Introduction
• 00:02:53 – Django security releases issued: 5.0.2, 4.2.10, and 3.2.24
• 00:03:10 – Python 3.12.2 and 3.11.8 are now available
• 00:03:21 – Introducing PSF Grants Program Office Hours
• 00:04:19 – Python’s Format Mini-Language for Tidy Strings
• 00:12:22 – Ten Python datetime Pitfalls
• 00:18:34 – Sponsor: Sentry
• 00:19:37 – Real Life Use of Decorators
• 00:29:18 – Handling Unset Values in FastAPI With Pydantic
• 00:35:43 – Video Course Spotlight
• 00:37:06 – The Python Rust-Aissance
• 00:50:19 – django-mock-queries: Mock Django Queryset Functions
• 00:53:09 – niquests: Requests but Multiplexed
• 00:55:55 – Thanks and goodbye

News:

• Django security releases issued: 5.0.2, 4.2.10, and 3.2.24
• Python 3.12.2 and 3.11.8 are now available
• Introducing PSF Grants Program Office Hours

Show Links:

• Python’s Format Mini-Language for Tidy Strings – In this tutorial, you’ll learn about Python’s format mini-language. See how to use it for creating working format specifiers and build nicely formatted strings and messages in your code.
• Ten Python datetime Pitfalls – It’s no secret that the Python datetime library has its quirks. Not only are there probably more than you think, but third-party libraries don’t address most of them! Arie created a new library to explore what a better datetime library could look like.
• Real Life Use of Decorators – Part 3 in a series on how Python decorators are used. This part covers real-life use cases including call interception, function registration, and behavioral enrichment.
• Handling Unset Values in FastAPI With Pydantic – When using the HTTP PATCH method only those fields that got changed are updated. Pydantic sets fields not given as arguments as None so there is no way to distinguish between an explicit None value and an unset field. This post explains how you process this scenario.

Discussion:

• The Python Rust-Aissance – Companies like Polars are showing how with Rust, Python developers now have a better, smoother path towards building high-performance libraries.
• Rye: A Python Developer Experience Vision Continued
• PyO3: Rust bindings for the Python interpreter
• Rust in Linux: Where we are and where we’re going next
• RustPython: OSS CPython Written in Rust
• granian: Rust HTTP Server for Python Applications

Projects:

• django-mock-queries: Mock Django Queryset Functions for Testing
• niquests: Requests but Multiplexed

Additional Links:

• Backus–Naur form - Wikipedia
• ANTLR - (ANother Tool for Language Recognition) A Powerful Parser Generator
• Xmas Decoration, Part 1 - Bite code!
• Xmas Decoration, Part 2 - Bite code!
• Falsehoods programmers believe about time - @noahsussman - Infinite Undo
• 10 Reasons You Should Quit Your HTTP Client - Ahmed TAHRI - Dev Genius
• HTTPX - Fully Featured HTTP Client for Python 3

Level up your Python skills with our expert-led courses:

• Using Python's datetime Module
• Python Decorators 101
• Python 3's F-Strings: An Improved String Formatting Syntax

Support the podcast & join our community of Pythonistas

How do you manage the dependencies of a large-scale data science project? How do you migrate that project from a laptop to cloud infrastructure or utilize GPUs and multiple instances in parallel? This week on the show, Savin Goyal returns to discuss the updates to the open-source framework Metaflow.

Savin briefly describes the Metaflow platform and the goal of simplifying engineering overhead for data scientists and programmers. We discuss how the platform captures snapshots of a project as you work, allowing you to go back in time or share the state of your project with another team member.

We dig into the complicated process of managing dependencies for machine learning and data science projects. Savin describes how the required external libraries can be specified within a flow with the new @pypi or @conda decorators. This allows a project to scale from a local machine to the cloud or multiple instances with all dependencies included.

He talks about starting a new company, Outerbounds, with fellow co-workers from Netflix. Their vision is to continue to build the Metaflow open-source platform and offer customers scalable enterprise-grade infrastructure.

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

Topics:

• 00:00:00 – Introduction
• 00:02:25 – Update on Metaflow
• 00:04:13 – What is Outerbounds?
• 00:07:26 – An ML platform to serve data scientists needs
• 00:13:02 – Dependency reproducibility via @conda and @pypi decorators
• 00:26:18 – Sponsor: Intel
• 00:27:10 – Storing lock files along with snapshots
• 00:29:17 – Working alongside code and dependency management systems
• 00:34:03 – Scaling a project from laptop to the cloud
• 00:40:13 – Video Course Spotlight
• 00:41:41 – Getting visibility on processes
• 00:47:23 – Adjusting your project due to GPU availability
• 00:52:27 – Example of jumping back into a project one year later
• 00:55:54 – What are you excited about in the world of Python?
• 00:57:39 – What do you want to learn next?
• 00:59:35 – How can people follow your work online?
• 01:00:19 – Thanks and goodbye

Show Links:

• Metaflow - a framework for real-life ML, AI, and data science
• Infrastructure for ML, AI, and Data Science - Outerbounds
• Human-Friendly, Production-Ready Data Science with Metaflow- Savin Goyal | SciPy 2022 - YouTube
• Episode #61: Scaling Data Science and Machine Learning Infrastructure Like Netflix – The Real Python Podcast
• New in Metaflow: The Long-Awaited @pypi Decorator - Outerbounds
• Managing Dependencies - Metaflow Docs
• Secure ML with Secure Software Dependencies - Outerbounds
• Directed acyclic graph (DAG) - Wikipedia article
• Visualizing Results - Metaflow Docs
• Seamless Data and ML Pipelines with Airflow and Metaflow - Outerbounds
• Episode #142: Orchestrating Large and Small Projects With Apache Airflow – The Real Python Podcast
• Savin (@SavinGoyal) - X
• Savin Goyal - LinkedIn
• Building the ML-driven future - Outerbounds Blog

Level up your Python skills with our expert-led courses:

• Everyday Project Packaging With pyproject.toml
• Combining Data in pandas With concat() and merge()
• Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn

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What’s it like to sit down for your first developer sprint at a conference? How do you find an appropriate issue to work on as a new open-source contributor? This week on the show, author and software engineer Stefanie Molin is here to discuss starting to contribute to open-source projects.

Stefanie is a data scientist and software engineer on Bloomberg’s Security Data Science team. She recently wrote an article titled “5 Ways to Get Started in Open Source.” We discuss finding ways to contribute that fit your interests and developer skills. We dig into the experience of participating in community sprints at a conference.

Stefanie is the author of Hands-On Data Analysis with Pandas. We also discuss the different processes between writing technical articles and authoring a book.

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

Topics:

• 00:00:00 – Introduction
• 00:02:55 – Being asked about how to get started
• 00:07:13 – Differences in short vs long form writing
• 00:09:03 – What was your introduction to contributing?
• 00:17:52 – What are additional benefits of contributing?
• 00:22:32 – Sponsor: Intel
• 00:23:22 – Sprints as an entry point
• 00:34:36 – Other requirements of a sprint
• 00:36:05 – Differences in conferences
• 00:41:52 – Other sprint experiences
• 00:42:50 – Contributing examples to documentation
• 00:45:59 – Video Course Spotlight
• 00:47:11 – Looking for good first issues
• 00:52:04 – Is this a bug?
• 00:54:10 – Proposing a new feature
• 00:56:36 – Data Morph and working on personal projects
• 01:07:29 – Showing up in the Python community
• 01:12:14 – What are you excited about in the world of Python?
• 01:14:59 – How can people follow the work you do online?
• 01:15:28 – What do you want to learn next?
• 01:18:23 – Thanks and goodbye

Show Links:

• 5 Ways to Get Started in Open Source - by Stefanie Molin - Level Up Coding
• seaborn: Statistical Data Visualization - Documentation
• Docstring Validation using Pre-Commit Hook - numpydoc
• Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition - Amazon.com
• Episode #173: Getting Involved in Open Source & Generating QR Codes With Python – The Real Python Podcast
• Episode #8: Docker + Python for Data Science and Machine Learning With Tania Allard – The Real Python Podcast
• Mentored Sprints for Diverse Beginners - PyCon US 2023
• Episode #177: Welcoming PyPI’s Safety & Security Engineer Mike Fiedler – The Real Python Podcast
• Data Morph: Moving Beyond the Datasaurus Dozen - Level Up Coding
• Data Morph: A Cautionary Tale of Summary Statistics – Slides
• Anscombe’s quartet - Wikipedia
• How to Set Up Pre-Commit Hooks - Stefanie Molin
• Dog Speaks In Italian Accent To Sound Like His Owner - Bored Panda
• Stefanie Molin (@StefanieMolin) / X
• Stefanie Molin - Personal Website

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Data Cleaning With pandas and NumPy
• Documenting Python Projects With Sphinx and Read the Docs

Support the podcast & join our community of Pythonistas

How does a debugger work? What can you learn about Python by building one from scratch? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a two-part tutorial on building a debugger in Python? Creating a simple one requires less code than you might think.

We also talk about an article from Itamar Turner-Trauring about how to prepare for the upcoming changes to NumPy. The new version is not backward compatible and will require some inspection of your project dependencies. Itamar includes advice, techniques, and tools for updating your code.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about managing advice as a new developer, moving to Python as a former R developer, building a Markov chain to generate readable nonsense, optimizing fonts to individual glyphs on your website, and a project for working with units of measurement in Python.

This week’s episode is brought to you by Posit Connect.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – DjangoCon Europe 2024 CFP Now Open
• 00:02:38 – Python Insider: Python 3.13.0 alpha 3 is now available
• 00:03:04 – NumPy 2 Is Coming: Preventing Breakage, Updating Your Code
• 00:07:37 – Using a Markov Chain to Generate Readable Nonsense
• 00:12:53 – Sponsor: Posit Connect
• 00:13:43 – Python Rgonomics
• 00:20:10 – Let’s Create a Python Debugger Together
• 00:23:49 – Video Course Spotlight
• 00:25:08 – Advice for New Devs Who’ve Read Other Advice Essays
• 00:42:44 – Fontimize: Optimize Fonts to the Glyphs on Your Site
• 00:44:57 – Pint: Units for Python
• 00:46:55 – Thanks and Goodbye

News:

• DjangoCon Europe 2024 CFP Now Open
• Python Insider: Python 3.13.0 alpha 3 is now available

Show Links:

• NumPy 2 Is Coming: Preventing Breakage, Updating Your Code – NumPy 2 is coming, and it’s backwards incompatible. Learn how to keep your code from breaking, and how to upgrade.
• Using a Markov Chain to Generate Readable Nonsense – Describes a simple Markov chain algorithm to generate reasonable-sounding but utterly nonsensical text, and presents some example outputs as well as a Python implementation in only 20 lines of code.
• Python Rgonomics – If you’re coming to Python from R, this article outlines some libraries that have an R-like feel, helping you make the transition to Pythonic workflows.
• Let’s Create a Python Debugger Together – Ever wondered how a debugger works? Implementing a simple one requires less code than you might think. Read on to find out how.

Discussion:

• Advice for New Devs Who’ve Read Other Advice Essays – After reading some programming advice posts, this author decided a lot of them concentrated on the wrong things. Here is his own take.
• Associated Hacker News Discussion

Projects:

• Fontimize: Optimize Fonts to the Glyphs on Your Site
• Pint: Units for Python

Additional Links:

• Let’s create a Python Debugger together: Part 2 - Mostly nerdless
• “Making Hard Things Easy” by Julia Evans (Strange Loop 2023) - YouTube
• Episode #71: Start Using a Debugger With Your Python Code – The Real Python Podcast
• Debugging Rules! – Find out what’s wrong with anything, fast.

Level up your Python skills with our expert-led courses:

• Debugging in Python With pdb
• Using Python's assert to Debug and Test Your Code
• Create a Tic-Tac-Toe Python Game Engine With an AI Player

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How can you measure the quality of a large language model? What tools can measure bias, toxicity, and truthfulness levels in a model using Python? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to discuss techniques and tools for evaluating LLMs With Python.

Jodie provides some background on large language models and how they can absorb vast amounts of information about the relationship between words using a type of neural network called a transformer. We discuss training datasets and the potential quality issues with crawling uncurated sources.

We dig into ways to measure levels of bias, toxicity, and hallucinations using Python. Jodie shares three benchmarking datasets and links to resources to get you started. We also discuss ways to augment models using agents or plugins, which can access search engine results or other authoritative sources.

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

Topics:

• 00:00:00 – Introduction
• 00:02:19 – Testing characteristics of LLMs with Python
• 00:04:18 – Background on LLMs
• 00:08:35 – Training of models
• 00:14:23 – Uncurated sources of training
• 00:16:12 – Safeguards and prompt engineering
• 00:21:19 – TruthfulQA and creating a more strict prompt
• 00:23:20 – Information that is out of date
• 00:26:07 – WinoBias for evaluating gender stereotypes
• 00:28:30 – BOLD dataset for evaluating bias
• 00:30:28 – Sponsor: Intel
• 00:31:18 – Using Hugging Face to start testing with Python
• 00:35:25 – Using the transformers package
• 00:37:34 – Using langchain for proprietary models
• 00:43:04 – Putting the tools together and evaluating
• 00:47:19 – Video Course Spotlight
• 00:48:29 – Assessing toxicity
• 00:50:21 – Measuring bias
• 00:54:40 – Checking the hallucination rate
• 00:56:22 – LLM leaderboards
• 00:58:17 – What helped ChatGPT leap forward?
• 01:06:01 – Improvements of what is being crawled
• 01:07:32 – Revisiting agents and RAG
• 01:11:03 – ChatGPT plugins and Wolfram-Alpha
• 01:13:06 – How can people follow your work online?
• 01:14:33 – Thanks and goodbye

Background Links:

• A Beginner’s Guide to Language Models - Built In
• ChatGPT - Explained! - YouTube

Dataset Links:

• truthful_qa - Datasets at Hugging Face
• wino_bias - Datasets at Hugging Face
• bold - Datasets at Hugging Face

Tutorials and Documentation for Python Packages:

• Evaluating Language Model Bias with 🤗 Evaluate
• Hugging Face - HF_bias_evaluation - Google Colab
• General Usage - Load a Dataset - Hugging Face
• What is Text Generation? - Hugging Face
• 🤗 Evaluate - Library Evaluating ML Models
• Python Quickstart - 🦜️🔗 Langchain

Measurement Links:

• Toxicity - a Hugging Face Space by evaluate-measurement
• Regard - a Hugging Face Space by evaluate-measurement
• Open LLM Leaderboard - a Hugging Face Space

Training Data for LLMs:

• Common Crawl - Open Repository of Web Crawl Data
• The Pile
• The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora

Agents and Plugin Links:

• Transformers Agents - Hugging Face
• Agents - 🦜️🔗 Langchain
• ChatGPT Gets Its “Wolfram Superpowers”! - Stephen Wolfram

Additional Links:

• Inside the AI Factory: The Humans that Make Tech Seem Human - The Verge
• Jodie Burchell - The JetBrains Blog
• Jodie Burchell’s Blog - Standard error
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) - Twitter
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl@fosstodon.org) - Fosstodon
• JetBrains: Essential tools for software developers and teams

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Creating Web Maps From Your Data With Python Folium
• Learn Text Classification With Python and Keras

Support the podcast & join our community of Pythonistas

Do you need to transfer an extensive data collection for a science project? What’s the best way to send executable code over the wire for distributed processing? What are the different ways to serialize data in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a tutorial by Real Python author Bartosz Zaczyński called “Serialize Your Data With Python.” This comprehensive guide moves beyond XML and JSON to explore multiple data formats and their potential use cases. It’s a deep dive into the topic and provides a thorough resource for future reference.

We also discuss a Real Python tutorial about naming conventions in Python that use single and double underscores. The piece covers differentiating between public and non-public names in APIs, writing safe classes for subclassing purposes, and avoiding name clashes with keywords.

We also share several other articles and projects from the Python community, including a couple of release announcements and news items, a discussion about never being taught how to construct quality software, building a small REPL in Python, using the key parameter in Python functions and methods, a framework for RESTful APIs using Flask and SQLAlchemy, and a Rust-based HTML sanitizer for your Python projects.

Topics:

• 00:00:00 – Introduction
• 00:02:47 – JIT Coming to Python 3.13
• 00:03:13 – A copy-and-patch JIT compiler - Pull Request #113465
• 00:03:54 – Django bugfix releases issued: 4.2.9 and 5.0.1
• 00:04:24 – Single and Double Underscores in Python Names
• 00:16:42 – Building a Small REPL in Python
• 00:20:07 – The Key to the key Parameter in Python
• 00:25:23 – Video Course Spotlight
• 00:26:44 – Serialize Your Data With Python
• 00:30:23 – You Are Never Taught How to Build Quality Software
• 00:48:43 – flask-muck: RESTful APIs Using Flask and SQLAlchemy
• 00:51:26 – nh3: Python binding to Ammonia HTML sanitizer Rust crate
• 00:53:33 – Thanks and goodbye

News:

• JIT Coming to Python 3.13 – Slides related to the upcoming JIT commit for Python 3.13. Note that GitHub paginates the slides if you don’t download them, so click the More Pages button to keep reading.
• A copy-and-patch JIT compiler by brandtbucher - Pull Request #113465
• Django bugfix releases issued: 4.2.9 and 5.0.1 - Weblog Django

Show Links:

• Single and Double Underscores in Python Names – In this tutorial, you’ll learn a few Python naming conventions involving single and double underscores (_). You’ll learn how to use this character to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.
• Building a Small REPL in Python – Learn how to write your own REPL by building on top of the one that comes with Python. With a few lines of code, you can customize Python’s REPL environment as your own.
• The Key to the key Parameter in Python – A parameter named key is present in several Python functions, such as sorted(). This article explores what it is and how to use it.
• Serialize Your Data With Python – In this in-depth tutorial, you’ll explore the world of data serialization in Python. You’ll compare and use different data serialization formats, serialize Python objects and executable code, and handle HTTP message payloads.

Discussion:

• You Are Never Taught How to Build Quality Software – Learning how to build quality software isn’t part of computer science education. How do we learn it?
• Hacker News discussion about the article

Projects:

• flask-muck: RESTful APIs Using Flask and SqlAlchemy
• nh3: Python binding to Ammonia HTML sanitizer Rust crate

Additional Links:

• PEP 8: The Style Guide for Python Code
• Python’s Magic Methods: Leverage Their Power in Your Classes – Real Python
• A Philosophy of Software Design Book - John Ousterhout
• A Philosophy of Software Design - John Ousterhout - Talks at Google - YouTube
• Episode #49: The Challenges of Developing Into a Python Professional – The Real Python Podcast
• Django: Sanitize Incoming HTML Fragments With nh3 – Allowing users to input HTML in comments or blog posts is problematic and can lead to exploits on your site. For years, the Django community used django-bleach, but since its deprecation, Adam has been using nh3, a Rust-based HTML sanitizer.
• nh3 · PyPI

Level up your Python skills with our expert-led courses:

• Working With JSON in Python
• Writing Beautiful Pythonic Code With PEP 8
• Serializing Objects With the Python pickle Module

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Are you interested in using your Python skills within Excel? Would you like to share a data science project or visualization as a single Office file? This week on the show, we speak with Principal Architect John Lam and Sr. Cloud Developer Advocate Sarah Kaiser from Microsoft about Python in Excel.

John shares the multi-year journey of adding Python to Excel. He describes how the project moved beyond writing user functions in Python to something much more elaborate. He details assembling a team with diverse skills in interface design, languages, and security.

Sarah discusses the instant convenience of having familiar Python and pandas techniques at your fingertips inside Excel. We cover typical data science workflows and the potential of interactive visualizations within a spreadsheet. We also share multiple resources for you to learn more.

Note: Python in Excel is currently a preview accessible by joining the Microsoft 365 Insider Program and selecting the Beta Channel.

Topics:

• 00:00:00 – Introduction
• 00:01:53 – Sr. Cloud Developer Advocate Sarah Kaiser
• 00:02:46 – Principal Architect John Lam
• 00:04:08 – What is Dev Div?
• 00:04:33 – Python data science inside Excel
• 00:09:05 – Designing features with a focus on sharing
• 00:14:28 – Moving between Excel and Python objects
• 00:18:20 – What libraries are imported by default?
• 00:23:11 – Sharing a workbook with others
• 00:26:12 – Recalculating data workflow
• 00:30:07 – Working in Jupyter Notebook vs Excel
• 00:33:03 – Creating a Python object
• 00:33:38 – Video Course Spotlight
• 00:35:02 – More history and project team
• 00:40:19 – Immediate wins of having Python in Excel
• 00:42:28 – Interactive visualizations
• 00:44:34 – Answering security concerns
• 00:49:17 – Limitations and potential
• 00:54:34 – Creating demo projects
• 01:00:25 – Resources to learn more
• 01:02:59 – What are you excited about in the world of Python?
• 01:10:41 – What do you want to learn next?
• 01:12:09 – How can people follow your work online?
• 01:13:26 – Thanks and goodbye

Show Links:

• Python in Excel – Python to Excel - Microsoft 365
• Get started with Python in Excel - Microsoft Support
• Python in Excel DataFrames - Microsoft Support
• Open-source libraries and Python in Excel - Microsoft Support
• User guide and tutorial - seaborn 0.13.1 documentation
• Assessing and Restoring Reproducibility of Jupyter Notebooks - IEEE Conference Publication - IEEE Xplore
• Book of Python in Excel - John Lam’s Website
• GitHub - microsoft/python-in-excel - Python in Microsoft Excel
• Use Python in Excel to enhance your data science - Python Day - YouTube
• Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization - Microsoft Community Hub
• PEP 703 – Making the Global Interpreter Lock Optional in CPython - peps.python.org
• Dr. Sarah Kaiser (@crazy4pi314@mathstodon.xyz) - Fosstodon
• John Lam (@john_lam) - X

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn
• The pandas DataFrame: Working With Data Efficiently

Support the podcast & join our community of Pythonistas

Three members of the Real Python team are joining us this week: Kate Finegan, Tappan Moore, and Philipp Acsany. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, code conversations, and video courses that showcase what our team created this year.

Kate helps to shepherd articles through the multi-stage editing process. She and the rest of the team ensure these resources impart crucial Python knowledge and provide a thorough didactic experience. Kate was also instrumental in helping introduce a new group of tutorial authors to the Real Python editorial process and house style.

Philipp returns to the podcast after our conversation earlier this year, and it was great to talk to him about onboarding new video instructors. Tappan edits all our video courses and ensures that the sound, picture, and animations are just right. He also helped provide feedback to the new instructors on our video creation process.

We hope you enjoy this review! We look forward to bringing you another year full of great guests, articles, and topics.

Topics:

• 00:00:00 – Introduction
• 00:02:50 – New Video Instructors
• 00:05:31 – New Tutorial Authors
• 00:07:37 – Shout-out to Aldren Santos
• 00:09:27 – Build a Wordle Clone With Python and Rich
• 00:11:37 – Python Classes: The Power of Object-Oriented Programming
• 00:13:43 – SOLID Principles: Improve Object-Oriented Design in Python
• 00:14:47 – Using the NumPy Random Number Generator
• 00:17:58 – Recursion in Python
• 00:19:44 – Filtering Iterables With Python
• 00:21:04 – Creating Web Maps From Your Data With Python Folium
• 00:24:51 – Video Course Spotlight
• 00:26:13 – Python Basics Exercises: Building Systems With Classes
• 00:31:00 – Real Python Quizzes
• 00:33:53 – Process Images Using the Pillow Library and Python
• 00:36:00 – How to Sort Unicode Strings Alphabetically in Python
• 00:38:36 – The Python Rich Package: Unleash the Power of Console Text
• 00:42:31 – Embeddings and Vector Databases With ChromaDB
• 00:46:41 – Advent of Code: Solving Puzzles With Python
• 00:51:07 – Thanks and goodbye

Show Links:

• Build a Wordle Clone With Python and Rich – Step-by-Step Project
• Python Classes: The Power of Object-Oriented Programming – Tutorial
• SOLID Principles: Improve Object-Oriented Design in Python – Tutorial
• Using the NumPy Random Number Generator – Tutorial
• Recursion in Python – Video Course
• Filtering Iterables With Python – Video Course
• Creating Web Maps From Your Data With Python Folium – Video Course
• Python Basics Exercises: Building Systems With Classes – Video Course
• Process Images Using the Pillow Library and Python – Video Course
• How to Sort Unicode Strings Alphabetically in Python – Tutorial
• The Python Rich Package: Unleash the Power of Console Text – Showcase
• Embeddings and Vector Databases With ChromaDB – Tutorial
• Advent of Code: Solving Puzzles With Python – Code Conversation