What are the recent advances in the field of quantum computing and high-performance computing? And what Python tools can you use to develop programs that run on quantum computers? This week on the show, Real Python author Negar Vahid discusses her tutorial, “Quantum Computing Basics With Qiskit.” Negar digs into the fundamentals of quantum computers, describing qubits, superposition, entanglement, and interference. We discuss the concept of quantum advantage and the fields of exploration where quantum computing promises speed-ups over classical computers. She also shares tools for designing quantum circuits with Python. Course Spotlight: Profiling Performance in Python Learn to profile Python programs with built-in and popular third-party tools, and turn performance insights into faster code. Topics: 00:00:00 – Introduction 00:01:31 – Writing for Real Python 00:02:37 – What drew you to quantum computing? 00:04:27 – What is quantum advantage? 00:07:10 – Quantum computing basics article 00:09:32 – Linear algebra 00:10:32 – What is a quantum computer? 00:14:52 – Superconducting devices 00:17:29 – Looking for ways to advance computing 00:19:17 – Superposition of qubits and entanglement 00:22:43 – Video Course Spotlight 00:24:27 – Potential areas of research 00:26:45 – IBM quantum computing & Qiskit 00:29:43 – Describing superposition as a spinning coin 00:30:41 – Other types of quantum computers 00:32:08 – Qiskit Global Summer School 2025 00:32:48 – Qiskit Advocate 00:33:49 – What do you see in the near future? 00:37:42 – What are the type of HPCs? 00:40:05 – Additional resources to learn more 00:43:32 – Answering to skeptics 00:47:51 – What are you excited about in the world of Python? 00:48:24 – What do you want to learn next? 00:49:04 – What’s the best way to follow your work online? 00:49:25 – Thanks and goodbye Show Links:

What are the steps to get started building a FastAPI application? What are the different types of concurrency available in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent Real Python step-by-step tutorial about programming a FastAPI example application. You practice installing FastAPI, building your first endpoints, adding path and query parameters, and validating endpoints using Pydantic. Christopher covers updates to his Real Python video course about concurrency in Python. The course digs into what concurrency means in Python and why you might want to incorporate it in your code. He describes the different methods and demonstrates how to approach coding using threading, asyncio, and multiprocessing. We also share several other articles and projects from the Python community, including a news roundup, the PSF fundraiser campaign for 2025, where Python stores attributes, performance hacks for faster Python code, a project to transform functions into a web interface, and a Python disk-backed cache. Course Spotlight: Python Descriptors Learn what Python descriptors are, how the descriptor protocol works, and when descriptors are useful, with practical, hands-on examples. Topics: 00:00:00 – Introduction 00:02:18 – Django Security Release 00:02:46 – Django Is Now a CVE Numbering Authority (CNA) 00:03:53 – An Annual Release Cycle for Django 00:04:12 – PEP 810: Explicit Lazy Imports (Accepted) 00:04:27 – PSF Board Office Hour Sessions for 2026 00:05:42 – PyCon US 2026: Call for Proposals Open 00:06:15 – PSF Fundraiser campaign for 2025 00:10:12 – A Close Look at a FastAPI Example Application 00:16:36 – Speed Up Python With Concurrency 00:21:08 – __dict__: Where Python Stores Attributes 00:25:59 – Video Course Spotlight 00:27:17 – 10 Smart Performance Hacks for Faster Python Code 00:29:56 – FuncToWeb: Transform Python Functions Into a Web Interface 00:32:48 – python-diskcache: Python Disk-Backed Cache 00:34:07 – Thanks and goodbye

How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, “Production Ready Data Science.” Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production. Topics: 00:00:00 – Introduction 00:01:27 – Recent article about top six visualization libraries 00:02:19 – How long have you been blogging? 00:03:55 – What do you cover in your book? 00:07:07 – Potential issues with notebooks 00:11:40 – Structuring data science projects 00:15:12 – Reproducibility and sharing notebooks 00:20:33 – Using Polars 00:26:03 – Advantages of marimo notebooks 00:34:21 – Video Course Spotlight 00:35:44 – Shipping a project in data science 00:42:10 – Advice on testing 00:49:50 – Creating importable parameter values 00:53:55 – Seeing the commit diff of a notebook 00:55:12 – What are you excited about in the world of Python? 00:56:04 – What do you want to learn next? 00:56:52 – What’s the best way to follow your work online? 00:58:28 – Thanks and goodbye Show Links: Production Ready Data Science by Khuyen Tran - CodeCut CodeCut Top 6 Python Libraries for Visualization: Which One to Use? - CodeCut Ruff uv Cookiecutter marimo - a next-generation Python notebook Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python Polars — DataFrames for the new era

What are techniques for writing maintainable Python code? How do you make your Python more readable and easier to refactor? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent article about writing code that is easy to maintain. We cover writing comments, creating meaningful names, avoiding magic numbers, and preparing code for your future self. We also share several other articles and projects from the Python community, including release news, modifying the REPL, differences between Polars and pandas, generating realistic test data in Python, investigating quasars with Polars and marimo, creating simple meta tags for Django objects, and a GUI toolkit for grids of buttons. Course Spotlight: Modern Python Linting With Ruff Ruff is a blazing-fast, modern Python linter with a simple interface that can replace Pylint, isort, and Black—and it’s rapidly becoming popular. Topics: 00:00:00 – Introduction 00:01:53 – PyTorch 2.9 Release 00:02:38 – Django 6.0 Beta 1 00:03:05 – Handy Python REPL Modifications 00:11:06 – Polars vs pandas: What’s the Difference? 00:17:55 – Faker: Generate Realistic Test Data in Python 00:22:06 – Video Course Spotlight 00:23:35 – Investigating Quasars With Polars and marimo 00:27:37 – Writing Maintainable Code 00:49:48 – buttonpad: GUI Toolkit for Grids of Buttons 00:52:10 – django-snakeoil: Simple Meta Tags for Django Objects 00:54:07 – Thanks and goodbye News: PyTorch 2.9 Release Django 6.0 Beta 1 Show Links: Handy Python REPL Modifications – Trey uses the the Python REPL a lot. In this post he shows you his favorite customizations to make the REPL even better. Polars vs pandas: What’s the Difference? – Discover the key

How do you deploy your Python application without getting locked into an expensive cloud-based service? This week on the show, Michael Kennedy from the Talk Python podcast returns to discuss his new book, “Talk Python in Production.” Michael runs multiple Python applications online, including a training site, blog, and two podcasts. While searching for the best solution for hosting his business, he documented his findings in a book. We talk about containerizing Python applications, generating static sites, preparing for traffic spikes, and avoiding cloud service lock-in. Course Spotlight: Speed Up Python With Concurrency Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing. Topics: 00:00:00 – Introduction 00:01:28 – Welcome back! 00:03:05 – Is this your first book? 00:04:13 – A book that reads like a blog 00:06:15 – Incentives to keep you locked in 00:09:20 – Following the journey of the Talk Python sites 00:11:47 – Audio reader briefs 00:15:19 – Discussing Dev Ops as a topic 00:18:31 – Background of developing for the web 00:20:14 – Stack-Native vs Cloud-Native 00:24:40 – Using Quart web framework 00:25:50 – Embracing Docker 00:32:39 – Sharing a single powerful machine allows for individual peaks 00:37:04 – Video Course Spotlight 00:38:30 – Minimal cloud lock-in 00:40:04 – Using OrbStack for local builds and testing 00:42:07 – Coolify as a Docker host 00:47:14 – Moving away from Google analytics and a GDPR rant 00:50:43 – Diving deep into web tech of ngnix, SSL, and CDNs 00:54:33 – Talking about the prices for hosting 00:59:09 – Creating static sites 01:06:22 – Invitation to come back to discuss AI and agents 01:10:06 – What are you excited about in the world of Python? 01:16:19 – What do you want to learn next? 01:17:34 – What’s the best way to fol

How does Python 3.14 perform under a few hand-crafted benchmarks? Does the performance of asyncio scale on the free-threaded build? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. At the top of the show, we have a mountain of release news and Python Enhancement Proposals to cover. Then, we dig into a couple of articles covering the performance of Python 3.14. The first is a benchmarking comparison of the last several Python versions and their variations, including JIT and free-threaded mode. The second explores the changes in 3.14 that enable asyncio to scale on CPython’s free-threaded build. We also share several other articles and projects from the Python community, including an introduction to NiceGUI, a free-threaded Python library compatibility checker, an exploration of what is “good taste” in software engineering, HTML templating with t‑strings, and a tool for testing Sphinx docs instantly in the browser. Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read the Docs to automatically build and publish your code documentation. Topics: 00:00:00 – Introduction 00:02:28 – Python 3.12.12, 3.11.14, 3.10.19 and 3.9.24 now available 00:03:08 – Python 3.13.9 is now available 00:03:26 – Python 3.15.0 alpha 1 00:04:02 – PEP 804: An External Dependency Registry and Name Mapping Mechanism 00:04:56 – PEP 806: Mixed Sync/Async Context Managers With Precise Async Marking 00:06:34 – PEP 807: Index Support for Trusted Publishing 00:07:21 – PEP 809: Stable ABI for the Future 00:08:10 – PEP 810: Explicit Lazy Imports 00:10:31 – Python lazy imports you can use today 00:10:48 – Lazy Imports Using wrapt 00:11:18 – Python 3.14 Is Here. How Fast Is It? 00:17:45 – Free-Threaded Python Library Compatibility Checker 00:19:54 – Scaling Asyncio on Free-Threaded Python 00:24:06 – Real P

How is teaching young students Python changing with the advent of LLMs? This week on the show, Kelly Schuster-Paredes from the Teaching Python podcast joins us to discuss coding and AI in the classroom. Kelly shares her current thoughts on teaching Python to young students. She stresses that the earliest classes still need to cover the fundamentals. We also discuss how coding instruction is evolving toward reading and reviewing code more than writing it. Course Spotlight: Building a Python GUI Application With Tkinter In this video course, you’ll learn the basics of GUI programming with Tkinter, the de facto Python GUI framework. Master GUI programming concepts such as widgets, geometry managers, and event handlers. Then, put it all together by building two applications: a temperature converter and a text editor. Topics: 00:00:00 – Introduction 00:01:28 – Starting to use AI in the classroom 00:05:17 – How did students react to using NLP and NLTK? 00:06:40 – Teaching Python beginners now 00:12:31 – Code editor changes with LLM features 00:15:25 – Less in-classroom teaching 00:16:22 – What sparks interest in coding for students? 00:21:14 – Video Course Spotlight 00:22:44 – Exploratory robotics 00:25:56 – Do you need to struggle to learn? 00:32:52 – Working through frustration and the tendency not to read 00:36:37 – Mixed feelings on the changes to teaching 00:41:19 – Teaching through reading and code reviewing 00:45:33 – Lower-level classes are still about fundamentals 00:47:47 – Other areas of teaching using AI tools 00:51:29 – Improving prompting skills 00:54:22 – Using these tools to organize creativity 00:54:53 – What AI tools are working for you? 00:57:13 – Sharing knowledge and techniques with other teachers 01:00:02 – General advice for teachers 01:02:03 – What are you excited about in the world of Python? 01:02:52 – What do you want to learn next? 01:03:46 – How can people follow your work online? 01:04:10 –

Python 3.14 is here! Christopher Trudeau returns to discuss the new version with Real Python team member Bartosz Zaczyński. This year, Bartosz coordinated the series of preview articles with members of the Real Python team and wrote the showcase tutorial, “Python 3.14: Cool New Features for You to Try.” Christopher’s video course, “What’s New in Python 3.14”, covers the topics from the article and shows the new features in action. Christopher and Bartosz dug into the new release to create code examples showcasing the new features for the tutorial and course. We look at the enhanced and more colorful REPL experience, better error messages, safer hooks for live debugging, and deferred annotation evaluation. We also discuss template strings, Zstandard compression, and multiple performance improvements. We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.14. Course Spotlight: What’s New in Python 3.14 Covers Python 3.14’s key changes: free-threading, subinterpreters, t-strings, lazy annotations, new REPL features, and improved error messages. Topics: 00:00:00 – Introduction 00:02:03 – Cool New Features for You to Try - Tutorial 00:02:20 – What’s New in Python 3.14 - Video Course 00:02:54 – Even Friendlier Python REPL 00:05:05 – Allow “json” instead of “json.tool” 00:05:49 – Zstandard compression 00:06:37 – Working With Numbers 00:11:09 – Deferred Evaluation of Annotations 00:17:35 – More Helpful Error Messages 00:20:41 – Warnings in try…finally Blocks 00:22:08 – Safer Live Process Debugging 00:25:22 – Pathlib Improvements 00:26:50 – Additional assert methods for unittest 00:27:19 – Template Strings (T-strings) 00:31:01 – Free-threaded Build Updates 00:35:30 – Incremental Garbage Collector 00:37:59 – functools partial & placeholder improvement 00:40:13 – Video Course Spotlight 00:41:12 – Experimental JIT Builds 00:

What’s changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python. We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions. Stephen shares details about our upcoming eight-week live course, Python for Beginners: Code With Confidence. Check out realpython.com/live to learn more and reserve your spot. Course Spotlight: 11 Beginner Tips for Learning Python In this course, you’ll see several learning strategies and tips that will help you jumpstart your journey towards becoming a successful Python programmer. Topics: 00:00:00 – Introduction 00:02:36 – Martin’s teaching background 00:02:57 – Stephen’s teaching background 00:04:07 – Having a vested interest in learning 00:08:35 – No shortcut to learning the fundamentals 00:09:21 – Parallels to learning a foreign language 00:12:43 – What’s different about starting to learn Python now? 00:15:03 – Stephen’s journey to coaching and using LLMs 00:16:20 – Are LLMs helpful for learning? 00:18:50 – Teaching what you’ve learned to someone else 00:19:38 – Learning how to ask good questions 00:22:11 – Improved error messages 00:24:35 – REPL: Read Evaluate Print Loop 00:26:33 – Video Course Spotlight 00:27:48 – Installing Python and choosing an editor 00:35:16 – Considering the scale of beginner projects 00:37:39 – Should a beginner be concerned with making Pythonic code? 00:40:55 – Using LLM tools and defining your level of skill 00:42:39 – Python for Beginners: Code With Confidence - live course

What’s a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article exploring the use of feature flags. The piece advises targeting specific users, storing schemas, and relying on well-worn code paths. He also discusses the danger of the exponential growth of functionality tests as feature flags are added. We dig into a comparison of six popular Python visualization libraries. The article provides code examples along with their respective visual output to highlight features and make selecting the optimal library for your work easier. We also share several other articles and projects from the Python community, including a news roundup, benchmarking MicroPython, simplifying IPs and networks in Python, a “scream” cipher, and a browser-based graphical viewer for the output of Python’s cProfile module. This episode is sponsored by InfluxData. Course Spotlight: Exploring Astrophysics in Python With pandas and Matplotlib This course uses three problems often covered in introductory astrophysics courses to explore in Python. Along the way, you’ll learn some astronomy and how to use a variety of data science libraries like NumPy, Matplotlib, pandas, and pint. Topics: 00:00:00 – Introduction 00:03:04 – Python 3.14.0rc3 is go! 00:03:14 – Django 6.0 alpha 1 released 00:04:27 – PEP 782: Add PyBytesWriter C API 00:05:06 – PEP 794: Import Name Metadata 00:05:30 – PEP 803: Stable ABI for Free-Threaded Builds 00:05:55 – Announcing the 2025 PSF Board Election Results! 00:06:18 – Top 6 Python Libraries for Visualization: Which One to Use? 00:18:34 – Sponsor: InfluxData 00:19:24 – Feature Flags in Depth 00:22:55 – Benchmarking MicroPython 00:30:02 – Video Course Spotlight 00:31:16 – Simplify IPs, Networks, and Subnets With the ipaddress 00:34:52 – SCREAM CIPHER (&ldquo

When converting an existing REST API to the Model Context Protocol, what should you consider? What anti-patterns should you avoid to keep an AI agent’s context clean? This week on the show, Kyle Stratis returns to discuss his upcoming book, “AI Agents with MCP”. Kyle has been busy since he last appeared on the show in 2020. He’s taken his experience working in machine learning startups and started his own consultancy, Stratis Data Labs. He’s been documenting his explorations working with LLMs and MCP on his blog, The Signal Path. Kyle is also writing a book about building MCP clients, services, and end-to-end agents. We discuss a recent article he wrote about the hazards of using an automated tool to convert a REST API into an MCP server. He shares his personal experiences with building MCP tools and provides additional resources for you to learn more about the topic. This episode is sponsored by InfluxData. Spotlight: Python for Beginners: Code With Confidence – Real Python Learn Programming Fundamentals and Pythonic Coding in Eight Weeks—With a Structured Course Topics: 00:00:00 – Introduction 00:02:41 – Updates on career 00:04:36 – The Signal Path - newsletter 00:07:15 – Moving into consulting 00:12:35 – Recent projects 00:14:51 – Need for data skills with MCP 00:16:49 – Describing the differences between REST APIs and MCP 00:19:59 – Interaction model differences 00:27:29 – Sponsor: InfluxData 00:28:21 – Agent stories 00:32:58 – Going through a simple example of MCP server 00:37:50 – Defining client and server 00:40:19 – Examples of servers currently 00:51:44 – Announcement: Python for Beginners: Code with Confidence 01:02:07 – Resources for further study 01:05:07 – Breaking down advice on moving an API to MCP 01:08:04 – What are you excited about in the world of Python? 01:18:20 – What do you want to learn next? 01:21:35 – How can people follow your work online? 01:22:46 – Thanks and goodbye Show Links:

What are your options for hosting your Python application or scripts? What are the advantages of a platform as a service, container-based hosts, or setting up a virtual machine? 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 hosting Python applications. The piece digs into the differences between using containers or platform-provided tools to automatically configure your project. We also cover an older article listing several free hosting options for smaller projects or Python scripts. The Python documentary premiered this past week, and we provide a synopsis and our thoughts. Following a similar theme, we cover our recent code conversation video course, which explores the history of Python versions and the features added across the releases. We also share several other articles and projects from the Python community, including a news roundup, regex affordances, four different ways to speed up your code, a Python-based music library manager, a tool to visualize the structure of your data in Python, and a project for exploring color science in Python. This episode is sponsored by InfluxData. Course Spotlight: A History of Python Versions and Features Explore Python’s evolution from the 1990s to today with a brief history and demos of key features added throughout its lifetime. Topics: 00:00:00 – Introduction 00:03:02 – PEP 728: TypedDict With Typed Extra Items 00:04:09 – Django security releases issued: 5.2.6, 5.1.12, and 4.2.24 00:04:37 – Python Type System and Tooling Survey 2025 00:04:56 – Python: The Documentary - An Origin Story 00:14:11 – A History of Python Versions and Features 00:16:58 – Sponsor: InfluxData 00:17:48 – Regex Affordances 00:21:34 – Where to Host Your Python App 00:26:48 – Best hosting platforms for Python applications and Python scripts 00:30:18 – Video Course Spotlight 00:31:59 – 330× Faster: Four Different Ways to Speed Up Your Code 00:37:18 – beets: Music Library Manager 00:39:12 – Memory Graph - GitHub 00

What’s happening with the latest releases of large language models? Is the industry hitting the edge of the scaling laws, and do the current benchmarks provide reliable performance assessments? This week on the show, Jodie Burchell returns to discuss the current state of LLM releases. The most recent release of GPT-5 has been a wake-up call for the LLM industry. We discuss how the current scaling of these systems is reaching a diminishing edge. Jodie also shares how many AI model assessments and benchmarks are flawed. We also take a sober look at the productivity gains from using these tools for software development within companies. We discuss how newer developers should consider additional factors when looking at the current job market. Jodie digs into how economic changes and rising interest rates are influencing layoffs and hiring freezes. Then we share a wide collection of resources for you to continue exploring these topics. This episode is sponsored by InfluxData. Course Spotlight: Exploring Python Closures: Examples and Use Cases Learn about Python closures: function-like objects with extended scope used for decorators, factories, and stateful functions. Topics: 00:00:00 – Introduction 00:03:00 – Recent conferences and talks 00:04:18 – What’s going on with LLMs? 00:06:06 – What happened with the GPT-5 release? 00:08:14 – Simon Willison - 2025 in LLMs so far 00:09:00 – How did we get here? 00:10:37 – OpenAI’s and scaling laws 00:12:25 – Pivoting to post-training 00:16:01 – Some history of AI eras 00:17:54 – Issues with measuring performance and benchmarks 00:22:19 – Chatbot Arena 00:24:06 – Languages are finite 00:26:22 – LLMs and the illusion of humanity 00:30:41 – Sponsor: InfluxData 00:31:34 – Types of solutions to move past these limits 00:36:57 – Does AI actually boost developer productivity? 00:44:19 – Agentic Al Programming with Python 00:48:02 – Results of non-programmers vibe coding 00:50:18 – Back to the concept of overfitting 00:52:52 –

What is a good way to add isolated, reusable functionality to Python classes? 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 about developing mixin classes to reuse code across multiple Python classes. He describes how mixins rely on multiple inheritance to combine features from different classes, enhancing flexibility and code reuse. We also share several other articles and projects from the Python community, including a news roundup, highlights from the 2024 Python Developers Survey, reasons you might not need a Python class, an exploration of asyncio’s sharp corners, an explanation of how JIT builds of CPython work, a web-based GUI library, and a project for quickly querying Python lists. This episode is sponsored by InfluxData. Course Spotlight: Design and Guidance: Object-Oriented Programming in Python In this video course, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that’s more maintainable, extensible, scalable, and testable. Topics: 00:00:00 – Introduction 00:03:14 – Python 3.13.7 Released 00:03:38 – Python 3.14.0rc2 Released 00:04:10 – PEP 802: Display Syntax for the Empty Set 00:04:59 – Announcing the PSF Board Candidates for 2025 00:05:26 – Rodrigo - PSF - Community Service Award for Q2 2025 00:06:06 – Python Developers Survey 2024 Results 00:13:27 – pyx: A Python-Native Package Registry 00:15:12 – Test & Code Final Episode 00:15:48 – You Might Not Need a Python Class 00:20:52 – Sponsor: InfluxData 00:21:44 – asyncio: A Library With Too Many Sharp Corners 00:25:43 – How JIT Builds of CPython Actually Work 00:35:21 – Video Course Spotlight 00:37:12 – What Are Mixin Classes in Python? 00:44:05 – nicegui: Create Web-Based UI With Python 00:46:51 – leopards: Quickly query your Python lists 00:49:00 &ndash

What went into developing the open-source Python tools data scientists use every day? This week on the show, we talk with Travis Oliphant about his work on SciPy, NumPy, Numba, and many other contributions to the Python scientific community. Travis discusses his initial involvement in the open-source community and how he discovered Python while working in biomedical imaging. He was trying to find ways to manage large sets of numerical data, which led to his initial contributions and collaborations in building scientific libraries. His appearance on the show coincides with the release of the Python documentary, in which he’s featured. We discuss the myriad organizations Travis founded, including Quansight, OpenTeams, and Anaconda. We dig into his underlying mission to continue fostering the growth of the open-source scientific computing community. This episode is sponsored by InfluxData. Course Spotlight: NumPy Techniques and Practical Examples In this video course, 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. Topics: 00:00:00 – Introduction 00:02:41 – Python documentary 00:07:44 – Getting involved in open source 00:12:04 – Numeric Python 00:15:36 – SciPy and the SciPy community 00:17:35 – Starting to think about entrepreneurship 00:18:16 – NumPy evolving from the work of Numeric 00:22:01 – Sponsor: InfluxData 00:22:53 – Python as controlling code for lower-level libraries 00:23:37 – Numba open-source JIT compiler 00:30:09 – Starting to build in Python before learning it all 00:34:45 – Python as the language AI generates 00:36:31 – Guilds and sharing knowledge 00:40:15 – More NumPy backstory 00:46:36 – Contributing to Python 00:48:24 – Video Course Spotlight 00:49:41 – The investment of companies in Python 00:51:22 – Quansight and busine

When considering an object-oriented programming problem, should you prefer inheritance or composition? Why wouldn’t it just be simpler to use functions? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article about structuring code using inheritance, composition, or only functions. We discuss how the piece is a good exploration of the trade-offs of each solution. Unlike the tutorials beginners typically encounter while learning the fundamentals, the article goes much deeper into the “why” of object-oriented programming. We also share several other articles and projects from the Python community, including a news roundup, processing audio in Python, reasons why you shouldn’t call dunder methods, smuggling arbitrary data through an emoji, an HTML to markdown converter, and a library to convert Python requests into curl commands. Course Spotlight: Single and Double Underscore Naming Conventions in Python In this video course, 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. Topics: 00:00:00 – Introduction 00:02:23 – Python 3.13.6 Released 00:02:47 – Django 5.2.5 Released 00:02:55 – Django REST Framework Release v3.16.1 00:03:09 – Narwhals v2.0.0 Released 00:03:22 – mypy 1.17 Released 00:03:42 – PEP 798: Unpacking in Comprehensions 00:04:11 – PEP 799: A Dedicated Profilers Package for Organizing Python Profiling Tools 00:06:12 – PyPI Users Email Phishing Attack 00:07:33 – Django in Action 00:08:00 – Call for proposals deadline - PyCon NL 00:08:59 – Python Audio Processing With pedalboard 00:18:49 – Smuggling Arbitrary Data Through an Emoji 00:21:53 – Don’t Call Dunder Methods 00:28:51 – Video Course Spotlight 00:30:27 – Inheritance Over Composition, Sometimes 00:40:03

What are the advantages of using Polars for your Python data projects? When should you use the lazy or eager APIs, and what are the benefits of each? This week on the show, we speak with Jeroen Janssens and Thijs Nieuwdorp about their new book, Python Polars: The Definitive Guide. Jeroen and Thijs describe how they were introduced to Polars while working at Xomnia. They were converting a large data project to Python and saw surprising speed increases using the new library. We discuss converting projects from pandas to Polars, getting away from indexes, consistent syntax, and using lazy vs eager APIs. Along the way, Jeroen and Thijs offer tips for getting the most out of Polars in your code. We dig into the process of writing a definitive guide and the advantages of working collaboratively on a book project. They also share resources for practicing data wrangling and building visualizations with Pydy Tuesday. Course Spotlight: Working With Python Polars Welcome to the world of Polars, a powerful DataFrame library for Python. In this video course, you’ll get a hands-on introduction to Polars’ core features and see why this library is catching so much buzz. Topics: 00:00:00 – Introduction 00:02:47 – Polars start at Xomnia 00:04:08 – Putting Polars into production 00:07:18 – Realizing the speed differences 00:08:49 – Converting the project from R to Python 00:14:34 – How did Polars improve the project? 00:16:34 – Making the code more ergonomic and readable 00:19:21 – Only grabbing the data that is needed 00:20:37 – Titling and deciding to write the book 00:24:40 – Advantages to collaboration 00:29:34 – What were you excited to include in the book? 00:31:55 – Working with different engines and Nvidia’s Cuda 00:35:05 – Defining a Polars expression 00:36:11 – Transitioning from pandas to Polars 00:37:34 – Not needing an index 00:39:56 – What inspired the syntax? 00:45:01 – Defining lazy vs eager workflows 00:49:16 – Examples covered in first chapter preview 00:51:51 &ndash

Do the design patterns learned in other programming languages translate to coding in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher dives into an article that tackles popular object-oriented design patterns from the famous Gang of Four book. These patterns offer solutions to common coding problems, but as Christopher explores, Python often doesn’t even have the problems these solutions try to fix. He discusses several common design patterns and the simpler, more Pythonic ways to achieve the same goals. We also share several other articles and projects from the Python community, including an exceptionally robust news roundup, running coverage on tests, an exploration of expert generalists, a preview of template strings from Python 3.14, a quiz on f-strings, and a project that calculates the complexity of your Python code. Course Spotlight: Working With Python’s Built-in Exceptions Learn the most common built-in Python exceptions, when they occur, how to handle them, and how to raise them properly in your code. Topics: 00:00:00 – Introduction 00:02:03 – Python 3.14.0b4 Released 00:02:11 – Python 3.14 release candidate 1 is go! 00:02:48 – PyPy v7.3.20 Release 00:03:00 – Textual 4.0.0 Released 00:03:23 – Announcing Toad - a universal UI for agentic coding in the terminal 00:03:42 – uv 0.8.0 Released 00:03:56 – Django Bugfix Release 5.2.4 00:04:14 – Django Community Ecosystem 00:04:52 – Happy 20th Birthday Django! 00:05:31 – PyData London 2025 Videos 00:05:48 – PEP 792: Project Status Markers in the Simple Index 00:06:09 – PEP 800 – Solid bases in the type system 00:07:06 – Run Coverage on Tests 00:14:32 – Design Patterns You Should Unlearn in Python 00:18:13 – Video Course Spotlight 00:19:24 – Expert Generalists 00:34:42 – Python 3.14 Preview: Template Strings (T-Strings) 00:41:00 – fstrings.wtf - Python F-String Quiz 00:43:09 – complexipy: Calculate Complexity of Your Python

What goes into supporting more than 650,000 projects and nearly a million users of the Python Package Index? This week on the show, we speak with Maria Ashna about her first year as the inaugural PyPI Support Specialist. Maria has a varied background in creative arts and neuroscience. She decided to apply for the PyPI support position, defying common misconceptions about who can take on roles inside the Python Software Foundation, and challenging imposter syndrome along the way. Her recent talks at PyCon US 2025 and EuroPython 2025 were about her experiences in the role. She describes tackling the backlogs of account recovery and PEP 541 requests, and we also discuss PyPI community and company organizations. Course Spotlight: Publishing Python Packages to PyPI In this video course, you’ll learn how to create a Python package for your project and how to publish it to PyPI, the Python Package Index. Quickly get up to speed on everything from naming your package to configuring it using setup.cfg. Topics: 00:00:00 – Introduction 00:01:42 – What led you to learn Python? 00:08:09 – PyCon 2025 talk about the first year at PyPI 00:11:06 – Embracing asking questions 00:13:55 – Being willing to say “I don’t know, let’s find out” 00:15:06 – What is PEP 541 and resolving name retention issues 00:23:22 – Video Course Spotlight 00:24:40 – Addressing the account recovery backlog 00:26:43 – PyPI Organizations 00:30:54 – Moving beyond the hesitancy to submit a package to PyPI 00:40:43 – Getting past imposter syndrome and applying 00:45:07 – What are you excited about in the world of Python? 00:46:10 – What do you want to learn next? 00:47:52 – How can people follow your work online? 00:49:03 – Thanks and goodbye Show Links: Adventures in Account Recovery, PEP 541 & More As Inaugural PyPI Support Specialist - Maria Ashna - YouTube PyCon US 2025 - A PEP Talk: Adventure

How does the performance of an algorithm hold up when you put it into a realistic context? Where might Python code defy Big O notation expectations when using a profiler? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article about why real-world performance often defies Big O expectations. The piece starts with a task coded in Go and then optimized from O(n²) to O(n). Can an interpreted language like Python compete with a compiled language? Profiling the performance of both versions provides some interesting results. We also share several other articles and projects from the Python community, including a news roundup, the fastest way to detect a vowel in a string, whether Python dictionaries are ordered data structures, an overview of Python’s enum module, a Python client library for Google Data Commons, and a project to convert plain ASCII to “smart” punctuation. Course Spotlight: Building Enumerations With Python’s enum In this video course, you’ll discover the art of creating and using enumerations of logically connected constants in Python. To accomplish this, you’ll explore the Enum class and other associated tools and types from the enum module in the Python standard library. Topics: 00:00:00 – Introduction 00:02:12 – ruff Release 0.12.0 00:02:38 – streamlit Release 1.46.0 00:02:48 – lxml 6.0.0 Released 00:03:00 – PSF Board Election Schedule 00:03:26 – Are Python Dictionaries Ordered Data Structures? 00:08:59 – The Fastest Way to Detect a Vowel in a String 00:16:37 – Module enum Overview 00:24:46 – Video Course Spotlight 00:26:24 – O(no) You Didn’t 00:38:34 – New Python Client Library for Google Data Commons 00:41:55 – smartypants.py: Plain ASCII to “Smart” Punctuation 00:44:07 – Thanks and goodbye News: ruff Release 0.12.0