The Real Python Podcast 6w232z

Looking for a guide on getting started with Marimo notebooks? How do you build a reproducible notebook for sharing or create a dashboard with interactive UI elements? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We cover a recent Real Python article by Ian Eyre ing Marimo notebooks. The tutorial covers installing Marimo, taking advantage of reactivity, building interactive dashboards, and managing a notebook’s environment through sandboxing. The piece ends by examining the limitations of traditional linear notebooks and how Marimo addresses them. Christopher discusses an article about how to store the configurations for your Python scripts and projects. Whether you’re managing resource handles to a database, deployment variables, or credentials to external services, you’ll need a way to save and load the details into your Python project. The piece compares saving configurations in several common file formats or through environment variables. We also share several other articles and projects from the Python community, including a news roundup, the discourse between generative AI coding proponents and detractors, catching memory leaks with your test suite, epigrams on programming, a command line tool to check packages on PyPI, and a collection of string, file, and object utilities. This episode is sponsored by Six Feet Up. Course Spotlight: Python Continuous Integration and Deployment Using 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. Topics: 00:00:00 – Introduction 00:02:44 – Python Release 3.14.0b2 00:03:09 – Django security releases issued: 5.2.2, 5.1.10, and 4.2.22 00:03:27 – PyBay 2025 00:03:43 – PyCon NL 2025 - Call for Proposals 00:04:05 – Django Forum: ing t-strings 00:04:44 – Ruff s: What Rules Are You Using and What Are You Ignoring? 00:05:19 – My Shot at Real Python 00:06:03 – My AI Skeptic Friends Are All Nuts 00:10:06 – I Think I’m Done Thinking About genAI For Now 00:11:12 – AI Changes Everything 00:23:01 – Video Course Spotlight 00:24:14 – Configuration of Python Applications 00:29:15 – Marimo: A Reactive, Reproducible Notebook 00:35:15 – Sponsor: Six Feet Up 00:36:02 – Catching memory leaks with your test suite 00:41:45 – Epigrams on Programming 00:46:54 – whatsonpypi: Check PyPI From the Command Line 00:48:18 – strif: String, File, and Object Utilities 00:49:44 – PyCoder’s Weekly - Submit a Link 00:50:13 – Thanks and goodbye News: Python Release 3.14.0b2 Django security releases issued: 5.2.2, 5.1.10, and 4.2.22 PyBay 2025 PyCon NL 2025 - Call for Proposals Django Forum: ing t-strings Ruff s: What Rules Are You Using and What Are You Ignoring? My Shot at Real Python – Amanda has recently written her first article for Real Python and this post talks about her experience doing so. If you want to check out the article, it’s on Nested Loops. Show Links: My AI Skeptic Friends Are All Nuts I Think I’m Done Thinking About genAI For Now AI Changes Everything AI Blog Comparison - Armin Ronacher Configuration of Python Applications – This post talks about how to store configuration for your script and how and when to load the information into your program. Marimo: A Reactive, Reproducible Notebook – Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks. Catching memory leaks with your test suite – “If you have a good test suite, you may be able to use pytest fixtures to identify memory and other resource leaks.” Discussion: Epigrams on Programming Alan Perlis - Wikipedia Projects: whatsonpypi: Check PyPI From the Command Line strif: String, File, and Object Utilities Additional Links: Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv Episode #250: DjangoCon Europe 2025: Live Recording From Dublin Episode #236: Simon Willison: Using LLMs for Python Development GenAI Criticism and Moral Quandaries - Armin Ronacher Math in Markdown - The GitHub Blog Quiz: Marimo: A Reactive, Reproducible Notebook Episode #42: What Is Data Engineering and Researching 10 Million Jupyter Notebooks We ed 10,000,000 Jupyter Notebooks From Github – This Is What We Learned Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python Episode #501 - Marimo - Reactive Notebooks for Python - Talk Python To Me Podcast Episode #24: Options for Packaging Your Python Application: Wheels, Docker, and More - Itamar Turner-Trauring Episode #128: Using a Memory Profiler in Python & What It Can Teach You Episode #172: Measuring Multiple Facets of Python Performance With Scalene PyCoder’s Weekly - Submit a Link Level up your Python skills with our expert-led courses: Python Continuous Integration and Deployment Using GitHub Actions Python mmap: Doing File I/O With Memory Mapping Testing Your Code With pytest the podcast & our community of Pythonistas

Once you’ve learned the vocabulary and syntax of the Python language, how do you progress into learning the right combinations to put into your code? How can Python’s built-in itertools library enhance your skills? This week on the show, we speak with Rodrigo Girão Serrão about teaching Python through his blog and his ion for the itertools library. We discuss Rodrigo’s different approaches to writing on his blog. He likes to document smaller concepts about Python and building code in his “Today I Learned” series. He’s also been collecting advice about the best way to use core Python features in another series called “Pydon’ts.” We cover his recent PyCon US tutorial about the built-in itertools module. The functions contained in the module create iterators for efficient looping. We discuss the categories of tools inside the collection and ways to simplify your code. We also explore the concept of vocabulary versus idioms in writing. Idioms are a group of words that hold a symbolic meaning that goes beyond the literal meaning of the individual words. We dig into how that applies to learning Python and building a personal collection of programming idioms. This episode is sponsored by AMD. Course Spotlight: Working With Missing Data in Polars In this video course, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them. Topics: 00:00:00 – Introduction 00:02:34 – Creating Polars video course 00:03:27 – How did you start programming and teaching Python? 00:04:59 – Where did mathspp come from? 00:05:38 – Exploring math and programming in university 00:07:48 – Learning APL 00:09:24 – What goes into building the blog? 00:15:05 – The Pydon’ts and writing books 00:18:37 – PyCon US 2025 00:20:46 – Sponsor: AMD 00:21:23 – Teaching a tutorial about itertools 00:28:58 – Categorizing itertools 00:40:39 – Video Course Spotlight 00:41:55 – The difference between me and Shakespeare 00:46:51 – Learning and practicing with idioms 00:51:01 – TIL and asking questions 00:53:54 – What are you excited about in the world of Python? 00:55:40 – What do you want to learn next? 00:57:35 – How can people follow your work online? 01:01:19 – Thanks and goodbye Show Links: mathspp blog TIL (Today I Learned) - mathspp Working With Missing Data in Polars Paul Valéry - “A poem is never finished” - Oxford Reference Personal highlights of PyCon US 2025 - mathspp PyCon US 2025 Lightning Talks - Friday, May 16th, 2025 PM - YouTube PyCon US 2025 Tutorial Sneak Peek: “Reimplement itertools for fun & profit” Rodrigo Girão Serrão - YouTube Alan Perlis - Wikipedia Epigrams on Programming What learning APL taught me about Python - mathspp What APL taught me about Python ⚡️ – lightning talk by Rodrigo Girão Serrão at EuroPython 2023 - YouTube itertools — Functions creating iterators for efficient looping — Python 3.13.4 documentation Module itertools overview - mathspp The little book of itertools - mathspp Python itertools By Example – Real Python What’s new in Python 3.14 — Python 3.15.0a0 documentation beehiiv — The newsletter platform built for growth Python drops 🐍💧 newsletter - mathspp Books - mathspp Rodrigo Girão Serrão 🐍🚀 (@mathspp.com) — Bluesky Rodrigo Girão Serrão - LinkedIn Rodrigo 🐍🚀 (@[email protected]) - Fosstodon Rodrigo 🐍🚀 (@mathsppblog) / X Level up your Python skills with our expert-led courses: Efficient Iterations With Python Iterators and Iterables Working With Python Polars Working With Missing Data in Polars the podcast & our community of Pythonistas

What are the ways you can manage multithreaded code in Python? What synchronization techniques are available within Python’s threading module? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher discusses his recent Real Python video course about thread safety. The course provides a quick overview of race conditions and how to use locks in your code. It then goes on to share a collection of additional synchronization primitives to make your code thread-safe. We dig into a tutorial by Leodanis Pozo Ramos about managing Python projects with uv. The tutorial shows you how to quickly initialize a project, build the directory structure, add dependencies, and publish your package while practicing the commands inside uv. We also share several other articles and projects from the Python community, including a news roundup, unraveling t-strings, what’s new in pip 25.1, an SVG-first plotting library, and a data modeling tool built on top of Polars and Pydantic. Course Spotlight: Thread Safety in Python: Locks and Other Techniques In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe. Topics: 00:00:00 – Introduction 00:02:23 – PEP 773: A Python Installation Manager for Windows 00:03:09 – PEP 784: Adding Zstandard to the Standard Library 00:03:28 – Python Insider: Python 3.14.0 Beta 1 Is Here! 00:03:48 – Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.2 00:04:09 – ty: New Type Checker and Language Server by Astral 00:05:01 – pyrefly: A Fast Type Checker and IDE for Python 00:06:03 – The Future of Textualize 00:07:08 – Managing Python Projects With uv 00:12:20 – pre-commit: Install With uv 00:13:03 – Python’s New t-strings 00:16:38 – Unraveling t-strings 00:18:33 – Video Course Spotlight 00:19:50 – What’s New in Pip 25.1 00:24:30 – Thread Safety in Python: Locks and Other Techniques 00:28:40 – glyphx: SVG-first Plotting Library 00:31:20 – patito: A data modeling layer built on top of Polars and Pydantic 00:34:02 – Thanks and goodbye News: PEP 773: A Python Installation Manager for Windows (Accepted) PEP 784: Adding Zstandard to the Standard Library (Accepted) Python Insider: Python 3.14.0 Beta 1 Is Here! Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.21 ty: New Type Checker and Language Server by Astral pyrefly: A Fast Type Checker and IDE for Python The Future of Textualize – Will McGugan, founder of Textualize the company has announced that they will be closing their doors. Textualize the open source project will remain. Show Links: Managing Python Projects With uv – In this tutorial, you’ll learn how to create and manage your Python projects using uv, an extremely fast Python package and project manager written in Rust. pre-commit: Install With uv – pre-commit is Adam’s favorite Git-integrated “run things on commit” tool. It acts as a kind of package manager, installing tools as necessary from their Git repositories. This post explains how to use it with uv. Python’s New t-strings – Using f-strings is a readable way of building output, but there are situations where they can’t be used because the contents need to be verified before being string-ified. The new t-strings, coming in 3.14, are a solution to this problem. Unraveling t-strings – PEP 750 introduced t-strings for Python 3.14. These are a template string mechanism similar to f-strings. Although they are in 3.14.0b1, there isn’t any documentation yet, so this post explains what they are how they can be used. What’s New in Pip 25.1 – pip 25.1 introduces for Dependency Groups (PEP 735), resumable s, and an installation progress bar. Dependency resolution has also received a raft of bugfixes and improvements. Thread Safety in Python: Locks and Other Techniques – In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe. Projects: glyphx: SVG-first Plotting Library JakobGM/patito: A data modeling layer built on top of Polars and Pydantic Additional Links: Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv pgjones/sql-tstring: SQL-tString allows for f-string like construction of sql queries PEP 787: Safer Subprocess Usage Using t-strings (Postponed to 3.15) davepeck/pep750-examples: Examples of using t-strings as defined in PEP 750 xkcd: Exploits of a Mom Little Bobby Tables - explain xkcd Level up your Python skills with our expert-led courses: Threading in Python Thread Safety in Python: Locks and Other Techniques Python Basics Exercises: Installing Packages With pip the podcast & our community of Pythonistas

What goes into making video courses at Real Python? How should you build an installable Django application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. This week, the Real Python Podcast is experiencing several firsts. We recorded a show in front of a live audience for the very first time, and it happened in Dublin, Ireland—a place neither of us had visited before. I also got to meet Christopher Trudeau in person for the first time. We’re sharing that live recording from the conference in this episode. We discuss how we create video courses at Real Python, and Christopher talks about his tutorial on how to write an installable Django application. We also share a few summaries of the talks from the conference and projects from the Django community, including a news roundup, how decisions are made inside the Django Foundation, ways you can help with reviews, using the Django ORM with Marimo notebooks, maintaining a data-oriented project, how to get foreign keys horribly wrong, a project for simple deployment, and a project for adding extra buttons inside the Django . We would like to thank the audience from Ireland who heard about DjangoCon by listening to the podcast. Thank you for attending the conference and for taking the time to say hello. We also appreciate those who asked us insightful questions at the end of the show. We enjoyed exploring Dublin and recording the show in front of such a welcoming audience. We learned a ton from all the great talks given at the conference and made some new connections for future interviews. This episode is sponsored by AMD. Course Spotlight: How to Set Up a Django Project In this course, you’ll learn the necessary steps that you’ll need to take to set up a new Django project. You’ll learn the basic setup for any new Django project, which needs to happen before programming the specific functionality of your project. Topics: 00:00:00 – Introduction 00:03:59 – PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials 00:04:22 – PEP 736 – Shorthand syntax for keyword arguments at invocation 00:04:46 – PEP 661 – Sentinel Values 00:05:21 – Pydantic v2.11 Released 00:05:41 – How We Build Video Courses at Real Python 00:17:17 – Sponsor: AMD 00:17:56 – How to Write an Installable Django App 00:22:21 – Attendees from Ireland who heard about the conference from us 00:23:09 – Django needs you! (to do code review) 00:24:12 – How we make decisions in Django 00:26:07 – Marimo: Sharing the joys of the Django ORM with Python Notebooks 00:27:30 – Steering Council Introduction 00:28:17 – Video Course Spotlight 00:29:45 – Data-Oriented Django Drei 00:31:04 – How to get Foreign Keys horribly wrong in Django 00:32:17 – Converting integer fields to bigint using Django migrations at scale 00:33:17 – django-simple-deploy 00:34:57 – django--extra-buttons 00:37:51 – What goes into creating the podcast? 00:44:04 – How does RP decide what Learning Paths to create? 00:48:30 – Python background when starting with a framework 00:54:46 – Django getting started resources at Real Python 00:55:34 – Thanks and goodbye News: PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials (Accepted) PEP 736 – Shorthand syntax for keyword arguments at invocation (Rejected) PEP 661 – Sentinel Values (Deferred) Pydantic v2.11 Released Show Links: DjangoCon Europe 2025 How We Build Video Courses at Real Python How to Write an Installable Django App – Real Python Django needs you! (to do code review) - Sarah Boyce How we make decisions in Django - Carlton Gibson Marimo and Jupyter: Sharing the joys of the Django ORM with Python Notebooks - Chris Adams Steering Council Introduction- Emma Delescholle Data-Oriented Django Drei - Adam Johnson How to get Foreign Keys horribly wrong in Django - Haki Benita Turn back time: Converting integer fields to bigint using Django migrations at scale - Tim Bell Projects: django-simple-deploy - readthedocs django--extra-buttons - PyPI Additional Links: django-awl Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python marimo - a next-generation Python notebook Data-Oriented Django - Adam Johnson - YouTube - DjangoCon 2022 Data Oriented Django Deux - Adam Johnson - YouTube - DjangoCon Europe 2024 Episode #165: Leveraging the Features of Your Database With Postgres and Python pgMustard - review Postgres query plans quickly Django Chat Django for Data Science: Deploying Machine Learning Models with Django - William Vincent Episode #500 - Django Simple Deploy and other DevOps Things - Talk Python To Me Podcast Episode #234: Building New Structures for Learning Python Reference – Real Python Django for Web Development (Learning Path) – Real Python Getting Started With Django: Building a Portfolio App – Video Course Your First Steps With Django: Set Up a Django Project – Tutorial Level up your Python skills with our expert-led courses: Getting Started With Django: Building a Portfolio App Sneaky REST APIs With Django Ninja How to Set Up a Django Project the podcast & our community of Pythonistas

What is the best way to record the Python dependencies for the reproducibility of your projects? What advantages will lock files provide for those projects? This week on the show, we welcome back Python Core Developer Brett Cannon to discuss his journey to bring PEP 751 and the pylock.toml file format to the community. Brett has been working on a way to move beyond the requirements.txt file for over six years. He was on the show previously to discuss his work on PEP 665, which was rejected. He decided to continue to push forward, authoring PEP 751 last year, which was accepted at the end of March this year. The PEP calls for a new file format to record your project’s dependencies. The goal was to have a standardized immutable record for what should be installed to reproduce your project in a virtual environment. He discusses working with other packaging projects and the compromises involved in creating a standard. Course Spotlight: Using the Python subprocess Module In this video course, you’ll learn how to use Python’s subprocess module to run and control external programs from your scripts. You’ll start with launching basic processes and progress to interacting with them as they execute. Topics: 00:00:00 – Introduction 00:02:38 – Brett’s roles within the Python community 00:05:41 – How to move beyond requirement.txt? 00:10:58 – What does the community use as project artifacts? 00:15:28 – Building on the success of pyproject.toml 00:17:44 – Introducing PEP 665 00:19:49 – Software Bills of Materials and security 00:25:20 – Back to lock files and security 00:31:08 – Video Course Spotlight 00:32:27 – Not giving up on the idea 00:34:01 – Leading into PEP 751 00:38:54 – Working toward a single multi-platform file 00:43:02 – The final push 00:48:54 – Leaving room for flexibility 00:53:50 – And it’s done, PEP 751 accepted unconditionally 00:58:06 – Keynote speaker at EuroPython 2025 00:58:45 – What are uv workspaces? 01:01:02 – Considering the use of lock files in data science 01:05:23 – Updates about Python for WASI and Emscripten 01:13:51 – Clarification on WASI 01:20:28 – Future conversation about Python launcher 01:23:04 – What are you excited about in the world of Python? 01:24:25 – What do you want to learn next? 01:28:41 – What’s the best way to follow your work online? 01:31:00 – Thanks and goodbye Show Links: Tall, Snarky Canadian BREAKING: Guido van Rossum Returns as Python’s BDFL - YouTube Python Packaging Guide PEP 751 – A file format to record Python dependencies for installation reproducibility PEP 665 – A file format to list Python dependencies for reproducibility of an application pylock.toml Specification - Python Packaging Guide Inline script metadata - Python Packaging Guide PEP 723 – Inline script metadata Using workspaces - uv Do you have a flag? - Eddie Izzard - YouTube OpenBLAS : An optimized BLAS library EuroPython 2025 - July 14 to 20, 2025 - Prague, Czech Republic & Remote Bytecode Alliance Recent conversations - Bytecode Alliance - Zulip My impressions of Gleam My impressions of ReScript Python on Exercism Brett Cannon’s Films - Letterboxd Media I Like - Open Source by Brett Cannon Brett Cannon (@snarky.ca) — Bluesky Brett Cannon (@[email protected]) - Fosstodon Level up your Python skills with our expert-led courses: Python Basics Exercises: Installing Packages With pip Everyday Project Packaging With pyproject.toml Using the Python subprocess Module the podcast & our community of Pythonistas

Are you looking for some projects where you can practice your Python skills? Would you like to experiment with building a generative AI app or an automated knowledge graph sentiment analysis tool? This week on the show, we speak with Raymond Camden about his journey into Python, his work in developer relations, and the Python projects featured on his blog. Raymond is a developer evangelist and advocate who works with APIs, AI, and the web. He’s been expanding his developer knowledge by learning Python and documenting his journey through his blog and with the live-streaming show Code Break. We discuss a couple of his recent Python projects. The first is building a resume review and revision system with generative AI and Flask. The other project uses Diffbot’s knowledge graph and Pipedream’s workflow tools to create an automated sentiment analysis tool. This episode is sponsored by AMD. Course Spotlight: What Can You Do With Python? In this video course, you’ll find a set of guidelines that will help you start applying your Python skills to solve real-world problems. By the end, you’ll be able to answer the question, “What can you do with Python?” Topics: 00:00:00 – Introduction 00:03:15 – Programming background and learning Python 00:07:59 – What’s been hard about learning a new language? 00:09:26 – Learning pip, managing packages, and suggesting uv 00:12:26 – Developer relations and sharing knowledge 00:15:17 – Moving things from Code Break to the blog 00:17:27 – Building a resume review and revise system with Gen AI 00:31:58 – Video Course Spotlight 00:33:16 – Adding the revision step 00:35:59 – Exploring code assistance 00:38:52 – Changing into the developer relations role 00:41:40 – Using Diffbot and Pipedream for sentiment analysis project 00:48:06 – Pipedream workflow with Python scripts 00:53:28 – What are you excited about in the world of Python? 00:55:45 – What do you want to learn next? 00:57:45 – How can people follow your work online? 00:58:03 – Thanks and goodbye Show Links: Raymond Camden Code Break - CFE.dev Exploring AI with Gemini and Transformers.js - CFE.dev Building a Resume Review and Revise System with Generative AI and Flask Flask Quickstart - Flask Documentation Get a Gemini API key - Google AI for Developers Automating and Responding to Sentiment Analysis with Diffbot’s Knowledge Graph Diffbot - Knowledge Graph, AI Web Data Extraction and Crawling Python Posts - Raymond Camden (27 Posts) Pipedream - Connect APIs, AI, databases, and more Geolocating a Folder of Images with Python Mastering Google Fu: An Expert’s Guide To Advanced Search Techniques uv - Astral Managing Python Projects With uv: An All-in-One Solution – Real Python Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv – The Real Python Podcast marimo - A next-generation Python notebook Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python Drumeo - Reach your drumming goals Jess Bowen Hears Rage Against The Machine For The First Time - YouTube REAL ID - Homeland Security PyCon US 2025 Raymond Camden - LinkedIn Raymond Camden (@[email protected]) - Mastodon Level up your Python skills with our expert-led courses: Creating a Scalable Flask Web Application From Scratch Python Basics: Installing Packages With pip What Can You Do With Python? the podcast & our community of Pythonistas

Are you looking for a fast database that can handle large datasets in Python? What’s the difference between a Python expression and a statement? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We cover a Real Python article that explores DuckDB and discuss creating a database by reading data from multiple file formats. When building queries, DuckDB uses standard SQL syntax, or for an object-oriented approach, you can chain methods together using the Python API. We also explore the advantages of lazy evaluation using DuckDB relations. Christopher digs into another Real Python tutorial that covers the differences between expressions and statements in Python. The piece goes beyond definitions to answer questions about where and when to use them in your code. We also share several other articles and projects from the Python community, including a news roundup, an investigation into the lack of security in M, a discussion on the differences between staff engineer and engineering manager roles, guidance on creating and modifying Word documents with Python, and a project to go beyond print for debugging your code. Check out realpython.com/workshops to the cohort of the Intermediate Python Deep Dive course. Course Spotlight: Creating a Python Dice Roll Application In this step-by-step video course, you’ll build a dice-rolling simulator app with a minimal text-based interface using Python. The app will simulate the rolling of up to six dice. Each individual die will have six sides. Topics: 00:00:00 – Introduction 00:02:24 – Python 3.14.0a7, 3.13.3, 3.12.10, 3.11.12, 3.10.17 and 3.9.22 are now available 00:02:47 – PEP 768: Safe External Debugger Interface for ython (Accepted) 00:03:16 – PEP 781: Make TYPE_CHECKING a Built-in Constant 00:03:43 – PEP 750: Template Strings (Accepted) 00:04:15 – PEP 751: A file format to record Python dependencies for installation reproducibility (Accepted) 00:05:20 – EuroPython July 14th-20th Prague, Tickets Available 00:05:42 – Django 5.2 Released 00:05:59 – Django security releases issued: 5.1.8 and 5.0.14 00:06:19 – Introducing DuckDB 00:12:19 – Expression vs Statement in Python: What’s the Difference? 00:17:11 – Video Course Spotlight 00:18:33 – The “S” in M Stands for Security 00:28:08 – Real Python Workshops 00:30:26 – Staff Engineer vs Engineering Manager 00:44:48 – python-docx: Create and modify Word documents with Python 00:47:28 – peek: like print, but easy 00:50:32 – Thanks and goodbye News: Python 3.14.0a7, 3.13.3, 3.12.10, 3.11.12, 3.10.17 and 3.9.22 are now available PEP 768: Safe External Debugger Interface for ython (Accepted) PEP 781: Make TYPE_CHECKING a Built-in Constant – This PEP proposes adding a new built-in variable, TYPE_CHECKING, which is True when the code is being analyzed by a static type checker, and False during normal runtime. PEP 750: Template Strings (Accepted) – This PEP introduces template strings for custom string processing. PEP 751: A file format to record Python dependencies for installation reproducibility (Accepted) EuroPython July 14th-20th Prague, Tickets Available Django 5.2 Released Django security releases issued: 5.1.8 and 5.0.14 Topics: Introducing DuckDB – In this showcase tutorial, you’ll be introduced to a library that allows you to use a database in your code. DuckDB provides an efficient relational database that s many features you may already be familiar with from more traditional relational database systems. Expression vs Statement in Python: What’s the Difference? – In this tutorial, you’ll explore the differences between an expression and a statement in Python. You’ll learn how expressions evaluate to values, while statements can cause side effects. You’ll also explore the gray areas between them, which will enhance your Python programming skills. The “S” in M Stands for Security - Elena Cross – Model Context Protocol is a new standard behind how Large Language Models integrate with tools and data. Unfortunately, M is not secure by default. Staff Engineer vs Engineering Manager - Alex Ewerlöf Notes – When do you need a Staff Engineers? What’s the difference between Staff Engineer and Engineering Manager? This article covers these questions and more. Projects: python-docx: Create and modify Word documents with Python peek: like print, but easy Additional Links: Intermediate Python Deep Dive Course – Real Python Episode #227: New PEPs: Template Strings & External Wheel Hosting DuckDB – An in-process SQL OLAP database management system Online analytical processing - Wikipedia Model Context Protocol has prompt injection security problems Model Context Protocol - Documentation modelcontextprotocol/python-sdk: The official Python SDK for Model Context Protocol servers and clients “Biggest commitment to a 3 second joke I’ve ever seen” — Bluesky Level up your Python skills with our expert-led courses: Creating a Python Dice Roll Application Python Assignment Expressions and Using the Walrus Operator Debugging in Python With pdb the podcast & our community of Pythonistas

Do you want to learn deeper concepts in Python? Would the ability of scheduled group classes help you get past the basics? This week, five Real Python Intermediate Deep Dive workshop discuss their experiences. We discuss the struggles of learning Python independently and the barriers to moving beyond the basics. We also explore the advantages of having a curated collection of both written tutorials and video courses. The cohort also talk about filling in the gaps in their knowledge, using their new skills at work, and building confidence in their Python journey. Check out realpython.com/workshops to the cohort of the Intermediate Python Deep Dive course. Course Spotlight: Efficient Iterations With Python Iterators and Iterables In this video course, you’ll learn what iterators and iterables are in Python. You’ll learn how they differ and when to use them in your code. You’ll also learn how to create your own iterators and iterables to make data processing more efficient. Topics: 00:00:00 – Introduction 00:02:04 – Matt’s background 00:03:17 – Chris’ background 00:05:55 – Jerry’s background 00:07:40 – Akhil’s background 00:09:25 – Rich’s background 00:10:35 – What skills didn’t translate from the previous language? 00:11:54 – Learning deeper concepts about OOP in Python 00:15:42 – Moving beyond scripts and ability to read code 00:19:41 – How ability helps with learning 00:23:41 – Challenges with self-paced learning 00:28:11 – Having a curated collection of written and video materials 00:33:28 – Video Course Spotlight 00:34:56 – What were surprising discoveries? 00:36:32 – Working on a project 00:37:27 – Using these new skills at work 00:45:01 – Refining existing skills 00:46:41 – Do you feel more confident to learn even further? 00:49:26 – What are other Python projects you work on? 00:55:17 – Thanks and goodbyes Show Links: Intermediate Python Deep Dive Course – Real Python Object-Oriented Programming (OOP) in Python – Real Python Flipped classroom - Wikipedia itertools — Functions creating iterators for efficient looping — Python 3.13.3 documentation Primer on Python Decorators – Real Python Pointers in Python: What’s the Point? Intern Objects – Real Python Data Classes in Python – Real Python Level up your Python skills with our expert-led courses: Efficient Iterations With Python Iterators and Iterables Python's map() Function: Transforming Iterables Python Decorators 101 the podcast & our community of Pythonistas

What are the current Python graphical interface libraries? Should you build everything in the terminal and create a text-based interface instead? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We cover a Real Python article that explores the Textual library. Textual is a Python toolkit and framework for creating attractive and functional text-based interface (TUI) applications that run in the ’s terminal. The tutorial covers organizing layouts of widgets, styling components, and handling events and actions within an application. We continue our exploration of interface options for your projects by discussing a recent article about Python GUI libraries. The piece compares the frameworks, showing a quick preview of how they look and sample code for a simple application. We share our thoughts and experiences with several of the libraries as we go through the collection. We also share several other articles and projects from the Python community, including a news roundup, handling binary data in Python, exploring the rules terminal programs follow, using Microsoft Edge’s online text-to-speech service from Python, and a project for reading and writing compressed JSON. Course Spotlight: Building a Code Image Generator With Python In this step-by-step video course, you’ll build a code image generator that creates nice-looking images of your code snippets to share on social media. Your code image generator will be powered by the Flask web framework and include exciting packages like Pygments and Playwright. Topics: 00:00:00 – Introduction 00:02:16 – PyCon US: Travel Grants & Refund Policy 00:02:57 – PyCon US 2025 travel guidance? 00:03:32 – Faster Branch Coverage Measurement 00:04:11 – Python Release Python 3.14.0a6 00:04:21 – Django 5.2 Release Candidate 1 Released 00:04:30 – PyOhio July 26-27, Call for Papers 00:04:59 – PEP 779: Criteria for ed Status for Free-Threaded Python 00:05:45 – Python Textual: Build Beautiful UIs in the Terminal 00:11:32 – Bytes Objects: Handling Binary Data in Python 00:16:41 – Video Course Spotlight 00:18:01 – Which Python GUI Library Should You Use in 2025? 00:32:08 – Real Python Workshops 00:34:23 – “Rules” That Terminal Programs Follow 00:40:29 – edge-tts: Use Microsoft Edge’s online text-to-speech service from Python 00:44:07 – compress_json: Read and Write Compressed JSON 00:45:34 – Thanks and goodbye News: PyCon US: Travel Grants & Refund Policy – PyCon US offers travel grants to visitors. This post explains how they’re decided. Also, with changing border requirements in the US, you may also be interested in the Refund Policy for International Attendees. PyCon US 2025 travel guidance? - PSF / Ask the staff! - Discussions on Python.org Faster Branch Coverage Measurement – After nearly two years, Ned thinks this is finally ready: coverage.py can use sys.monitoring to more efficiently measure branch coverage. Python Release Python 3.14.0a6 Django 5.2 Release Candidate 1 Released PyOhio July 26-27, Call for Papers PEP 779: Criteria for ed Status for Free-Threaded Python – PEP 703 (Making the Global Interpreter Lock Optional in ython), described three phases of development. This PEP outlines the criteria to move between phases. Show Links: Python Textual: Build Beautiful UIs in the Terminal – Textual is a Python library for building text-based interfaces (TUIs) that rich text, advanced layouts, and event-driven interactivity in the terminal. This tutorial showcases some of the ways you can design an appealing and engaging UI using Textual. Bytes Objects: Handling Binary Data in Python – In this tutorial, you’ll learn about Python’s bytes objects, which help you process low-level binary data. You’ll explore how to create and manipulate byte sequences in Python and how to convert between bytes and strings. Additionally, you’ll practice this knowledge by coding a few fun examples. Which Python GUI Library Should You Use in 2025? – This post compares the Python GUI libraries available in 2025, including PyQT, PySide, TKinter, and Kivy. “Rules” That Terminal Programs Follow – The conventions that most terminal programs follow mean that you can more easily know how to control them. Julia’s post talks about “rules” that terminal programs tend to follow, and so should yours. Projects: edge-tts: Use Microsoft Edge’s online text-to-speech service from Python WITHOUT needing Microsoft Edge or Windows or an API key compress_json: Read and Write Compressed JSON Additional Links: Intermediate Python Deep Dive Course – Real Python Episode #80: Make Your Python App Interactive With a Text Interface (TUI) Build a Book App With Python, Textual, and SQLite Binary, Bytes, and Bitwise Operators in Python – Video Course Nibble (magazine) - Wikipedia Python GUI Programming – Real Python Python GUI Programming With Tkinter – Tutorial Python and PyQt: Building a GUI Desktop Calculator – Tutorial Build Cross-Platform GUI Apps With Kivy – Tutorial How to Build a Python GUI Application With wxPython – Tutorial Episode #182: Building a Python JSON Parser & Discussing Ideas for PEPs Speech Synthesis Markup Language (SSML) overview - Speech service - Azure AI services | Microsoft Learn Level up your Python skills with our expert-led courses: Building a Python GUI Application With Tkinter Build a GUI Calculator With PyQt and Python Building a Code Image Generator With Python the podcast & our community of Pythonistas

What goes into updating one of the most popular books about working with Python? After a decade of changes in the Python landscape, what projects, libraries, and skills are relevant to an office worker? This week on the show, we speak with previous guest Al Sweigart about the third edition of “Automate the Boring Stuff With Python.” Al shares his thoughts on teaching Python and writing books over the past decade. In this third edition, he shares several new projects and updates to existing ones. We discuss Python tools for transcription, text-to-speech, notifications, and data storage. We talk about the importance of debugging and improvements to Python error messages. He also shares a collection of resources, including conference talks, small projects, and Python libraries. Course Spotlight: Exploring Scopes and Closures in Python In this Code Conversation video course, you’ll take a deep dive into how scopes and closures work in Python. To do this, you’ll use a debugger to walk through some sample code, and then you’ll take a peek under the hood to see how Python holds variables internally. Topics: 00:00:00 – Introduction 00:01:46 – The Recurse Center and scrollart.org 00:05:11 – Third Edition of Automate the Boring Stuff With Python 00:07:32 – The types of projects covered in the new edition 00:09:44 – What was the original page count? 00:11:00 – Learning Python and it being perceived as magic 00:12:00 – PyCon US 2025 - Make Python Talk and Listen 00:14:22 – Text-to-speech with pyttsx3 00:19:31 – Generating notifications and messages with ntfy.sh 00:22:09 – Exploring SQLite 00:28:26 – Teaching enough to start building 00:31:03 – The Recursive Book of Recursion 00:32:45 – Do you see a change in the audience of Python learners 00:35:36 – Expectations put upon a new Python learner 00:40:28 – What changes has 10 years inspired for the book? 00:43:40 – Teaching things in a new order and debugging 00:47:31 – Video Course Spotlight 00:48:56 – Including simple projects 00:54:12 – Book release timeframe and pre-orders 00:58:26 – In-line metadata for Python script sharing 00:59:33 – What are you excited about in the world of Python? 01:01:56 – What do you want to learn next? 01:04:34 – How can people follow your work online? 01:05:19 – Thanks and goodbye Show Links: Automate the Boring Stuff with Python, 3rd Edition - No Starch Press The Recurse Center scrollart.org 20 GOTO 10: How to Make Scrolling ASCII Art - PyTexas 2024 - YouTube Episode #26: 5 Years Podcasting Python With Michael Kennedy: Growth, GIL, Async, and More whisper: Robust Speech Recognition via Large-Scale Weak Supervision PyVideo.org - Al Sweigart pyttsx3: Offline Text To Speech Synthesis for Python pyttsx3 - PyPI tesseract: Tesseract Open Source OCR Engine Make Python Talk, Make Python Listen - PyCon US 2025 yt-dlp: A feature-rich command-line audio/video er ntfy.sh - Send push notifications to your phone via PUT/POST SQLite Home Page SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files – video course The Recursive Book of Recursion - No Starch Press Al Sweigart: The Amazing Mutable, Immutable Tuple - YouTube Python Developers Survey 2023 Results Inline script metadata - Python Packaging Guide PyCon US 2025 Rust Programming Language Al Sweigart (@[email protected]) - Fosstodon Al Sweigart (@alsweigart.bsky.social) — Bluesky Invent with Python Level up your Python skills with our expert-led courses: Debugging in Python With pdb Exploring Scopes and Closures in Python SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files the podcast & our community of Pythonistas

How can you simplify the management of your Python projects with one file? What are the advantages of using LazyFrames in Polars? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We share a recent Real Python tutorial by Ian Currie about managing projects with a pyproject.toml file. This file simplifies Python project configuration by unifying package setup, managing dependencies, and streamlining builds. Christopher continues his exploration of the Polars library by covering another Real Python tutorial about working with LazyFrames. He describes how LazyFrames don’t contain data but instead store a set of instructions known as a query plan. We also share several other articles and projects from the Python community, including a news roundup, building a to-do app with Python and Kivy, working with DuckDB directly instead of using a DataFrame library, a discussion on fiction and nonfiction books about computer science, a terminal visual effects engine, and a full-stack platform for interactive data apps. Course Spotlight: Everyday Project Packaging With pyproject.toml In this Code Conversation video course, you’ll learn how to package your everyday projects with pyproject.toml. Playing on the same team as the import system means you can call your project from anywhere, ensure consistent imports, and have one file that’ll work for many build systems. Topics: 00:00:00 – Introduction 00:02:00 – Happy Pi Day! 00:02:15 – Follow-up: Is BDD Dying? 00:03:32 – Django security releases issued: 5.1.7, 5.0.13 and 4.2.20 00:04:01 – Django 5.2 Beta 1 Released 00:04:11 – DjangoCon Africa Aug 2025 CFP 00:04:29 – Launching the PyCon US 2025 Schedule 00:04:48 – PyPy v7.3.19 Release 00:05:06 – Poetry 2.0.0 Released 00:05:34 – How to Manage Python Projects With pyproject.toml 00:12:10 – Build a To-Do App With Python and Kivy 00:16:22 – Mastering DuckDB When You’re Used to pandas or Polars 00:21:08 – Video Course Spotlight 00:22:42 – How to Work With Polars LazyFrames 00:27:41 – Fiction/Non-Fiction Books on the Topic of CS? 00:42:28 – preswald: Full-Stack Platform for Interactive Data Apps 00:45:52 – terminaltexteffects: Terminal Visual Effects Engine 00:47:59 – Thanks and goodbye Follow-up: Episode #239: Behavior-Driven vs Test-Driven Development & Using Regex in Python Is BDD Dying? - Automation Panda News: Django security releases issued: 5.1.7, 5.0.13 and 4.2.20 | Weblog | Django Django 5.2 Beta 1 Released DjangoCon Africa Aug 2025, Arusha, Tanzania, (Call for Proposals) Launching the PyCon US 2025 Schedule – This post summarizes the schedule for PyConUS, including a summary of the keynote speakers, and updates on conference swag. PyPy v7.3.19 Release Poetry 2.0.0 Released Show Links: How to Manage Python Projects With pyproject.toml – Learn how to manage Python projects with the pyproject.toml configuration file. In this tutorial, you’ll explore key use cases of the pyproject.toml file, including configuring your build, installing your package locally, managing dependencies, and publishing your package to PyPI. Build a To-Do App With Python and Kivy – “In this tutorial, you’ll go through a series of steps to build a basic To-Do app with Python, SQLite, and Kivy.” Mastering DuckDB When You’re Used to pandas or Polars – Why use DuckDB / SQL at all if you’re used to DataFrames? This article makes the case for some reasons why, and shows how to perform some operations which in DataFrames are basic but in SQL aren’t necessarily obvious. How to Work With Polars LazyFrames – In this tutorial, you’ll gain an understanding of the principles behind Polars LazyFrames. You’ll also learn why using LazyFrames is often the preferred option over more traditional DataFrames. Discussion: Fiction/Non-Fiction Books on the Topic of CS? Christopher Trudeau’s most recommended books (picked by super fans) ctrudeau - LibraryThing Project: preswald: Full-Stack Platform for Interactive Data Apps terminaltexteffects: Terminal Visual Effects Engine Additional Links: Pi Day - Celebrate Mathematics on March 14th What’s new in Python 3.14 — Python 3.14.0a5 documentation Mark Litwintschik - Tech Blog Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries Working With Python Polars - Video Course How to Deal With Missing Data in Polars – Tutorial Book Review: The Little Schemer - The Invent with Python Blog Books Mentioned by Mr. Trudeau: “The Cuckoo’s Egg” by Clifford Stoll “Mythical Man Month” by Frederick Brooks “Phoenix Project” by Gene Kim “Dreaming in Code” by Scott Rosenberg “Digital Fortress” by Dan Brown “Godel Escher, Bach” by Douglas Hofstadlter “A Philosophy of Software Design” by John Ousterhout’s “I Hate The Internet” by Jarret Kobek “Snow Crash” by Neal Stephenson “Automate the Boring Stuff with Python” by Al Sweigart “Django In Action” by Christopher Trudeau “Refactoring Databases” by Scott W Ambler and Pramod J Sadalage “The C Programming Language” by Dennis M. Ritchie and Brian W. Kernighan “Open Source Licensing” by Lawrence Rosen “The Quick Python Book” by Naomi R. Ceder “Learn to Code By Solving Problems: A Python Programming Primer” by Daniel Zingaro “Python Automation Cookbook” by Jaime Buelta Books Mentioned by Mr. Bailey: “The Little Schemer” by Daniel P. Friedman “Zen and the Art of Motorcycle Maintenance” by Robert M. Pirsig “Shop Class as Soulcraft: An Inquiry into the Value of Work” by Matthew B. Crawford “Django for Beginners, APIs, and Professionals” by William S. Vincent “Python Crash Course” by Eric Matthes “Automate the Boring Stuff With Python” by Al Sweigart “Fluent Python” by Luciano Ramalho “Practices of the Python Pro” by Dane Hillard “Daemon and Freedom™” by Daniel Suarez Level up your Python skills with our expert-led courses: Everyday Project Packaging With pyproject.toml Working With Python Polars Publishing Python Packages to PyPI the podcast & our community of Pythonistas

Should you always start testing your code with unit tests? When does it make sense to look at integration or end-to-end testing as a first step instead? This week on the show, we speak with previous guest Eric Matthes about where to begin testing your code. Eric is the author of the popular book Python Crash Course. Early in the development of the book, he decided to introduce testing and added a chapter on testing code with pytest. Over the past couple of years, Eric has continued to consider when and where to test a project’s code. He thinks there are hazards to always starting with unit tests. The type of project and its audience should determine what kind of testing to employ initially. We discuss using pytest to develop integration tests on multiple types of projects. We also explore fixtures and what goes into building a test suite. Eric also shares criteria for when and where it makes sense to add unit tests to a project. Course Spotlight: Using Python’s assert to Debug and Test Your Code In this course, you’ll learn how to use Python’s assert statement to document, debug, and test code in development. You’ll learn how assertions might be disabled in production code, so you shouldn’t use them to validate data. You’ll also learn about a few common pitfalls of assertions in Python. Topics: 00:00:00 – Introduction 00:01:47 – Submitting talks to conferences 00:04:10 – Don’t start with unit tests! 00:07:35 – How did you start with testing? 00:11:30 – Example of a project needing tests 00:14:54 – Defining types of tests 00:16:44 – Integration vs end-to-end tests 00:19:09 – When should you build tests? 00:22:13 – Trade offs of integration vs unit tests 00:24:05 – Why is there push back on this idea? 00:27:36 – Video Course Spotlight 00:29:09 – Using pytest 00:33:24 – Transcripts project example 00:37:03 – py-image-border project 00:40:29 – Criteria for when you should write unit tests 00:48:51 – How to practice writing tests 00:50:28 – Building an integration test and pytest fixtures 00:55:05 – What’s in the test folder? 00:56:31 – Idea for a PyCon tutorial on implementing tests 00:57:29 – Other pytest advice and parametrization 01:01:13 – Caveats to not starting with unit tests 01:02:30 – pytest documentation and other advice 01:05:23 – How to reach Eric online 01:06:47 – What are you excited about in the world of Python? 01:08:23 – What do you want to learn next? 01:09:48 – What conferences are you attending? 01:10:06 – Thanks and goodbye Show Links: Don’t start with unit tests - by Eric Matthes Sleep Better By Writing Python Tests with Eric Matthes - YouTube Episode #163: Python Crash Course & Learning Enough to Start Creating git-sim: Visually simulate Git operations in your own repos with a single terminal command Manim Community django-simple-deploy Learn the grand staff! py-image-border: Add a border to any image pytest Documentation - Get Started About fixtures - pytest documentation Parametrizing tests - pytest documentation uv: Unified Python packaging Prophet 5 Compact Poly Synth - Sequential PyCon US 2025 EuroPython 2025 - July 14th-20th 2025 - Prague, Czech Republic & Remote Python Crash Course, 3rd Edition - No Starch Press Mostly Python - Eric Matthes Level up your Python skills with our expert-led courses: Everyday Project Packaging With pyproject.toml Testing Your Code With pytest Using Python's assert to Debug and Test Your Code the podcast & our community of Pythonistas

How do you learn the commonly used when speaking about Python? How is the jargon similar to other programming languages? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a Python glossary recently created by Trey Hunner. Trey describes it as an unofficial glossary and Python jargon file. We dig into the and colloquial language often used when describing Python. We cover a blog post celebrating 31 years of Python by compiling Python 1.0. The piece walks through the hoops of finding the source code and standing up an old version of Debian. Once compiled, they open the REPL and find it surprisingly capable. We also share several other articles and projects from the Python community, including release news, a Python enhancement proposal roundup, managing Django’s queue, a course about NumPy techniques including practical examples, getting platform-specific directories, detecting which shell is in use, and a project for sorted container types. This episode is sponsored by Postman. 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 00:02:42 – Python Release 3.14.0a5 00:02:54 – PyPy v7.3.18 Released 00:03:32 – Beautifulsoup 4.13 Released 00:04:13 – PEP 759: External Wheel Hosting (Withdrawn) 00:04:54 – PEP 2026: Calendar Versioning for Python (Rejected) 00:06:48 – PEP 739: Static Description File for Build Details (Accepted) 00:07:51 – PEP 765: Disallow Return/Break/Continue That Exit a Finally Block (Accepted) 00:09:01 – Python Terminology: An Unofficial Glossary 00:19:32 – Sponsor: Postman 00:20:28 – NumPy Techniques and Practical Examples 00:24:12 – Let’s Compile Python 1.0 00:28:55 – Video Course Spotlight 00:30:14 – Managing Django’s Queue 00:36:41 – platformdirs: Get Platform-Specific Dirs 00:39:57 – shellingham: Tool to Detect Surrounding Shell 00:41:02 – python-sortedcontainers: Python Sorted Container Type 00:41:58 – Thanks and goodbye News: Python Release 3.14.0a5 PyPy v7.3.18 Released Beautifulsoup 4.13 Released PEP 759: External Wheel Hosting (Withdrawn) PEP 2026: Calendar Versioning for Python (Rejected) PEP 739: Static Description File for Build Details (Accepted) PEP 765: Disallow Return/Break/Continue That Exit a Finally Block (Accepted) Topics: Python Terminology: An Unofficial Glossary – “Definitions for colloquial Python terminology (effectively an unofficial version of the Python glossary).” 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. Let’s Compile Python 1.0 – As part of the celebration of 31 years of Python, Bite Code compiles the original Python 1.0 and plays around with it. Managing Django’s Queue – Carlton is one of the core developers of Django. This post talks about staying on top of the incoming pull-requests, bug fixes, and everything else in the development queue. Projects: platformdirs: Get Platform-Specific Dirs, e.g. “ Data Dir” shellingham: Tool to Detect Surrounding Shell python-sortedcontainers: Python Sorted Container Types Additional Links: Reference: Concise definitions for common Python – Real Python NumPy Practical Examples: Useful Techniques – Tutorial NumPy Practical Examples: Useful Techniques Quiz Python 1.0.0 is out! Podman OrbStack · Fast, light, simple Docker & Linux Level up your Python skills with our expert-led courses: Data Cleaning With pandas and NumPy Building Command Line Interfaces With argparse NumPy Techniques and Practical Examples the podcast & our community of Pythonistas

How do you make compelling visualizations that best convey the story of your data? What methods can you employ within popular Python tools to improve your plots and graphs? This week on the show, Matt Harrison returns to discuss his new book “Effective Visualization: Exploiting Matplotlib & Pandas.” As a data scientist and instructor, Matt has been teaching the concepts of managing tabular data and making visualizations for over 20 years. Matt shares his methodology for taking a basic plot and then telling a compelling story with it. We discuss why you should limit your plot types to a few that your audience is familiar with. We cover the resources built into pandas and Matplotlib and some of the libraries’ limitations. Matt talks about the professionally produced plots that inspired him and the process of recreating them. He also answers questions about finding data sources to practice these techniques with. This episode is sponsored by Postman. Course Spotlight: Using plt.scatter() to Visualize Data in Python In this course, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots. Topics: 00:00:00 – Introduction 00:02:57 – XGBoost book and interview 00:04:00 – Effective Visualization – Exploiting Matplotlib & pandas 00:04:27 – Why focus on pandas? 00:06:01 – Plotting inside of pandas 00:08:41 – How did you get involved in visualizations? 00:13:54 – Why write this book? 00:16:17 – Sponsor: Postman 00:17:09 – What are the plots you appreciate? 00:22:41 – Creating a methodology for plotting 00:24:24 – Color to spell out the story 00:27:50 – Limited and simple types of visualizations 00:31:34 – Explaining the story 00:37:19 – highlight-text library for matplotlib 00:39:02 – Video Course Spotlight 00:40:11 – Who is the audience? 00:43:19 – Why not include interactivity? 00:45:38 – Listing the references for the data 00:49:12 – Deciding on the examples and recipes 00:54:45 – Using existing visualizations as inspiration 00:55:41 – Matplotlib style sheets 00:57:54 – Finding sources of data to work with 01:04:17 – How to purchase the book 01:05:07 – What are you excited about in the world of Python? 01:06:33 – What do you want to learn next? 01:07:36 – How can people follow your work online? 01:08:04 – Thanks and goodbye Show Links: Effective Visualization – Exploiting Matplotlib & Pandas Matplotlib — Visualization with Python Episode #169: Improving Classification Models With XGBoost Episode #214: Build Captivating Display Tables in Python With Great Tables pandas documentation highlight-text · PyPI Style sheets — Matplotlib 3.10.0 documentation Kaggle: Your Machine Learning and Data Science Community nytimes/data-training: Files from the NYT data training program, available for public use. Astral: Next-gen Python tooling Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv Polars — DataFrames for the new era CircuitPython Effective Visualization: Exploiting Matplotlib & Pandas - Amazon Matt Harrison (@dunder-matt.bsky.social) — Bluesky Level up your Python skills with our expert-led courses: Plot With pandas: Python Data Visualization Basics Using plt.scatter() to Visualize Data in Python Exploring Astrophysics in Python With pandas and Matplotlib the podcast & our community of Pythonistas

What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. In this episode, we expand on our software testing discussion from two weeks ago by adding behavior-driven development concepts. Christopher describes how BDD correlates with test-driven development and how it fosters collaboration within a team. We discuss building acceptance tests written in plain language and a handy tool for creating them. We also share several other articles and projects from the Python community, including a news roundup, using regular expressions in Python, dealing with missing data in Polars, monkey patching in Django, first steps with Playwright, 3D printing giant things with a Python jigsaw generator, and a query language for JSON. This episode is sponsored by Postman. Course Spotlight: Regular Expressions and Building Regexes in Python In this course, you’ll learn how to perform more complex string pattern matching using regular expressions, or regexes, in Python. You’ll also explore more advanced regex tools and techniques that are available in Python. Topics: 00:00:00 – Introduction 00:02:21 – PyOhio 2025 July 26-27, 2025 Announced 00:02:38 – Python 3.13.2 and 3.12.9 now available! 00:02:52 – Django bugfix releases issued: 5.1.6, 5.0.12, and 4.2.19 00:03:04 – DjangoCon Europe 2025 - Real Python Podcast 00:05:24 – How to Deal With Missing Data in Polars 00:10:29 – Monkey Patching Django 00:15:50 – Sponsor: Postman 00:16:42 – My First Steps With Playwright 00:20:48 – How to Use Regular Expressions in Python 00:25:55 – Video Course Spotlight 00:27:25 – TDD vs. BDD: What’s the Difference? 00:50:13 – 3D Printing Giant Things With a Python Jigsaw Generator 00:53:58 – jmespath.py: Query Language for JSON 00:55:58 – Thanks and goodbye News: PyOhio 2025 July 26-27, 2025 Announced Python 3.13.2 and 3.12.9 now available! Django bugfix releases issued: 5.1.6, 5.0.12, and 4.2.19 DjangoCon Europe 2025: Schedule Topics: How to Deal With Missing Data in Polars – In this tutorial, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them. Monkey Patching Django – The nanodjango project is a modification to the Django framework that lets you get started with a single file instead of the usual cookie-cutter directory structure. This is a detailed post explaining how nanodjango monkey patches Django to achieve this result. Fake Django Objects With Factory Boy – The My First Steps With Playwright – Playwright is a browser-based automation tool that can be used for web scraping or testing. This intro article shows you how to use the Python interface to access a page including using cookies. How to Use Regular Expressions in Python – This post explores the basics of regular expressions in Python, as well as more advanced techniques. It includes real-world use cases and performance optimization strategies. Discussion: TDD vs. BDD: What’s the Difference? – Discover the key differences between TDD vs BDD, their workflows, tools, and best practices for developers. Cucumber Projects: 3D Printing Giant Things With a Python Jigsaw Generator – This is a long, detailed article on 3D printing objects too large for the printer bed. The author has created dovetail ts to assemble pieces together. He wrote a Python program to automatically split up the larger model files into the jigsaw pieces needed to build a final result. jmespath.py: Query Language for JSON Additional Links: Polars — DataFrames for the new era nanodjango: Full Django in a single file - views, models, API ,with async . Automatically convert it to a full project. factory_boy library is a tool for managing fixtures for your tests. This article shows you how to use it with Django. trimesh 4.6.2 documentation Email::RFC822::Address - Regex Recipe Level up your Python skills with our expert-led courses: Regular Expressions and Building Regexes in Python Test-Driven Development With pytest How to Set Up a Django Project the podcast & our community of Pythonistas

Are you looking for fast tools to lint your code and manage your projects? How is the Rust programming language being used to speed up Python tools? This week on the show, we speak with Charlie Marsh about his company, Astral, and their tools, uv and Ruff. Charlie started working on Ruff as a proof of concept, stating that Python tooling could be much faster. He had seen similar gains in JavaScript tools written in Rust. The project started as a speedy linter with a small ruleset. It’s grown to include code formatting and over 800 built-in linting rules. Last year, the team at Astral started working on a Python package and project manager written in Rust. As a single tool, uv can replace pip, pip-tools, pipx, poetry, pyenv, and more. We discuss how uv can install and manage versions of Python and run scripts without thinking about virtual environments or dependencies. Charlie talks about growing the team at Astral over the past couple of years. We also discuss the funding model Astral has adopted and sustaining open-source software. This episode is sponsored by Postman. Course Spotlight: Python Basics: Installing Packages With pip Python’s standard library includes a whole buffet of useful packages, but sometimes you need to reach for a third-party library. That’s where pip comes in handy. In this video course, you’ll learn how to pip install packages. Topics: 00:00:00 – Introduction 00:03:37 – How did you get involved in open source? 00:07:01 – Fostering a community around a project 00:11:32 – Python tooling could be much, much faster 00:15:45 – Changing the ergonomics of tooling 00:19:59 – What is ruff and what jobs can it do? 00:22:23 – How do you configure ruff? 00:26:02 – Where do the linting rules come from? 00:29:29 – Can you build your own rules? 00:31:28 – Performance difference for ruff 00:36:25 – Installing ruff 00:37:34 – The rustification of Python 00:40:52 – The initial features and release of uv 00:45:07 – Installing Python 00:47:50 – Taking over the python-build-standalone project 00:53:02 – Installation methods and suggestions 00:55:37 – Video Course Spotlight 00:57:07 – The project API 01:01:57 – Inline script metadata and PEP 723 01:06:49 – Installing tools with uvx 01:09:37 – Project management 01:11:20 – Astral as company and VC funding 01:19:23 – New static type checker 01:26:15 – What are you excited about in the world of Python? 01:27:12 – What do you want to learn next? 01:28:52 – How can people follow your work online? 01:29:34 – Thanks and goodbye Show Links: Astral: Next-gen Python tooling Python tooling could be much, much faster Ruff, an extremely fast Python linter - Astral PEP 8 – Style Guide for Python Code FastHTML - Modern web applications in pure Python uv: An extremely fast Python package and project manager, written in Rust. Using Python’s pip to Manage Your Projects’ Dependencies – Tutorial Install and Execute Python Applications Using pipx – Tutorial Python Standalone Builds — python-build-standalone documentation Running scripts - uv Inline script metadata - Python Packaging Guide marimo - a next-generation Python notebook Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python “We’re building a new static type checker for Python, from scratch, in Rust.” Charlie Marsh (@charliermarsh) - X Charlie Marsh (@crmarsh.com) — Bluesky Level up your Python skills with our expert-led courses: Python Basics Exercises: Installing Packages With pip Python Basics: Installing Packages With pip Writing Beautiful Pythonic Code With PEP 8 the podcast & our community of Pythonistas

What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems. We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing. This episode is sponsored by Postman. Course Spotlight: Testing Your Code With pytest In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain. Topics: 00:00:00 – Introduction 00:02:28 – Python news and releases 00:04:02 – From Default Line Charts to Journal-Quality Infographics 00:07:25 – PyViz: Python Tools for Data Visualization 00:09:25 – Why Is hash(-1) == hash(-2) in Python? 00:12:40 – Sponsor: Postman 00:13:32 – Data Cleaning in Data Science 00:19:29 – 10 Ways to Work With Large Files in Python 00:23:40 – Unit Testing vs. Integration Testing 00:29:17 – Does university curriculum cover this? 00:31:22 – Building tests into smaller projects 00:36:04 – Video Course Spotlight 00:37:30 – How does the approach differ with clients or larger-scale projects? 00:40:45 – How do tests act as documentation? 00:42:02 – Difficulties in building integration tests 00:45:24 – How do you limit the results of tests? 00:47:52 – klp: Lightweight CLI Viewer for Log Files 00:50:54 – freezegun: Mocks the datetime Module for Testing 00:53:11 – Thanks and goodbye News: Python 3.14.0 Alpha 4 Released Django 5.2 Alpha 1 Released Django Security Releases Issued: 5.1.5, 5.0.11, and 4.2.18 SciPy 1.15.0 Released Pygments 2.19 Released PyConf Hyderabad Feb 22-23 Topics: From Default Line Charts to Journal-Quality Infographics – “Everyone who has used Matplotlib knows how ugly the default charts look like.” In this series of posts, Vladimir shares some tricks to make your visualizations stand out and reflect your individual style. PyViz: Python Tools for Data Visualization – This site contains an overview of all the different visualization libraries in the Python ecosystem. If you’re trying to pick a tool, this is a great place to better understand the pros and cons of each. Why Is hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in ython. This post examines how and discovers why this is the case. Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.” 10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge. Discussion: Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python. Project: klp: Lightweight CLI Viewer for Log Files freezegun: Mocks the datetime Module for Testing Additional Links: Matplotlib style sheets - Python Charts The Magic of Matplotlib Stylesheets Where To Get Data for Your Data Science Projects - The PyCharm Blog Data Exploration With pandas - The PyCharm Blog Python mmap: Improved File I/O With Memory Mapping - Tutorial Python mmap: Doing File I/O With Memory Mapping – Video Course pandera documentation Level up your Python skills with our expert-led courses: Python mmap: Doing File I/O With Memory Mapping Testing Your Code With pytest Python Plotting With Matplotlib the podcast & our community of Pythonistas

What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools. Simon has been researching LLMs over the past two and a half years and documenting the results on his blog. He shares which models work best for writing Python versus JavaScript and compares coding tools and environments. We discuss prompt engineering techniques and the first steps to take. Simon shares his enthusiasm for the usefulness of LLMs but cautions about the potential pitfalls. Simon also shares how he got involved in open-source development and Django. He’s a proponent of starting a blog and shares how it opened doors for his career. This episode is sponsored by Postman. Course Spotlight: Advanced Python import Techniques The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code. Topics: 00:00:00 – Introduction 00:02:38 – How did you get involved in open source? 00:04:04 – Writing an XML-RPC library 00:04:40 – Working on Django in Lawrence, Kansas 00:05:31 – Started building open-source collection 00:06:52 – shot-scraper: taking automated screenshots of websites 00:08:09 – First experiences with LLMs 00:10:08 – 22 years of simonwillison.net 00:18:22 – Navigating the hype and criticism of LLMs 00:22:14 – Where to start with Python code and LLMs? 00:26:22 – Sponsor: Postman 00:27:13 – ChatGPT Canvas vs Code Interpreter 00:28:23 – Asking nicely, tricking the system, and tipping? 00:30:35 – More Code Interpreter and building a C extension 00:32:05 – More details on Canvas 00:36:55 – What is a workflow for developing using LLMs? 00:39:43 – Creating pieces of code vs a system 00:42:00 – Workout program for prompting and pitfalls 00:53:54 – Video Course Spotlight 00:55:14 – Why an SVG of a pelican riding a bicycle? 00:57:48 – Repeating a query and refining 01:03:00 – Working in an IDE or text editor 01:05:45 – David Crawshaw on writing code with LLMs 01:08:33 – Running an LLM locally to write code 01:14:02 – Staying out of the AGI conversation 01:16:07 – What are you excited about in the world of Python? 01:18:34 – What do you want to learn next? 01:19:53 – How can people follow your work online? 01:20:51 – Thanks and goodbye Show Links: Simon Willison’s Weblog shot-scraper Matt’s Script Archive, Inc. - Free Perl CGI Scripts XR - my XML-RPC library, now in WordPress - GitHub Adrian Holovaty s for someone to him working in Lawrence (May 2003) - Holovaty.com Datasette: An open source multi-tool for exploring and publishing data My SQLite tag page - Simon Willison Chatbot Arena: Free AI Chat to Compare & Test Best AI Chatbots DeepSeek v3 notes on Christmas day DeepSeek_V3 - PDF Simon Willison on code-interpreter Gemini - Google DeepMind Claude ChatGPT Canvas can make API requests now, but it’s complicated Welcome to Click — Click Documentation My first experience with Llama in March 2023 I can now run a GPT-4 class model on my laptop Using LLMs and Cursor to become a finisher GitHub Copilot - Your AI pair programmer In Finland, classes in recognizing fake news, disinformation - Sunday Morning CBS 404Media Podcast: Why We Cover AI the Way We Do Jason Koebler from 404Media - tags on simonwillison.net Building Python tools with a one-shot prompt using uv run and Claude Projects How I program with LLMs - crawshaw - 2025-01-06 pelican-riding-a-bicycle - tags on simonwillison.net Things we learned about LLMs in 2024 Pyodide Simon Willison on pyodide astral-sh/uv: An extremely fast Python package and project manager Simon Willison on uv Simon Willison’s Newsletter - Substack Semi-automating a Substack newsletter with an Observable notebook Simon Willison (@simonwillison.net) — Bluesky Simon Willison (@[email protected]) - Mastodon Simon Willison (@simonw) - X Level up your Python skills with our expert-led courses: Building HTTP APIs With Django REST Framework Advanced Python import Techniques Absolute vs Relative Imports in Python the podcast & our community of Pythonistas

What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent article about effective Python developer tooling. Instead of digging into a list of current libraries, we talk about the principles you must consider before making decisions for your team. We cover common pitfalls teams get mired in and how to avoid them. We also share several other articles and projects from the Python community, including a news roundup, a huge collection of the top Python libraries of 2024, programming sockets in Python, merging dictionaries, a Django quiz, mistakes to avoid in production, building a Portal sentry turret, a powerful TUI expense tracker, and a pure-Python async rendering engine. Course Spotlight: Managing Dependencies With Python Poetry Learn how Python Poetry can help you start new projects, maintain existing ones, and master dependency management. Topics: 00:00:00 – Introduction 00:01:53 – DjangoCon US 2025 (Chicago, Sept 8-12) Announced 00:02:38 – Textualize 1.0 Released 00:03:15 – Top Python Libraries of 2024 00:07:07 – Programming Sockets in Python 00:11:56 – Merging Dictionaries in Python 00:17:03 – Django Quiz 2024 00:17:55 – Confessions of a Django Dev: Mistakes To Avoid in Production 00:18:40 – Sentry Turret Straight Out of the ‘Portal’ Franchise 00:20:00 – Video Course Spotlight 00:21:26 – Effective Python Developer Tooling in December 2024 00:41:13 – Bagels: Powerful TUI Expense Tracker 00:43:42 – htmy: Async, Pure-Python Rendering Engine 00:45:41 – Thanks and goodbye News: DjangoCon US 2025 (Chicago, Sept 8-12) Announced Textualize 1.0 Released Show Links: Top Python Libraries of 2024 – For the past ten years, Tyrolabs has put together a list of their favorite Python libraries of the year. This list includes ten general purpose libraries and ten more specific to AI/ML and Data. Programming Sockets in Python – In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications. Merging Dictionaries in Python – There are multiple ways of merging two or more dictionaries in Python. This post teaches you how to do it and how to deal with corner cases like duplicate keys. Django Quiz 2024 – Adam runs a quiz on Django at his Django London meetup. He’s shared it so you can try it yourself. Test how much you know about your favorite web framework. Confessions of a Django Dev: Mistakes To Avoid in Production – This post covers some of the common mistakes you might make when taking a Django project into production. Sentry Turret Straight Out of the ‘Portal’ Franchise – “Reckless_commenter has created a Raspberry Pi-powered sentry turret that looks and sounds just like the creepy machines found in the ‘Portal’ franchise.” Logic and sound effects managed through the PyGame library. Discussion: Effective Python Developer Tooling in December 2024 – This post talks about how tooling doesn’t solve all your problems when you code, especially with a team. It outlines some principles to implement, and bad practices to avoid when writing Python. Mistakes engineers make in large established codebases - Sean Goedecke Projects: Bagels: Powerful TUI Expense Tracker htmy: Async, Pure-Python Rendering Engine Additional Links: 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 the podcast & 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 Stephen Gruppetta and Martin Breuss us to discuss enhancements to the site and new ways to learn Python. Stephen has recently ed 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 ive community. Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python. Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months. This episode is sponsored by Sentry. Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one. Topics: 00:00:00 – Introduction 00:02:29 – What Stephen has been up to 00:03:31 – What’s new for Martin 00:04:07 – Bringing on new team 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 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 the podcast & our community of Pythonistas