Narwhals: Expanding DataFrame Compatibility Between Libraries

The Real Python Podcast

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Python

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Exile

1
Episode 10: From Refugee to Radical 44:09

منذ 2 years44:09

التشغيل لاحقا

قوائم

إعجاب

احب

44:09

When a young Eva Kollisch arrives as a refugee in New York in 1940, she finds a community among socialists who share her values and idealism. She soon discovers ‘the cause’ isn’t as idyllic as it seems. Little does she know this is the beginning of a lifelong commitment to activism and her determination to create radical change in ways that include belonging, love and one's full self. In addition to Eva Kollisch’s memoirs Girl in Movement (2000) and The Ground Under My Feet (2014), LBI’s collections include an oral history interview with Eva conducted in 2014 and the papers of Eva’s mother, poet Margarete Kolllisch, which document Eva’s childhood experience on the Kindertransport. Learn more at www.lbi.org/kollisch . Exile is a production of the Leo Baeck Institute , New York | Berlin and Antica Productions . It’s narrated by Mandy Patinkin. Executive Producers include Katrina Onstad, Stuart Coxe, and Bernie Blum. Senior Producer is Debbie Pacheco. Associate Producers are Hailey Choi and Emily Morantz. Research and translation by Isabella Kempf. Sound design and audio mix by Philip Wilson, with help from Cameron McIver. Theme music by Oliver Wickham. Voice acting by Natalia Bushnik. Special thanks to the Kollisch family for the use of Eva’s two memoirs, “Girl in Movement” and “The Ground Under My Feet”, the Sophia Smith Collection at Smith College and their “Voices of Feminism Oral History Project”, and Soundtrack New York.…

منذ عام واحد 1:00:33

MP3•منزل الحلقة

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

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

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

This episode is sponsored by CodeRabbit.

Course Spotlight: Differences Between Python’s Mutable and Immutable Types

In this video course, you’ll learn how Python’s mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code.

Topics:

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

Show Links:

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

Support the podcast & join our community of Pythonistas

242 حلقات

#Real Python #Data Science #Python2 #Python3 #Learn Python #Django #Programming #Development #Dev #Open Source #Software #Python #Tech #Prog Languages #Code #Test

The Real Python Podcast

Narwhals: Expanding DataFrame Compatibility Between Libraries

The Real Python Podcast

646 subscribers

published منذ عام واحد

MP3•منزل الحلقة

This episode is sponsored by CodeRabbit.

Course Spotlight: Differences Between Python’s Mutable and Immutable Types

In this video course, you’ll learn how Python’s mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code.

Topics:

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

Show Links:

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

Support the podcast & join our community of Pythonistas

242 حلقات

#Real Python #Data Science #Python2 #Python3 #Learn Python #Django #Programming #Development #Dev #Open Source #Software #Python #Tech #Prog Languages #Code #Test

كل الحلقات

1
Deciphering Python Jargon & Compiling Python 1.0 43:03

منذ 6 أيام43:03

43:03

How do you learn the terms 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 terms 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. “User Data Dir” shellingham: Tool to Detect Surrounding Shell python-sortedcontainers: Python Sorted Container Types Additional Links: Reference: Concise definitions for common Python terms – 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 Support the podcast & join our community of Pythonistas…

1
Telling Effective Stories With Your Python Visualizations 1:09:08

منذ 13 يومًا1:09:08

1:09:08

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 Support the podcast & join our community of Pythonistas…

1
Behavior-Driven vs Test-Driven Development & Using Regex in Python 57:03

منذ 20 يومًا57:03

57:03

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 joints to assemble pieces together. He wrote a Python program to automatically split up the larger model files into the jigsaw pieces needed to build a final result. jmespath.py: Query Language for JSON Additional Links: Polars — DataFrames for the new era nanodjango: Full Django in a single file - views, models, API ,with async support. Automatically convert it to a full project. factory_boy library is a tool for managing fixtures for your tests. This article shows you how to use it with Django. trimesh 4.6.2 documentation Email::RFC822::Address - Regex Recipe 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 Support the podcast & join our community of Pythonistas…

1
Charlie Marsh: Accelerating Python Tooling With Ruff and uv 1:30:37

منذ 27 يومًا1:30:37

1:30:37

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 User 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 Support the podcast & join our community of Pythonistas…

1
Testing Your Python Code Base: Unit vs. Integration 54:14

منذ 5 weeks54:14

54:14

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 CPython. This post examines how and discovers why this is the case. Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.” 10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge. Discussion: Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python. Project: klp: Lightweight CLI Viewer for Log Files freezegun: Mocks the datetime Module for Testing Additional Links: 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 Support the podcast & join our community of Pythonistas…

1
Simon Willison: Using LLMs for Python Development 1:22:04

منذ 6 weeks1:22:04

1:22:04

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 advertises for someone to join 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 (@simon@simonwillison.net) - 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 Support the podcast & join our community of Pythonistas…

1
Principles for Considering Your Python Tooling 46:47

منذ 7 weeks46:47

46:47

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 Support the podcast & join our community of Pythonistas…

1
Building New Structures for Learning Python 52:21

منذ 8 weeks52:21

52:21

What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn Python. Stephen has recently joined the team, bringing years of online training expertise. He discusses our new offering of cohort-based courses , which combine live expert instruction, hands-on exercises, and a supportive community. Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python. Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months. This episode is sponsored by Sentry. Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one. Topics: 00:00:00 – Introduction 00:02:29 – What Stephen has been up to 00:03:31 – What’s new for Martin 00:04:07 – Bringing on new team members 00:06:09 – Cohort-based courses 00:19:25 – Sponsor: Sentry 00:20:27 – Restructured and new learning paths 00:30:50 – Video Course Spotlight 00:32:19 – New Real Python Books 00:38:57 – A destination for learning 00:40:46 – Quizzes for tutorials and courses 00:44:58 – Video courses and updating content 00:47:52 – Code Mentor 00:49:45 – Code challenges 00:51:06 – Thanks and goodbye Show Links: Cohort Course - Intermediate Python Deep Dive Python Learning Paths Python Books by Real Python Python Quizzes Join the Real Python Community Chat Code Mentor: Intelligent Learning Tools Office Hours – Real Python Debugging Python with VS Code and Sentry - Product Blog - Sentry About Martin Breuss – Real Python About Stephen Gruppetta – Real Python Level up your Python skills with our expert-led courses: Handling or Preventing Errors in Python: LBYL vs EAFP Using raise for Effective Exceptions Python Basics Exercises: Scopes Support the podcast & join our community of Pythonistas…

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PyCoder's Weekly 2024 Top Articles & Missing Gems 41:03

منذ 9 weeks41:03

41:03

PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2024. Christopher Trudeau is back on the show this week to help wrap it all up by sharing some highlights and uncovering a few missing gems from the pile. We share the top links that PyCoder’s readers explored this year and uncover trends across all the articles and stories. We also highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year. We hope you enjoy this review! We look forward to bringing you another year filled with great Python news, articles, topics, and projects. Course Spotlight: Programming Sockets in Python In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications. Topics: 00:00:00 – Introduction 00:01:47 – New releases and updates 00:03:07 – PyCon US 2025 Registration Open 00:03:18 – PyCon Austria 2025 Call for Papers 00:03:36 – PSF Year End Fundraiser - Membership Drive 00:04:31 – Mr. Trudeau on Flying High with Flutter 00:05:29 – We’re on Bluesky - follow us! 00:07:44 – Build Captivating Display Tables in Python With Great Tables 00:08:45 – Overview of the Module itertools 00:09:23 – Customize VS Code Settings 00:10:34 – Modern Good Practices for Python Development 00:11:55 – Asyncio Event Loop in Separate Thread 00:12:38 – Python Protocols: Leveraging Structural Subtyping 00:13:06 – Thoughts on the top links 00:22:29 – Video Course Spotlight 00:23:40 – Why I’m Switching From pandas to Polars 00:29:29 – Lessons Learned Reinventing the Python Notebook 00:32:47 – What’s a Python Hashable Object? 00:36:10 – uv: Python Packaging in Rust 00:38:26 – CI/CD for Python With GitHub Actions 00:40:07 – Thanks and goodbye News: NumPy Release 2.2.0 Django Security Releases Issued: 5.1.4, 5.0.10, and 4.2.17 Python 3.13.1, 3.12.8, 3.11.11, 3.10.16, and 3.9.21 Released Python Insider: Python 3.14.0 alpha 3 is out PyCon US 2025 (Pittsburgh, PA) Registration Open PyCon Austria 2025 (Eisenstadt) Call for Papers PSF Year End Fundraiser - Membership Drive Top PyCoders Links 2024: Build Captivating Display Tables in Python With Great Tables – Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library. Overview of the Module itertools – This article proposes the top three iterators that are most useful from the module itertools , classifies all of the 19 iterators into five categories, and then provides brief usage examples for all the iterators in the module itertools . Customize VS Code Settings – In this course, Philipp helps you customize your Visual Studio Code settings to switch from a basic cluttered look to a clean presentable look. This is not just pleasant on the eyes, but also gives you a nice user interface if you want to share on a Zoom call or screen recording. Modern Good Practices for Python Development – This is a very detailed list of best practices for developing in Python. It includes tools, language features, application design, which libraries to use and more. Asyncio Event Loop in Separate Thread – Typically, the asyncio event loop runs in the main thread, but as that is the one used by the interpreter, sometimes you want the event loop to run in a separate thread. This article talks about why and how to do just that. Python Protocols: Leveraging Structural Subtyping – In this tutorial, you’ll learn about Python’s protocols and how they can help you get the most out of using Python’s type hint system and static type checkers. Featured Links: Why I’m Switching From pandas to Polars – Ari is switching from pandas to Polars and surprisingly (even to himself) it isn’t because of the better performance. Read on for the reasons why. Lessons Learned Reinventing the Python Notebook – Marimo is an open source alternative to Jupyter notebooks. This article is by one of marimo’s creators, talking about the design decisions made when creating it. What’s a Python Hashable Object? – You can ignore reading about hashable objects for quite a bit. But eventually, it’s worth having an idea of what they are. This post follows Winston on his first day at work to understand hashable objects uv : Python Packaging in Rust – uv is an extremely fast Python package installer and resolver, designed as a drop-in alternative to pip and pip-tools. This post introduces you to uv and shows some of its performance numbers. Associated HN discussion . CI/CD for Python With GitHub Actions – With most software following agile methodologies, it’s essential to have robust DevOps systems in place to manage, maintain, and automate common tasks with a continually changing codebase. By using GitHub Actions, you can automate your workflows efficiently, especially for Python projects. Additional Links: Flying High with Flutter The State of Python 2024 – This is a guest post on the PyCharm blog by Talk Python host Michael Kennedy who talks about the current state of Python in 2024. Topics include language usage, web frameworks, uv , and more. Django 2024 Year in Review – Carlton is a core contributor to Django and this post talks about what happened in 2024 with your favorite web framework. Episode #193: Wes McKinney on Improving the Data Stack & Composable Systems Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python Episode #203: Embarking on a Relaxed and Friendly Python Coding Journey Ruff: A Modern Python Linter for Error-Free and Maintainable Code Rodrigo 🐍🚀: Python folks, here’s an update on all the Python starter packs — Bluesky Christopher Bailey (@digiglean.bsky.social) — Bluesky Christopher Trudeau (@cltrudeau.bsky.social) — Bluesky Stephen Gruppetta (@stephengruppetta.com) — Bluesky Level up your Python skills with our expert-led courses: Building HTTP APIs With Django REST Framework HTML and CSS Foundations for Python Developers Programming Sockets in Python Support the podcast & join our community of Pythonistas…

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Exploring Modern Sentiment Analysis Approaches in Python 1:13:09

منذ 11 weeks1:13:09

1:13:09

What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python. Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career. In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own. This week’s episode is brought to you by Sentry. Course Spotlight: Learn Text Classification With Python and Keras In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization. Topics: 00:00:00 – Introduction 00:02:31 – Conference talks in 2024 00:04:23 – Background on sentiment analysis and studying feelings 00:07:09 – What led you to study emotions? 00:08:57 – Dimensional emotion classification 00:10:42 – Different types of sentiment analysis 00:14:28 – Lexicon-based approaches 00:17:50 – VADER - Valence Aware Dictionary and sEntiment Reasoner 00:19:41 – TextBlob and subjectivity scoring 00:21:48 – Sponsor: Sentry 00:22:52 – Measuring sentiment of New Year’s resolutions 00:27:28 – Lexicon-based approaches links for experimenting 00:28:35 – Multiple language support in lexicon-based packages 00:35:23 – Machine learning techniques 00:39:20 – Tools for this approach 00:42:54 – Video Course Spotlight 00:44:15 – Advantages to the machine learning models approach 00:45:55 – Large language model approach 00:48:44 – Encoder vs decoder models 00:52:09 – Comparing the concept of fine-tuning 00:56:49 – Is this a recent development? 00:58:08 – Ways to practice with these techniques 01:00:10 – Do you find this to be a promising approach? 01:07:45 – Resources to practice with all the techniques 01:11:06 – Upcoming conference talks 01:11:56 – Thanks and goodbye Show Links: Introduction to Sentiment Analysis in Python - The PyCharm Blog How to Do Sentiment Analysis With Large Language Models - The PyCharm Blog Talks - Jodie Burchell: Lies, damned lies and large language models - YouTube Mirror, mirror: LLMs and the illusion of humanity - Jodie Burchell - YouTube Separating fact from fiction in a world of AI fairytales - Jodie Burchell - NDC London 2024 - YouTube Hurt Feelings (Rap Version) - Flight Of The Conchords (Lyrics) - YouTube Universal Emotions - What are Emotions? - Paul Ekman Group VADER - nltk.sentiment.vader module clips/pattern: Web mining module for Python, with tools for scraping, natural language processing, machine learning TextBlob: Simplified Text Processing — TextBlob documentation Power vs. Force: The Hidden Determinants of Human Behavior by David R. Hawkins - Goodreads Episode #36: Sentiment Analysis, Fourier Transforms, and More Python Data Science – The Real Python Podcast Use Sentiment Analysis With Python to Classify Movie Reviews – Real Python Sentiment Analysis: First Steps With Python’s NLTK Library – Real Python Sentiment Analysis in DataSpell with @JetBrainsTV - YouTube Episode #119: Natural Language Processing and How ML Models Understand Text – The Real Python Podcast spaCy - Industrial-strength Natural Language Processing in Python amazon_polarity - Datasets at Hugging Face Introduction to Sentiment Analysis in Python - The PyCharm Blog Kaggle: Your Machine Learning and Data Science Community ZS BIT AI Community Day - 10 December 2024 Jodie Burchell - The JetBrains Blog Jodie Burchell’s Blog - Standard error Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) - Twitter Jodie Burchell (@t-redactyl.bsky.social) — Bluesky Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl@fosstodon.org) - Fosstodon JetBrains: Essential tools for software developers and teams Level up your Python skills with our expert-led courses: Data Cleaning With pandas and NumPy Learn Text Classification With Python and Keras Exploring Astrophysics in Python With pandas and Matplotlib Support the podcast & join our community of Pythonistas…

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Good Python Programming Practices When New to the Language 51:26

منذ 13 weeks51:26

51:26

What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss an older forum post from a new Python user who came from Perl. We suggest checking out PEP 8, or as it’s commonly known, “The Style Guide for Python Code.” We provide advice about installing Python, avoiding common pitfalls, learning how scope is managed, and taking advantage of a collection of Real Python resources. We share several other articles and projects from the Python community, including a new Python release, practical NumPy examples and exercises, considering targets of for loops, exploring Python dependency management, checking package compatibility with free-threading and subinterpreters, an experimental filesystem navigator in Textual, and a background workers reference implementation in Django. This episode is sponsored by AssemblyAI. Course Spotlight: Writing Beautiful Pythonic Code With PEP 8 Learn how to write high-quality, readable code by using the Python style guidelines laid out in PEP 8. Following these guidelines helps you make a great impression when sharing your work with potential employers and collaborators. This course outlines the key guidelines laid out in PEP 8. It’s aimed at beginner to intermediate programmers. Topics: 00:00:00 – Introduction 00:02:17 – Python 3.14.0 Alpha 2 Released 00:02:35 – Take the 2024 Django Developers Survey 00:03:17 – NumPy Practical Examples: Useful Techniques 00:07:09 – Loop Targets 00:09:19 – Python Dependency Management Is a Dumpster Fire 00:23:15 – Sponsor: AssemblyAI 00:24:00 – Package Compatibility With Free-Threading and Subinterpreters 00:27:02 – Suggestions for good programming practices? 00:37:59 – Video Course Spotlight 00:39:24 – terminal-tree: Experimental Filesystem Navigator in Textual 00:43:56 – django-tasks: Background Workers Reference Implementation 00:49:44 – Thanks and goodbye News: Python 3.14.0 Alpha 2 Released Take the 2024 Django Developers Survey Topics: NumPy Practical Examples: Useful Techniques – In this tutorial, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function. Loop Targets – Loop assignment allows you to assign to a dict item in a for loop. This post covers what that means and that it is no more costly than regular assignment. Python Dependency Management Is a Dumpster Fire – Managing dependencies in Python can be a bit of a challenge. This deep dive article shows you all the problems and how the problems are mitigated if not solved. Package Compatibility With Free-Threading and Subinterpreters – This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features. Discussion: Suggestions for good programming practices? Python Best Practices – Real Python PEP 8 – Style Guide for Python Code Projects: terminal-tree: Experimental Filesystem Navigator in Textual django-tasks: Background Workers Reference Implementation Additional Links: Episode #146: Using NumPy and Linear Algebra for Faster Python Code – The Real Python Podcast How to Write Beautiful Python Code With PEP 8 – Real Python Writing Idiomatic Python – Real Python Namespaces and Scope in Python – Real Python How to Install Python on Your System: A Guide – Real Python Python Virtual Environments: A Primer – Real Python Sourcery - Instant Code Review for Faster Velocity Episode #183: Exploring Code Reviews in Python and Automating the Process Textual uv - An extremely fast Python package and project manager, written in Rust. DEP 0014: Background workers - GitHub PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Navigating Namespaces and Scope in Python Writing Idiomatic Python Writing Beautiful Pythonic Code With PEP 8 Support the podcast & join our community of Pythonistas…

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marimo: Reactive Notebooks and Deployable Web Apps in Python 1:00:58

منذ 14 weeks1:00:58

1:00:58

What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python. Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG). Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide. Course Spotlight: Navigating Namespaces and Scope in Python In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope. Topics: 00:00:00 – Introduction 00:02:06 – Akshay’s background and studies 00:04:14 – Work at Google and PhD program 00:06:29 – Sharing notebooks 00:08:18 – Starting work on marimo 2 years ago 00:12:48 – Avoiding notebook issues and building a DAG 00:18:39 – The difference of reactivity 00:20:39 – What is a marimo notebook? 00:23:39 – Video Course Spotlight 00:24:50 – Reproducibility and managing package requirements 00:27:49 – Using decorators for cells 00:30:23 – Writing a design document before any coding 00:34:08 – Interactivity and UI widgets 00:38:20 – Design decisions and built-in widgets 00:42:05 – Creating a deployable web application 00:44:34 – Exploring examples and tutorials 00:46:13 – Supporting DataFrame libraries with narwhals 00:48:00 – Migrating from a Jupyter notebook 00:52:02 – Working with cells and not running code 00:54:30 – A couple favorite tutorials 00:56:17 – What are you excited about in the world of Python? 00:57:39 – What do you want to learn next? 00:59:34 – How can people follow the project and yourself? 01:00:12 – Thanks and goodbye Show Links: marimo - a next-generation Python notebook marimo: an open-source reactive notebook for Python - Akshay Agrawal (Nbpy2024) - YouTube TensorFlow Made with marimo - marimo FAQ - marimo Pluto.jl — interactive Julia programming environment Observable: Build expressive charts and dashboards with code We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned - The Datalore Blog A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks Lessons learned reinventing the Python notebook - marimo Episode #226: PySheets: Spreadsheets in the Browser Using PyScript PEP 723 – Inline script metadata Inline script metadata - Python Packaging User Guide Serializing package requirements in marimo notebooks - marimo uv: Unified Python packaging marimo Newsletter 7 - Jupyter to marimo Custom UI elements - marimo anywidget - anywidget Interactive elements - marimo Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries Calmcode - marimo: Introduction Join the marimo Discord marimo newsletter marimo on Twitter marimo on LinkedIn Akshay Agrawal’s website Aksahy on Twitter Level up your Python skills with our expert-led courses: Navigating Namespaces and Scope in Python Python Decorators 101 Using Jupyter Notebooks Support the podcast & join our community of Pythonistas…

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The Joy of Tinkering & Python Free-Threading Performance 45:50

منذ 15 weeks45:50

45:50

What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss the joy of tinkering with Python as a way to keep your developer skills sharp. We dig into our techniques for continuing to learn and build projects. Christopher shares an article that examines the performance of Python 3.13’s free-threading features. This piece uses a clever example to measure how the new features behave with large datasets and parallelization. We share several other articles and projects from the Python community, including a group of new releases, common use cases and examples for Python closures, finding the opposite of cloud-native, Python’s soft keywords, a command-line utility for taking automated screenshots of websites, and putting the Django admin in the terminal with Textual. This episode is sponsored by Windsurf. Course Spotlight: Python Inner Functions In this step-by-step course, you’ll learn what inner functions are in Python, how to define them, and what their main use cases are. You’ll see how to write helper functions, create closure factory functions, and how to add behavior to existing functions with decorators. Topics: 00:00:00 – Introduction 00:02:18 – Django Bugfix Release Issued: 5.1.3 00:02:46 – Pillow Release 11.0.0 00:03:14 – Flask Version 3.1.0 00:03:30 – PyCon US 2025 (Pittsburgh) Call for Proposals 00:03:46 – Python Closures: Common Use Cases and Examples 00:09:20 – State of Python 3.13 Performance: Free-Threading 00:15:42 – Sponsor: Windsurf 00:16:32 – Opposite of Cloud Native Is…? 00:22:36 – Python’s Soft Keywords 00:24:50 – Video Course Spotlight 00:26:11 – The Joy of Tinkering 00:38:33 – shot-scraper: A command-line utility for taking automated screenshots of websites 00:41:13 – django-admin-tui: Django Admin in the Terminal! 00:42:37 – django-admin-dracula: Dracula Themes for the Django Admin 00:44:21 – Thanks and goodbye News: Django Bugfix Release Issued: 5.1.3 Pillow Release 11.0.0 Flask Version 3.1.0 PyCon US 2025 (Pittsburgh) Call for Proposals Show Links: Python Closures: Common Use Cases and Examples – In this tutorial, you’ll learn about Python closures. A closure is a function-like object with an extended scope. You can use closures to create decorators, factory functions, stateful functions, and more. State of Python 3.13 Performance: Free-Threading – This article does a comparison between code in single threaded, threaded, and multi-process versions under Python 3.12, 3.13, and 3.13 free-threaded with the GIL on and off. Opposite of Cloud Native Is…? – Michael (from Talk Python fame) introduces the concept of “stack-native” as the opposite of “cloud-native”, and how it applies to Python web apps. Building applications with just enough full-stack building blocks to run reliably with minimal complexity, rather than relying on a multitude of cloud services. Python’s soft keywords – Python includes soft keywords: tokens that are important to the parser but can also be used as variable names. This article shows you what a soft keyword is and how to find them in Python 3.12 (both the easy and hard way). Discussion: Habits of Great Software Engineers - The Joy of Tinkering Projects: shot-scraper: A command-line utility for taking automated screenshots of websites django-admin-tui: Django admin in the terminal! django-admin-dracula: 🦇 Dracula themes for the Django admin Additional Projects: Primer on Python Decorators – Real Python We’ve moved to Hetzner - Talk Python Blog Talk Python rewritten in Quart (async Flask) - Talk Python Blog PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Python Decorators 101 Python Inner Functions Defining and Calling Python Functions Support the podcast & join our community of Pythonistas…

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Maintaining the Foundations of Python & Cautionary Tales 1:09:09

منذ 16 weeks1:09:09

1:09:09

How do you build a sustainable open-source project and community? What lessons can be learned from Python’s history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation. Paul has been an organizer in the Python community almost from the beginning. He shares how the project has navigated through multiple sponsors. We talk about the early governance models and the formation of the Python Software Foundation. We contrast this journey with the current drama unfolding in the WordPress community. We discuss the potential problems of having a benevolent dictator for life. We also dig into sponsorship models and ways to get companies to give back to the open-source projects they rely on. This episode is sponsored by Sentry. Course Spotlight: Using pandas to Make a Gradebook in Python With this course and Python project, you’ll build a script to calculate grades for a class using pandas. The script will quickly and accurately calculate grades from a variety of data sources. You’ll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics. Topics: 00:00:00 – Introduction 00:01:55 – Meeting Jodie Burchell at PyCon 2022 00:02:51 – A non-traditional path into open-source 00:07:09 – The current turmoil around WordPress 00:13:49 – Keeping things fair in the age of extraction 00:16:03 – Sponsor: Sentry 00:17:07 – Early Python organizing history and conservation 00:20:41 – The Python Software Activity precursor to PSF 00:24:14 – Creating the Python Software Foundation 00:27:24 – Keeping the perfect distance of business and project 00:28:13 – Who gets to capture the value from open-source? 00:31:07 – Sponsorships becoming more common 00:33:24 – BDFL to a steering council 00:34:58 – Video Course Spotlight 00:36:16 – What is Plone? 00:38:11 – Starting in Python and finding community 00:50:07 – Companies contributing 00:53:16 – Examples of how JetBrains contributes back 00:55:41 – Understanding the support system 00:58:09 – Talking to decision makers 01:00:07 – Python 1994 talk and continuation 01:01:49 – What are you excited about in the world of Python? 01:03:06 – What do you want to learn next? 01:04:17 – How can people follow your work online? 01:07:16 – Thanks and goodbye Show Links: JetBrains: Essential tools for software developers and teams PyCharm: the Python IDE for data science and web development PyCon - Join us at PyCon Benevolent dictator for life - Wikipedia The messy WordPress drama, explained - The Verge WordPress.org’s latest move involves taking control of a WP Engine plugin - The Verge WP Engine asks court to stop Matt Mullenweg from blocking access to WordPress resources - The Verge Podcast: Why the WordPress Chaos Matters - 404 Media Zope - Wikipedia Python Software Foundation PyLadies – Women Who Love Coding in Python Django Software Foundation - Django OpenCV - About Page Plone Foundation FastHTML - Modern web applications in pure Python Paul Everitt - Python 1994 - YouTube A Team at Microsoft is Helping Make Python Faster - Python Velda Kiara JetBrains Blog: The Drive to Develop Paul Everitt (@pauleveritt@fosstodon.org) - Fosstodon Guido van Rossum - Wikipedia The History of Python: Personal History - part 1, CWI Oral History of Guido van Rossum, part 1 - YouTube Level up your Python skills with our expert-led courses: Building Python Project Documentation With MkDocs The pandas DataFrame: Working With Data Efficiently Using pandas to Make a Gradebook in Python Support the podcast & join our community of Pythonistas…

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New PEPs: Template Strings & External Wheel Hosting 47:58

منذ 17 weeks47:58

47:58

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a set of recent Python Enhancement Proposals (PEPs). The idea of template strings has been under consideration for a while, and PEP 750 describes a new way forward. PEP 759 proposes a way for projects on PyPI to safely host resources on external sites using a new package upload format called a .rim file. We share several other articles and projects from the Python community, including what didn’t make the headlines about Python 3.13, solving Sudoku with Python packaging, what’s sweet about Python’s syntactic sugar, creating database-generated columns using SQLite and Django, a discussion about mentoring, an adaptive web scraper, and a debugging tool for HTTP(S) client requests. This episode is sponsored by Sentry. Course Spotlight: Using Pydantic to Simplify Python Data Validation Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on video course, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic. Topics: 00:00:00 – Introduction 00:02:08 – Python 3.14.0 Alpha 1 Released 00:02:38 – Python 3.13, What Didn’t Make the Headlines 00:05:23 – What’s up Python? 3.13 is out, t-strings look awesome 00:10:21 – Sponsor: Sentry 00:11:25 – Sudoku in Python Packaging 00:14:29 – Syntactic Sugar: Why Python Is Sweet and Pythonic 00:22:31 – Database generated columns: Django & SQLite 00:27:14 – Video Course Spotlight 00:28:39 – Mentors 00:42:23 – Scrapling: Lightning-Fast, Adaptive Web Scraping for Python 00:44:14 – httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests 00:46:04 – Request for project submissions to PyCoders 00:46:59 – Thanks and goodbye News: Python 3.14.0 Alpha 1 Released Show Links: Python 3.13, What Didn’t Make the Headlines – Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb , improvements to shutil , and small additions to the asyncio library. What’s up Python? 3.13 is out, t-strings look awesome, dep groups come in handy… Sudoku in Python Packaging – Simon writes about a Sudoku solver written by Konstin that uses the Python packaging mechanisms to do Sudoku puzzles. The results are output using a requirements.txt file, where sudoku-0-3==5 represents the (0,3) cell’s answer of 5. Syntactic Sugar: Why Python Is Sweet and Pythonic – In this tutorial, you’ll learn what syntactic sugar is and how Python uses it to help you create more readable, descriptive, clean, and Pythonic code. You’ll also learn how to replace a given piece of syntactic sugar with another syntax construct. Database generated columns: Django & SQLite – An introduction to database generated columns, using SQLite and the new GeneratedField added in Django 5.0 Discussion: Mentors – Ryan just finished his second round of mentoring with the Djangonaut.Space program. This post talks about how you can help your mentor help you and how to be a good mentor. Projects: Scrapling: Lightning-Fast, Adaptive Web Scraping for Python httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests in a Python program Additional Links: PEP 750 – Template Strings PEP 735 – Dependency Groups in pyproject.toml PEP 759 – External Wheel Hosting Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon – The Real Python Podcast Episode #92: Continuing to Unravel Python’s Syntactic Sugar With Brett Cannon – The Real Python Podcast Episode #4: Learning Python Through Errors – The Real Python Podcast PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Using Pydantic to Simplify Python Data Validation Python Type Checking Using Type Hints for Multiple Return Types in Python Support the podcast & join our community of Pythonistas…

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