Do you struggle to make sure your code is always correct before you check it in? What about your team members' code? That one person who never wants to run the linter? Tired of dealing with tons of conflicts and spurious git changes? You need git pre-commit hooks. We're lucky to have Stefanie Molin on this episode who has done a bunch of writing and teaching of git hooks. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Bluehost Talk Python Courses Links from the show Stefanie Molin: stefaniemolin.com Talk Python Blog: talkpython.fm/blog How to Set Up Pre-Commit Hooks: stefaniemolin.com Common Pre-Commit Errors and How to Solve Them: stefaniemolin.com A Behind-the-Scenes Look at How Pre-Commit Works: stefaniemolin.com Pre-Commit Hook Creation Guide: stefaniemolin.com (Pre-)Commit to Better Code Workshop: stefaniemolin.com exif-stripper: stefaniemolin.com exif-stripper on GitHub: github.com docstring-validation-using-pre-commit-hook: numpydoc.readthedocs.io Data Morph: Moving Beyond the Datasaurus Dozen: stefaniemolin.com Data Morph on GitHub: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Hynek has been writing and speaking on some of the most significant topics in the Python space and I've enjoyed his takes. So I invited him on the show to share them with all of us. This episode really epitomizes one of the reasons I launched Talk Python 9 years ago. It's as if we run into each other at a bar during a conference and I ask Hynek, "So what are your thoughts on ..." and we dive down the rabbit hole for an hour. I hope you enjoy it. Episode sponsors WorkOS Bluehost Talk Python Courses Links from the show Hynek Schlawack on Mastodon: @hynek Why I Still Use Python Virtual Environments in Docker: hynek.me Production-ready Python Docker Containers with uv: hynek.me Attrs: github.com uv: astral.sh What’s New In Python 4: python.org BusyBox: busybox.net Hynek's YouTube Channel: youtube.com MOPUp for macOS: github.com Homebrew Python Is Not For You: justinmayer.com argon2-cffi: Argon2 for Python: github.com pytest-freethreaded: github.com LM Studio: lmstudio.ai StackOverflow Trends Graph: trends.stackoverflow.co Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

If you work in data science, you definitely know about data frame libraries. Pandas is certainly the most popular, but there are others such as cuDF, Modin, Polars, Dask, and more. They are all similar but definitely not the same APIs and Polars is quite different. But here's the problem. If you want to write a library that is for users of more than one of these data frame frameworks, how do you do that? Or if you want to leave open the possibility of changing yours after the app is built, same problem. That's the problem that Narwhals solves. We have Marco Gorelli on the show to tell us all about it. Episode sponsors WorkOS Talk Python Courses Links from the show Marco Gorelli: @marcogorelli Marco on LinkedIn: linkedin.com Narwhals: github.io Narwhals on Github: github.com DuckDB: duckdb.org Ibis: ibis-project.org modin: readthedocs.io Pandas and Beyond with Wes McKinney: talkpython.fm Polars: A Lightning-fast DataFrame for Python: talkpython.fm Polars: pola.rs Pandas: pandas.pydata.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

You're about to launch your new app or API, or even just a big refactor of your current project. Will it stand up and deliver when you put it into production or when that big promotion goes live? Or will it wither and collapse? How would you know? Well you would test that of course. We have Anthony Shaw back on the podcast to dive into a wide range of tools and techniques for performance and loading testing of web apps. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON WorkOS Talk Python Courses Links from the show Anthony on Twitter: @anthonypjshaw Anthony's PyCon Au Talk: youtube.com locust load testing tool: locust.io playwright: playwright.dev mimesis: github.com mimesis providers: mimesis.name vscode pets: marketplace.visualstudio.com vscode power-mode: marketplace.visualstudio.com opentelemetry: opentelemetry.io uptime-kuma: github.com Talk Python uptime / status: talkpython.fm/status when your serverless computing bill goes parabolic...: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you have kids? Maybe nieces and nephews? Or maybe you work in a school environment? Maybe it's just friend's who know you're a programmer and ask about how they should go about introducing programming concepts with them. Anna-Lena Popkes is back on the show to share her research on when and how to teach kids programming. We spend the second half of the episode talking about concrete apps and toys you might consider for each age group. Plus, some of these things are fun for adults too. ;) Episode sponsors WorkOS Talk Python Courses Links from the show Anna-Lena: alpopkes.com Magical universe repo: github.com Machine learning basics repo: github.com PyData recording "when and how to start coding with kids": youtube.com Robots and devices Bee Bot: terrapinlogo.com Cubelets: modrobotics.com BBC Microbit: microbit.org RaspberryPi: raspberrypi.com Adafruit Qualia ESP32 for CircuitPython: adafruit.com Zumi: robolink.com Board games Think Fun Robot Turtles Board Game: amazon.com Visual programming: Scratch Jr.: scratchjr.org Scratch: scratch.org Blocky: google.com Microbit's Make Code: microbit.org Code Club: codeclubworld.org Textual programming Code Combat: codecombat.com Hedy: hedycode.com Anvil: anvil.works Coding classes / summer camps (US) Portland Community College Summer Teen Program: pcc.edu Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python. Episode sponsors Posit Talk Python Courses Links from the show Course: Getting Started with NLP and spaCy: talkpython.fm Vincent on X: @fishnets88 Vincent on Mastodon: @koaning Programmable Keyboards on CalmCode: youtube.com Sample Space Podcast: youtube.com spaCy: spacy.io Course: Build An Audio AI App: talkpython.fm Lemma example: github.com Code for spaCy course: github.com Python Bytes transcripts: github.com scikit-lego: github.com Projects that import "this": calmcode.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

A couple of weeks ago, Charlie Marsh and the folks at Astral made another big splash with a major release of uv called "uv: Unified Python packaging" which has many far reaching features. We had to have Charlie on the show to give us the inside look into this development. Let's get to it. Episode sponsors Posit Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh uv: Unified Python packaging: astral.sh Python executable management: astral.sh Projects: astral.sh Tools: astral.sh Scripts: astral.sh Rye and uv: August is Harvest Season for Python Packaging: lucumr.pocoo.org Python Build Standalone releases: github.com Rules: astral.sh Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Every year the core developers meet to discuss and propose the major changes and trends in Python itself. This invite-only conference of about 50 people happens inside PyCon in the US. Because it's private, we rarely get detailed looks inside this event. On this episode, we have Seth Michael Larson here to give us his account of the sessions and proposals. It's a unique look into the zeitgeist of CPython. Episode sponsors Posit Talk Python Courses Links from the show Seth on Mastodon: @[email protected] Seth on Twitter: @sethmlarson Seth on Github: github.com The Python Language Summit 2024: blogspot.com PEP 2026: Calendar versioning for Python: github.com PSF authorized as a CVE Numbering Authority: python.org Recommends Memory-Safe Programming Languages: blogspot.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists. Episode sponsors Posit Talk Python Courses Links from the show Stan on Twitter: @seibert Anaconda: anaconda.com High Performance Python with Numba training: learning.anaconda.cloud PEP 0703: peps.python.org Python 3.13 gets a JIT: tonybaloney.github.io Numba: numba.pydata.org LanceDB: lancedb.com Profiling tips: docs.python.org Memray: github.com Fil: a Python memory profiler for data scientists and scientists: pythonspeed.com Rust: rust-lang.org Granian Server: github.com PIXIE at SciPy 2024: github.com Free threading Progress: py-free-threading.github.io Free Threading Compatibility: py-free-threading.github.io caniuse.com: caniuse.com SPy, presented at PyCon 2024: us.pycon.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you feel like ADHD is holding you back? Maybe you don't personally have ADHD but you work with folks who do and you'd like to support them better. Either way, how ADHD interplays with programming and programmers is pretty fascinating. On this episode we have Chris Ferdinandi who himself has ADHD and has written a lot about it to share his journey and his advice for thriving with ADHD as a programmer or data scientist. Episode sponsors Posit Talk Python Courses Links from the show Chris on Mastodon: @cferdinandi ADHD FTW Talk Python Page: adhdftw.com Building a Second Brain: buildingasecondbrain.com Building a Second Brain Book: buildingasecondbrain.com White Collar Jobs are Just Meetings: theatlantic.com Article with Fighting Duck-Sized Horses Agile: mensurdurakovic.com Nothing Phone: nothing.tech Apple Watch: apple.com Todoist: todoist.com Anytype (open source Notion): anytype.io Obsidian: obsidian.md Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Flask is one of the most important Python web frameworks and powers a bunch of the internet. David Lord, Flask's lead maintainer is here to give us an update on the state of Flask and Pallets in 2024. If you care about where Flask is and where it's going, you'll definitely want to listen in. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show David on Mastodon: @davidism David on X: @davidism State of Pallets 2024 FlaskCon Talk: youtube.com FlaskCon: flaskcon.com FlaskCon 2024 Talks: youtube.com Pallets Discord: discord.com Pallets Eco: github.com JazzBand: jazzband.co Pallets Github Org: github.com Jinja: github.com Click: github.com Werkzeug: github.com MarkupSafe: github.com ItsDangerous: github.com Quart: github.com pypistats: pypistats.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

If you want to get better at something, often times the path is pretty clear. If you get better at swimming, you go to the pool and practice your strokes and put in time doing the laps. If you want to get better at mountain biking, hit the trails and work on drills focusing on different aspects of riding. You can do the same for programming. Reuven Lerner is back on the podcast to talk about his book Pandas Workout. We dive into strategies for learning Pandas and Python as well as some of his workout exercises. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Scalable Path Talk Python Courses Links from the show Reuven Lerner on Twitter: @reuvenmlerner Pandas Workout Book: manning.com Bamboo Weekly: Solar eclipse: bambooweekly.com Bamboo Weekly: Avocado hand: bambooweekly.com Scaling data science across Python and R: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Python is special. It's used by the big tech companies but also by those you would rarely classify as developers. On this episode, we get a look inside how Python is being used at a Children's Hospital to speed and improve patient care. We have Dr. Somak Roy here to share how he's using Python in his day to day job to help kids get well a little bit faster. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Posit Talk Python Courses Links from the show Somak Roy: linkedin.com Cincinnati Children's Hospital: cincinnatichildrens.org CNVkit: Genome-wide copy number: readthedocs.io cnaplotr: github.com hgvs: readthedocs.io openpyxl: readthedocs.io Hera is an Argo Python SDK: github.com insiM: in silico Mutator software for bioinformatics: github.com Bamsurgeon: github.com pysam - An interface for reading and writing SAM files: readthedocs.io Scientists rename human genes to stop Microsoft Excel from misreading them as dates: theverge.com BioPython: biopython.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Python is one of the most popular languages of the current era. It dominates data science, it an incredible choice for web development, and its many people's first language. But it's not super great on front-end programing, is it? Frameworks like React, Vue and other JavaScript frameworks rule the browser and few other languages even get a chance to play there. But with pyscript, which I've covered several times on this show, we have the possibility of Python on the front end. Yet it's not really a front end framework, just a runtime in the browser. That's why I'm excited to have Ken Kinder on the podcast to talk about his project PuePy, a reactive frontend framework in Python. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Code Comments Talk Python Courses Links from the show Michael's Code in a Castle Course: talkpython.fm/castle Ken Kinder: @[email protected] PuePy: puepy.dev PuePy Docs: docs.puepy.dev PuePy on Github: github.com pyscript: pyscript.net VueJS: vuejs.org Hello World example: docs.puepy.dev Tutorial: docs.puepy.dev Tutorial running at pyscript.com: pyscript.com Micropython: micropython.org Pyodide: pyodide.org PgQueuer: github.com Writerside: jetbrains.com Michael's PWA pyscript app: github.com Michael's demo of a PWA pyscript app: youtube.com Python iOS Web App with pyscript and offline PWAs video: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

I've gathered a group of Python experts who have been thinking deeply about where Python is going and who have lived through where it has been. This episode is all about near-term Python trends and things we each believe will be important to focus on as Python continues to grow. Our panelists are Jodie Burchell, Carol Willing, and Paul Everett. Episode sponsors Code Comments Posit Talk Python Courses Links from the show Shiny course at Talk Python: talkpython.fm/shiny Jodie Burchell: @t_redactyl Carol on Mastodon: @[email protected] Paul Everitt: @paulweveritt Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

I have a special episode for you this time around. We're coming to you live from PyCon 2024. I had the chance to sit down with some amazing people from the data science side of things: Jodie Burchell, Maria Jose Molina-Contreras, and Jessica Greene. We cover a whole set of recent topics from a data science perspective. Though we did have to cut the conversation a bit short as they were coming from and go to talks they were all giving but it was still a pretty deep conversation. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Code Comments Talk Python Courses Links from the show Jodie Burchell: @t_redactyl Jessica Greene: linkedin.com Maria Jose Molina-Contreras: linkedin.com Talk Python's free Shiny course: talkpython.fm/shiny Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

You're using Pydantic and it seems pretty straightforward, right? But could you adopt some simple changes to your code that would make it a lot faster and more efficient? Chances are, you'll find a couple of the tips from Sydney Runkle that will do just that. Join us to talk about Pydantic performance tips here on Talk Python. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Code Comments Talk Python Courses Links from the show Sydney Runkle: linkedin.com Pydantic: pydantic.dev Performance docs: docs.pydantic.dev Union tips: docs.pydantic.dev Sydney's presentation slides: docs.google.com JSON to Pydantic: jsontopydantic.com Samuel talking FastUI: talkpython.fm CodeFlash: codeflash.ai Codspeed: codspeed.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Porkbun Talk Python Courses Links from the show Ines Montani on Twitter: @_inesmontani spaCy: spacy.io Prodigy App: prodi.gy Ines' presentation at PyCon Lithuania: youtube.com LM Studio: lmstudio.ai Little Bobby Tables: xkcd.com spaCy and NLP course: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you want to look inside your Django request? How about all of your requests in development and see where they overlap? If that sounds useful, you should check out Kolo. It's a pretty incredible extension for your editor (VS Code at the moment, more editors to come most likely). We have Wilhelm Klopp on to tell us all about it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Wil on Twitter: @wilhelmklopp Kolo: kolo.app Kolo's info repo: github.com Kolo Playground: play.kolo.app Generating tests with Kolo: kolo.app Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

So you've created a web app with Python using Flask, Django, FastAPI, or even Emmett. It works great on your machine. How do you get it out to the world? You'll need a production-ready web server. On this episode, we have Giovanni Barillari to tell us about his relatively-new server named Granian. It promises better performance and much better consistency than many of the more well known ones today. Episode sponsors Neo4j Talk Python Courses Links from the show New spaCy course: talkpython.fm Giovanni: @gi0baro Granian: github.com Emmett: emmett.sh Renoir: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He's the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space. Episode sponsors Neo4j Mailtrap Talk Python Courses Links from the show Wes' Website: wesmckinney.com Pandas: pandas.pydata.org Apache Arrow: arrow.apache.org Ibis: ibis-project.org Python for Data Analysis - Groupby Summary: wesmckinney.com/book Polars: pola.rs Dask: dask.org Sqlglot: sqlglot.com Pandoc: pandoc.org Quarto: quarto.org Evidence framework: evidence.dev pyscript: pyscript.net duckdb: duckdb.org Jupyterlite: jupyter.org Djangonauts: djangonaut.space Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you use Python in an academic setting? Maybe you run a research lab or teach courses using Python. Maybe you're even a student using Python. Whichever it is, you'll find a ton of great advice in this episode. I talk with Keiland Cooper about how he is using Python at his neuroscience lab at the University of California, Irvine. Episode sponsors Neo4j Posit Talk Python Courses Links from the show Keiland's website: kwcooper.xyz Keiland on Twitter: @kw_cooper Keiland on Mastodon: @[email protected] Journal of Open Source Software: joss.readthedocs.io Avalanche project: avalanche.continualai.org ContinualAI: continualai.org Executable Books Project: executablebooks.org eLife Journal: elifesciences.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you find yourself or your team building internal apps frequently for your company? Are you familiar with the term "forms over data"? They are super empowering for your org but they can be pretty repetitive and you might find yourself spending more time than you'd like working on them rather than core products and services. I invited Jimmy Chan from Dropbase to tell us about their service who's tagline is "Build internal web apps with just Python." It's a cool service and a fun conversation. Episode sponsors Mailtrap Talk Python Courses Links from the show Build internal web apps with just Python.: dropbase.io Dropbase on Github: github.com Dropbase @ LinkedIn: linkedin.com Dropbase on Twitter: twitter.com Jimmy Chan: linkedin.com Jimmy on Twitter: twitter.com Dropbase Docs: docs.dropbase.io Dropbase: dropbase.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

We all know that tools like ChatGPT have really empowered developers to tackle bigger problems. Are you using TailwindCSS and need a login page? Try asking Chat "What is the HTML for a login page with the login username, password, and button in its own section in the center of the page?" It will literally give you a first pass version of it. But how far can you push this? Fred Tubiermont may have taken it farther than most. He built a functioning SaaS product with paying customers by only using ChatGPT and Python. It's fascinating to hear his story. Episode sponsors Mailtrap Talk Python Courses Links from the show Frederick Tubiermont: linkedin.com The #1 AI Jingle Generator: aijinglemaker.com Fred's YouTube Channel: youtube.com AI Coding Club: aicodingclub.com No Code: saashub.com Prompt Engineering 101 - Crash Course & Tips: youtube.com gpt-engineer: github.com Instant Deployments, Effortless Scale: railway.app Self-hosting with superpowers.: coolify.io The newsletter platform built for growth.: beehiiv.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

What is the state of serverless computing and Python in 2024? What are some of the new tools and best practices? We are lucky to have Tony Sherman who has a lot of practical experience with serverless programming on the show. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Mailtrap Talk Python Courses Links from the show Tony Sherman on Twitter: twitter.com Tony Sherman: linkedin.com PyCon serverless talk: youtube.com AWS re:Invent talk: youtube.com Powertools for AWS Lambda: docs.powertools.aws.dev Pantsbuild: The ergonomic build system: pantsbuild.org aws-lambda-power-tuning: github.com import-profiler: github.com AWS Fargate: aws.amazon.com Run functions on demand. Scale automatically.: digitalocean.com Vercel: vercel.com Deft: deft.com 37 Signals We stand to save $7m over five years from our cloud exit: world.hey.com The Global Content Delivery Platform That Truly Hops: bunny.net Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

We've spoken previously about security and software supply chains and we are back at it this episode. We're diving in again with Charles Coggins. Charles works at a software supply chain company and is on to give us the insiders and defender's perspective on how to keep our Python apps and infrastructure safe. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Mailtrap Talk Python Courses Links from the show Pick a Python Lockfile and Improve Security: blog.phylum.io Bad Beat Poetry: blog.phylum.io PEP 665 – A file format to list Python dependencies for reproducibility of an application: peps.python.org PEP 517 – A build-system independent format for source trees: peps.python.org PEP 518 – Specifying Minimum Build System Requirements for Python Projects: peps.python.org Lockfiles should be committed on all projects: classic.yarnpkg.com An Overview of Software Supply Chain Security: tldrsec.com Typosquatting: docs.phylum.io Common Attack Pattern Enumeration and Classification: capec.mitre.org Dependency Confusion: docs.phylum.io Expired Author Domains: docs.phylum.io Unverifiable Dependency: docs.phylum.io Repo Jacking: Hidden Danger in Broken Links: blog.phylum.io Software Libraries Are Terrifying: medium.com phylum 0.43.0: pypi.org linguist: github.com rich-codex ⚡️📖⚡️: ewels.github.io Phylum Community Discord: discord.gg The dream is dead?: mastodon.social When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev pip-tools: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Interested in data science but you're not quite working in it yet? In software, getting that very first job can truly be the hardest one to land. On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice for getting your first data job. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Posit Talk Python Courses Links from the show Avery Smith: www.linkedin.com Data Career Jumpstart: www.datacareerjumpstart.com Data Nerd Site: datanerd.tech Write C# LINQ queries to query data: learn.microsoft.com A faster way to build and share data apps: streamlit.io Plotly Dash: dash.plotly.com Michael's Keynote: State of Python in 2024: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you have data that you pull from external sources or is generated and appears at your digital doorstep? I bet that data needs processed, filtered, transformed, distributed, and much more. One of the biggest tools to create these data pipelines with Python is Dagster. And we are fortunate to have Pedram Navid on the show this episode. Pedram is the Head of Data Engineering and DevRel at Dagster Labs. And we're talking data pipelines this week at Talk Python. Episode sponsors Talk Python Courses Posit Links from the show Rock Solid Python with Types Course: training.talkpython.fm Pedram on Twitter: twitter.com Pedram on LinkedIn: linkedin.com Ship data pipelines with extraordinary velocity: dagster.io dagster-open-platform: github.com The Dagster Master Plan: dagster.io data load tool (dlt): dlthub.com DataFrames for the new era: pola.rs Apache Arrow: arrow.apache.org DuckDB is a fast in-process analytical database: duckdb.org Ship trusted data products faster: www.getdbt.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Have you ever been wait around for pip to do its thing while installing packages or syncing a virtual environment or through some higher level tool such as pip-tools? Then you'll be very excited to hear about the tool just announced from Astral called uv. It's like pip, but 100x faster. Charlie Marsh from Ruff fame and founder of Astral is here to dive in. Let's go. Episode sponsors Neo4j Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral: astral.sh uv: github.com Ruff: github.com Ruff Rules: docs.astral.sh When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev Talk Python's free Audio AI Course: training.talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Have you heard of Quart? It's the fully-async version of Flask created by Philip Jones who is working closely with the Flask team on these parallel projects. The TL;DR; version is that if you want to take advantage of async and await and you're using Flask, you want to give Quart a solid look. We've spoken to Philip previously about Quart. This time around here's here to share his top Quart extensions and libraries you can adopt today. Episode sponsors Posit Talk Python Courses Links from the show Pallets Team on ExTwitter: @PalletsTeam Quart Framework: quart.palletsprojects.com Using Quart Extensions: quart.palletsprojects.com Quart Tasks: quart-tasks.readthedocs.io Quart Minify: github.com Quart Db: github.com Hypercorn: github.com Quart-CORS: github.com Quart-Auth: github.com Quart-Rate: github.com Quart-Schma: github.com Flask-Socket: github.com Quart-SqlAlchemy: github.com Flask-Login: github.com greenback: github.com secure: github.com msgspec: jcristharif.com Server-Sent Events: pgjones.gitlab.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Are you interested in contributing to Django? Then there is an amazing mentorship program that helps Python and Django enthusiasts, because contributes and potentially core developers of Django. It's called Djangonauts and their slogan is "where contributors launch." On this episode, we have Sarah Boyce from the Django team and former Djangonaut and now Djangonaut mentor, Tushar Gupta. Not only is this excellent for the Django community, many of other open source communities would do well to keep an eye on how this creative project is working. Episode sponsors Neo4j Posit Talk Python Courses Links from the show Sarah on Mastodon: @[email protected] Sarah on LinkedIn: linkedin.com Tushar on Twitter: @tushar5526 Djangonaut Space on Mastodon: @[email protected] Djangonaut Space on Twitter: @djangonautspace Djangonaut Space on LinkedIn: linkedin.com Website: djangonaut.space Djangonaut Space Launch Video: youtube.com Sessions: djangonaut.space Djangonaut Space Interest Form: google.com/forms Program: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

You've built an awesome set of APIs and you have a wide array of devices and clients using them. Then you need to upgrade an end point or change them in a meaningful way. Now what? That's the conversation I dive into over the next hour with Stanislav Zmiev. We're talking about Versioning APIs. Episode sponsors Neo4j Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Stanislav Zmiev: github.com Monite: monite.com Cadwyn: github.com Stripe API Versioning: stripe.com API Versioning NOtes: github.com FastAPI-Versioning: github.com Flask-Rebar: readthedocs.io Django Rest Framework Versioning: django-rest-framework.org pytest-fixture-classes: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Building web UIs in Python has always been in interesting proposition. On one end, we have a the full web design story with artisanal HTML and CSS. On another end there are several Python platforms that aim to the bring RAD, rapid app development, style of building with Python. Those can be great, and I've covered a couple of them, but they usually reach a limit on what they can do or how they integrate with the larger web ecosystem. On this episode, we have Samuel Colvin to share his latest exciting project FastUI. With FastUI, you build responsive web applications using React without writing a single line of JavaScript, or touching npm. Yet designers and other tools can focus on React front-ends for a professional SPA like app experience. Episode sponsors bright data Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Samuel on Mastodon: fosstodon.org Samuel on X: x.com FastUI: github.com FastUI Demos: fastui-demo.onrender.com FastAPI: fastapi.tiangolo.com Pydantic: pydantic.dev How Did REST Come To Mean The Opposite of REST Article: htmx.org Tailwind UI: tailwindui.com Dropbase: dropbase.io Anvil: anvil.works Flutter code example: github.com ReactJS code example: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

So you've created a Python-based open source project and it's started to take off. You're getting contributors, lots of buzz in the podcast space, and more. But you have that day job working on Java. How do you make the transition from popular hobby project to full time job? After all, you are giving away your open source project for free, right? Well, on this episode, I have put together an amazing panel of guests who all have done exactly this: Turned their project into full time work and even companies in some cases. We have Samuel Colvin, Gina Häußge, Sebastián Ramírez, Charlie Marsh, Will McGugan and Eric Holscher on to share their stories. Episode sponsors Basedash Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Will McGugan: @willmcgugan Charlie Marsh: @charliermarsh@hachyderm Sebastián Ramírez: @tiangolo Samuel Colvin: @samuel_colvin Gina on Mastodon: chaos.social/@foosel Eric Holscher: @ericholscher Pydantic: pydantic.dev Astral (makes of Ruff): astral.sh Octoprint: octoprint.org Read the Docs: readthedocs.com FastAPI: fastapi.tiangolo.com Textual (makes of Rich): textualize.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters. Episode sponsors Pybites PDM Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Guests Anthony Shaw: @[email protected] Eric Snow: @[email protected] PEP 684 – A Per-Interpreter GIL: peps.python.org PEP 734 – Multiple Interpreters in the Stdlib: peps.python.org Running Python Parallel Applications with Sub Interpreters: fosstodon.org pytest subinterpreters: fosstodon.org Long-Term Vision for a Parallel Python Programming Model?: fosstodon.org Hypercorn Server: github.com msgspec: jcristharif.com Dill package: pypi.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Sarah's website: sckaiser.com Sarah on Mastodon: @[email protected] Get started with Python in Excel: microsoft.com Python in SQL Server: microsoft.com 8 of the Biggest Excel Mistakes of All Time: blog.hurree.co Security and Python in Excel: microsoft.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Do you know what custom GPTs are? They're configurable and shareable chat experiences with a name, logo, custom instructions, conversation starters, access to OpenAI tools, and custom API actions. And, you can build them with Python! Ian Maurer has been doing just that and is here to share his experience building them. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Neo4j Talk Python Courses Links from the show Ian on Twitter: @imaurer Mobile Navigation: openai.com What is a Custom GPT?: imaurer.com Mobile Navigation: openai.com FuzzTypes: Pydantic library for auto-correcting types: github.com pypi-gpt: github.com marvin: github.com instructor: github.com outlines: github.com llamafile: github.com llama-cpp-python: github.com LLM Dataset: llm.datasette.io Plugin directory: llm.datasette.io Data exploration at your fingertips.: visidata.org hottest new programming language is English: twitter.com OpenAI & other LLM API Pricing Calculator: docsbot.ai Vector DB Comparison: vdbs.superlinked.com bpytop: github.com Source Graph: about.sourcegraph.com Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

When you run your code in the cloud, how much do you know about where it runs? I mean, the hardware it runs on and the data center it runs in? There are just a couple of hyper-scale cloud providers in the world. This episode is a very unique chance to get a deep look inside one of them: Microsoft Azure. Azure is comprised of over 200 physical data centers, each with 100,000s of servers. A look into how code runs on them is fascinating. Our guide for this journey will be Mark Russinovich. Mark is the CTO of Microsoft Azure and a Technical Fellow, Microsoft's senior-most technical position. He's also a bit of a programming hero of mine. Even if you don't host your code in the cloud, I think you'll enjoy this conversation. Let's dive in. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Mark Russinovich: @markrussinovich Mark Russinovich on LinkedIn: linkedin.com SysInternals: learn.microsoft.com Zero Day: A Jeff Aiken Novel: amazon.com Inside Azure Datacenters: youtube.com What runs chatgpt?: youtube.com Azure Cobalt ARM chip: servethehome.com Closing talk by Mark at Ignite 2023: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Are you early in your software dev or data science career? Maybe it hasn't even really started yet and you're still in school. On this episode we have Sydney Runkle who has had a ton of success in the Python space and she hasn't even graduated yet. We sit down to talk about what she's done and might do differently again to achieve that success. It's "The Young Coder's Blueprint to Success" on episode 444 of Talk Python To Me. Links from the show Sydney Runkle: linkedin.com Pydantic: pydantic.dev Code Combat: codecombat.com Humanitarian Toolbox: www.htbox.org PyCon 2024: pycon.org Good first issue example: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy --- Episode sponsors --- Talk Python Training

Special crossover episode of Python Bytes to wrap up 2023. Topics include: Michael #1: Hatch v1.8 Brian #2: svcs : A Flexible Service Locator for Python Michael #3: Steering Council 2024 Term Election Results Brian #4: Python protocols. When to use them in your projects to abstract and decoupling ExtrasJoke: Joke: The dream is dead? --- Episode sponsors --- Posit Talk Python Training

If you're a fan of Pydantic or dataclasses, you'll definitely be interested in this episode. We are talking about a super fast data modeling and validation framework called msgspec. Some of the types in here might even be better for general purpose use than Python's native classes. Join me and Jim Crist-Harif to talk about his data exchange framework, mspspec. Links from the show Jim Crist-Harif: jcristharif.com Jim @ GitHub: github.com Jim @ Mastdon: @[email protected] msgspec: github.com Projects using msgspec: github.com msgspec on Conda Forge: anaconda.org msgspec on PyPI: pypi.org Litestar web framework: litestar.dev Litestar episode: talkpython.fm Pydantic V2 episode: talkpython.fm JSON parsing with msgspec article: pythonspeed.com msgspec bencharmks: jcristharif.com msgspec vs. pydantic v1 and pydantic v2: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy --- Episode sponsors --- Posit Talk Python Training

You've probably heard the term "syntactic sugar", that is, syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use. It turns out Brett Cannon has spent 2 years diving into and writing about Python's sweet language features and how they really work down inside CPython. He joins me on the show today to dive into a few of the more relevant posts he's written about it. Links from the show Brett Cannon: @[email protected] Syntactic sugar series: snarky.ca Syntactic sugar: wikipedia.org Unravelling attribute access in Python: snarky.ca Unravelling binary arithmetic operations: snarky.ca Unravelling the import statement: snarky.ca record-type: pypi.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy --- Episode sponsors --- Talk Python Training

We all know that LLMs and generative AI has been working its way into many products. It's Jupyter's turn to get a really awesome integration. We have David Qiu here to tell us about Jupyter AI. Jupyter AI provides a user-friendly and powerful way to apply generative AI to your notebooks. It lets you choose from many different LLM providers and models to get just the help you're looking for. And it does way more than just a chat pane in the UI. Listen to find out. Links from the show David Qiu: linkedin.com Jupyter AI: jupyter-ai.readthedocs.io Asking about something in your notebook: jupyter-ai.readthedocs.io Generating a new notebook: jupyter-ai.readthedocs.io Learning about local data: jupyter-ai.readthedocs.io Formatting the output: jupyter-ai.readthedocs.io Interpolating in prompts: jupyter-ai.readthedocs.io JupyterCon 2023 Talk: youtube.com PyData Seattle 2023 Talk: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Posit Talk Python Training

On this episode we have Wolf Vollprecht and Ruben Arts from the pixi project here to talk about pixi, a high performance package manager for Python and other languages that actually manages Python itself too. They have a lot of interesting ideas on where Python packaging should go and are putting their time and effort behind them. Will pixi become your next package manager? Listen in to find out. Links from the show Black Friday at Talk Python: talkpython.fm/blackfriday Guests Wolf Vollprecht: github.com/wolfv Ruben Arts: github.com/ruben-arts pixi: prefix.dev Prefix: prefix.dev Launching pixi: prefix.dev Conda: docs.conda.io Conda Forge: conda-forge.org NixOS: nixos.org Packaging Con 2023: packaging-con.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Posit Python Tutor Talk Python Training

Jupyter Notebooks and Jupyter Lab have to be one of the most important parts of Python when it comes to bring new users to the Python ecosystem and certainly for the day to day work of data scientists and general scientists who have made some of the biggest discoveries of recent times. And that platform has recently gotten a major upgrade with JupyterLab 4 released and Jupyter Notebook being significantly reworked to be based on the changes from JupyterLab as well. We have an excellent panel of guests, Sylvain Corlay, Frederic Collonval, Jeremy Tuloup, and Afshin Darian here to tell us what's new in these and other parts of the Jupyter ecosystem. Links from the show Guests Sylvain Corlay Frederic Collonval Jeremy Tuloup Afshin Darian JupyterLab 4.0 is Here: blog.jupyter.org Announcing Jupyter Notebook 7: blog.jupyter.org JupyterCon 2023 Videos: youtube.com Jupyterlite: github.com Download JupyterLab Desktop: github.com Mythical Man Month Book: wikipedia.org Blender in Jupyter: twitter.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Phylum Python Tutor Talk Python Training

Are you considering or struggling with replacing much of the interactivity of your Django app with frontend JavaScript frameworks? After all, your users do expect an interactive and modern app, right? Before you make a rash decision, you owe it to yourself to check out HTMX. It goes well with Django. We have Christopher Trudeau to run through a whole awesome list of HTMX and Python and tell us about his new HTMX + Django course. Links from the show Chris on ExTwitter: @cltrudeau Django in Action book: manning.com Django: djangoproject.com HTMX + Django course: talkpython.fm HTMX: htmx.org awesome-htmx: github.com awesome-python-htmx: github.com django-js-lib-htmx: github.com htmxflask: github.com fastapi-sse-htmx: github.com django-htmx-patterns: github.com jinja2-fragments: github.com jinja_partials: github.com chameleon_partials: github.com django-render-block: github.com flask-htmx: github.com htmx-flask: github.com asgi-htmx: github.com hx-requests: github.com django-dashboards: github.com A Real World React -> htmx Port: htmx.org 3 IRL use cases for Python and HTMX: bitecode.dev owela-club: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors IRL Podcast Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

How well do you know your Python packaging tools? These are things like pip which install your project's dependencies and their dependencies and so on. In this mix, we have more modern tools such as Poetry, Flit, Hatch and others. And even tools outside of Python itself which may attempt to manage Python itself in addition to the libraries. To make sense of all of this, we welcome back Anna-Lena Popkes for an unbiased evaluation of environment and packaging tools. Links from the show Anna-Lena's website: alpopkes.com Anna-Lena on GitHub: github.com Accompanying Blog Post: alpopkes.com Talk from PyCon DE: youtube.com Talk from EuroPython: youtube.com Talk Python's Data Science Jumpstart with 10 Projects course: talkpython.fm Rye: github.com Poetry: python-poetry.org Material for MkDocs: squidfunk.github.io 100 Days of Python in a Magical Universe Episode: talkpython.fm pip-tools: pip-tools.readthedocs.io Hatch: hatch.pypa.io PDM: pdm.fming.dev Flit: flit.pypa.io Conda: docs.conda.io Pipenv: pipenv.pypa.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors IRL Podcast Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Do you worry about your developer / data science supply chain safety? All the packages for the Python ecosystem are much of what makes Python awesome. But the are also a bit of an open door to your code and machine. Luckily the PSF is taking this seriously and hired Mike Fiedler as the full time PyPI Safety & Security Engineer (not to be confused with the Security Developer in Residence staffed by Seth Michael Larson). Mike is here to give us the state of the PyPI security and plans for the future. Links from the show Mike on Twitter: @mikefiedler Mike on Mastodon: @[email protected] Supply Chain examples SolarWinds: csoonline.com XcodeGhost: wikipedia.org Google Ad Malware: medium.com PyPI: pypi.org OWASP Top 10: owasp.org Trusted Publishers: docs.pypi.org libraries.io: libraries.io GitHub Full 2FA: github.blog Mike's Latest Blog Post: blog.pypi.org pprintpp package: github.com ICDiff: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Are you building a mobile app and wondering where Python fits in the mix? Are you support others building these apps with backend APIs written in Python? Can you write your entire app, end to end, in Python? I have a great panel put together to discuss exactly this. And they all have a different and unique take on the options. Welcome to Loren Aguey, Harout Boujakjian, Andréas Kühne, Jeyfrin and, Joshua. Links from the show Guests Loren Aguey: linkedin.com Harout Boujakjian: linkedin.com Andréas Kühne: linkedin.com Jeyfrin, Joshua Talk Python Mobile App: training.talkpython.fm/apps Epic Skies App: play.google.com PinPlanet App: pinplanetapp.com My Club App: apps.apple.com vid3d App: play.google.com Flutter: flutter.dev Flutter Showcase: flutter.dev pub.dev, Flutter's PyPI: pub.dev FastAPI: fastapi.tiangolo.com Litestar: litestar.dev Pyramid Web Framework: trypyramid.com Flask: flask.palletsprojects.com Django: djangoproject.com Django REST Framework: django-rest-framework.org Kivy: kivy.org Swift: developer.apple.com Ionic Framework: ionicframework.com Ionic Source: github.com Flutter Source: github.com Kivy Source: github.com Bloc: State management for Dart: bloclibrary.dev Swift Package Manager (SwiftPM): swift.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

We all know about Flask and Django. And of course FastAPI made a huge splash when it came on the scene a few years ago. But new web frameworks are being creating all the time. And they have these earlier frameworks to borrow from as well. On this episode we dive into a new framework gaining a lot of traction called Litestar. Will it be the foundation of your next project? Join me as I get to know Litestar with its maintainers: Jacob Coffee, Janek Nouvertné, and Cody Fincher. Links from the show Guests Jacob Coffee Jacob on Github: github.com Jacob on Twitter: @_scriptr Jacob on Mastodon: @Monorepo Cody Fincher Cody on LinkedIn: linkedin.com Cody on GitHub: github.com Email: [email protected] Janek Nouvertné Janek on GitHub: github.com Email: [email protected] Litestar: litestar.dev Litestar Documentation: litestar.dev Litestar on Twitter: @LitestarAPI Litestar on Mastodon: @litestar Litestar Blog: blog.litestar.dev Discord: discord.gg Reddit r/Litestar: eddit.com Litestar on PyPI: pypi.org Benchmarks: docs.litestar.dev v2.0 Release: github.com gunicorn: gunicorn.org msgspec: github.com httpx-sse: github.com duckdb: duckdb.org rich-click: github.com blacksheep server: neoteroi.dev Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

By now, surely you've heard how awesome Pydantic version 2 is. The team led by Samual Colvin spent almost a year refactoring and reworking the core into a high-performance Rust version while keeping the public API in Python and largely unchanged. The main benefit of this has been massive speed ups for frameworks and devs using Pydantic. But just how much work is it to take a framework deeply built on Pydantic and make that migration? What are some of the pitfalls? On this episode, we welcome back Roman Right to talk about his experience converting Beanie, the popular MongoDB async framework based on Pydantic, from Pydantic v1 to v2. And we'll have some fun talking MongoDB as well while we are at it. Links from the show Beanie: beanie-odm.dev Beanie on GitHub: github.com Roman on Twitter: @roman_the_right Beanie Release 1.21.0: github.com Talk Python's MongoDB with Async Python Course: talkpython.fm Pydantic Migration Guide: docs.pydantic.dev Customizing validation with __get_pydantic_core_schema__: docs.pydantic.dev Bunnet (Sync Beanie): github.com Generic `typing.ForwardRef` to support generic recursive types: discuss.python.org Pydantic v2 - The Plan Episode: talkpython.fm Future of Pydantic and FastAPI episode: talkpython.fm Beanie Lazy Parsing: beanie-odm.dev Beanie Relationships: beanie-odm.dev Locust Load Testing: locust.io motor package: pypi.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Studio 3T Talk Python Training

Every year Python has a new major release. This year it's Python 3.12 and it'll come out on October 2, 2023. That's 4 days from when this episode was published. There is quite process involved to test, build, and ship Python across many platforms and channels. We have Seth Michael Larson here to give us a detailed rundown on what exactly is involved in releasing CPython. Links from the show Seth on Mastodon: fosstodon.org/@sethmlarson Seth on Twitter: @sethmlarson Seth on Github: github.com Announcing Security Developer-in-Residence: sethmlarson.dev Visualizing the CPython Release Process: sethmlarson.dev PEP 101: peps.python.org CPython on Github: github.com Best Open SSF: best.openssf.org pip-audit: github.com PyPA Advisory Database: github.com Omnivore App: omnivore.app What's New in 3.12: docs.python.org release-tools package: github.com Talk Python's HTMX + Django course: talkpython.fm/htmx-django Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Talk Python Training

So, you've got this amazing machine learning model you created. And you want to share it and let your colleagues and users experiment with it on the web. How do you get started? Learning Flask or Django? Great frameworks, but you might consider Gradio which is a rapid development UI framework for ML models. On this episode, we have Freddy Boulton, to introduce us all to Gradio. Links from the show Freddy on Twitter: @freddy_alfonso_ Gradio: gradio.app Use as API Example: huggingface.co Components: gradio.app Svelte: svelte.dev Flutter UI/Code structure: docs.flutter.dev XKCD Matplotlib Theme: matplotlib.org Gradio XKCD Full Theme: huggingface.co PrivateGPT: ai.meta.com Langchain: docs.langchain.com pipdeptree: pypi.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

We write tests to show us when there are problems with our code. But what if there are intermittent problems with the tests themselves? That can be big hassle. In this episode, we have Gregory Kapfhammer and Owain Parry on the show to share their research and advice for taming flaky tests. Links from the show Gregory Kapfhammer: gregorykapfhammer.com Owain Parry on Twitter: @oparry9 Radon: pypi.org pytest-xdist: github.com awesome-pytest: github.com Tenacity: readthedocs.io Stamina: github.com Flaky Test Management: docs.cypress.io Flaky Test Management (Datadog): datadoghq.com Flaky Test Management (Spotify): engineering.atspotify.com Flaky Test Management (Google): testing.googleblog.com Detecting Test Pollution: github.com Surveying the developer experience of flaky tests paper: www.gregorykapfhammer.com Build Kite CI/CD: buildkite.com Flake It: Finding and Fixing Flaky Test Cases: github.com Unflakable: unflakable.com CircleCI Test Detection: circleci.com Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Have you heard of Django? It's this little web framework that, well, kicked off much of Python's significance in the web space back in 2005. And that makes Django officially an adult. That's right, Django is now 18. And Django continues to lead the way on how community should be done for individual projects such as web frameworks. We have Carlton Gibson and Will Vincent back on the show this episode to discuss a bit of the Django history, Django trends in 2023, a little HTMX + Django, and lots more. Links from the show Guests Will Vincent: wsvincent.com Carlton Gibson: @[email protected] Button.dev: btn.dev Learn Django: learndjango.com Django News: django-news.com Yak-Shaving to Where the Puck is Going to Be Talk: youtube.com Open Source for the Long Haul: fosstodon.org Django 4.2: docs.djangoproject.com Django 5: docs.djangoproject.com Environs: github.com Neapolitan: github.com Django Template Paritals: github.com Jinja Partials: github.com Django Chat Podcast: djangochat.com Locality of Behavior Essay: htmx.org HTMX: htmx.org You're Fullstack Now Meme: twitter.com Deployment Checklist: docs.djangoproject.com Django-HTMX: github.com Django @Instagram DjangoChat: djangochat.com Talk Python HTMX Course: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

One of the most exciting initiatives in the Python space these days is pyscript which enables Python running natively in your browser. With consistent support from the folks at Anaconda, this project has been making solid strides since its initial release. On this episode we catch up with Fabio Pliger and Nicholas Tollervey to see where they are with the pyscript project. Links from the show Guests and Host Links Nicholas Tollervey: @[email protected] Fabio Pliger: @b_smoke Michael Kennedy: @[email protected] pyscript: pyscript.net pyscript on Github: github.com Tic Tac Toe Example App: pyscriptapps.com PyperCard: github.com MicroPython: micropython.org pyscript core: pyscript.net Nich's PyScript gets Python anywhere there's a browser video: youtube.com HTMX: htmx.org Birth and Death of JavaScript: destroyallsoftware.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Sentry Error Tracing, Code TALKPYTHON Talk Python Training

Understanding how your Python application is using memory can be tough. First, Python has it's own layer of reused memory (arenas, pools, and blocks) to help it be more efficient. And many important Python packages are built in natively compiled languages like C and Rust often times making that section of your memory opaque. But with Memray, you can way deeper insight into your memory usage. We have Pablo Galindo Salgado and Matt Wozniski back on the show to dive into Memray, the sister project to their pystack one we recently covered. Links from the show Pablo Galindo Salgado: @pyblogsal Matt Wozniski: github.com pytest-memray: github.com PEP 669 – Low Impact Monitoring for CPython: peps.python.org Memray discussions: github.com Mandlebrot Flamegraph example: bloomberg.github.io Python allocators: bloomberg.github.io Profiling in Python: docs.python.org PEP 693 – Python 3.12 Release Schedule: peps.python.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm influxdata Talk Python Training

If you want to share your data science results as interactive web apps, you could learn Flask or Django and a bunch of other web technologies. Or, you could pick up one of the powerful frameworks for deploying data science specifically. And if you're searching through that space, you've likely hear of Shiny -- but that's just for the R side of data science, right? Not any longer. Joe Cheng is here to introduce us to the recently released Shiny for Python. And it looks like a very solid new framework on the block. Links from the show Joe on Twitter: @jcheng Shiny: shiny.posit.co Shiny for Python code: github.com Discord community for Shiny: discord.gg Reactive programming inside Shiny: shiny.posit.co Shiny Gallery: shiny.posit.co Examples: shiny.posit.co Orbital mechanics in Shiny: shiny.posit.co Wordle in Shiny: shiny.posit.co Keynote introducing Shiny for Python: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors influxdata GlareDB Talk Python Training

Python is used for a wide variety of software projects. One area it's really gained a huge amount of momentum is in the computational space (including data science). On this episode we welcome back Allen Downey to dive into a particular slice of this space: simulation problems and Python in Physics and Engineering in general. Links from the show Allen’s web page: allendowney.com Allen’s blog (Probably Overthinking It): allendowney.com/blog Allen on Twitter: @allendowney Allen on Mastodon: @[email protected] Modeling and Simulation in Python book: allendowney.github.io Programming as a Way of Thinking: blogs.scientificamerican.com Think Python book: greenteapress.com Think OS book: greenteapress.com Pint package: pint.readthedocs.io Free version of the book and Jupyter notebooks: allendowney.github.io Published version: nostarch.com Elm programming language: elm-lang.org SymPy examples: docs.sympy.org Guinness World Record won for bungee 'dunk' into cup of tea: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors influxdata Pybites PDM Talk Python Training

Getting started in Python is pretty easy. There's even a t-shirt that jokes about it: I learned Python, it was a good weekend. But to go from know how to create variables and writing loops, to building amazing things like FastAPI or Instagram, well there is this little gap between those two things. On this episode we welcome Eric Matthes to the show. He has thought a lot about teaching Python and comes to share his 10 tips for going from Python beginner to expert. Links from the show Eric on LinkedIn: linkedin.com Mostly Python Newsletter: mostlypython.substack.com Python Crash Course Book: nostarch.com Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Regardless of which side of Python, software developer or data scientist, you sit on, you surely know that data scientists and software devs seem to have different styles and priorities. But why? And what are the benefits as well as the pitfalls of this separation. That's the topic of conversation with our guest, Dr. Jodie Burchell, data science developer advocate at JetBrains. Links from the show Jodie on Twitter: @t_redactyl Jodie's PyCon Talk: youtube.com Deep Learning with Python book: manning.com Keras: keras.io scikit-learn: scikit-learn.org Matplotlib: matplotlib.org XKCD Matplotlib: matplotlib.org Pandas: pandas.pydata.org Polars: pola.rs Polars on Talk Python: talkpython.fm Jupyter: jupyter.org Ponder: ponder.io Dask: dask.org Explosion AI's Prodigy discount code: get a personal license for 25% off using the discount code TALKPYTHON. Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm Prodigy Talk Python Training

When you think of Netflix (as a technology company), you probably imagine them as cloud innovators. They were one of the first companies to go all-in on a massive scale for cloud computing as well as throwing that pesky chaos monkey into the servers. But they have become a hive of amazing Python activity. From their CDN, demand predictions and failover, security, machine learning, executable notebooks and lots more, the Python at play is super interesting. On this episode, we have Zoran Simic and Amjith Ramanujam on the show to give us this rare inside look. Links from the show Zoran on Twitter: @zsimic Amjith on Mastodon: @[email protected] Python at Netflix blog post: netflixtechblog.com pdb++: github.com Pickley: github.com Pickley vs. pipx: github.com DB CLI: dbcli.com Learn you a Haskell: learnyouahaskell.com How Much of the Internet's Bandwidth Does Netflix Use?: makeuseof.com PtPython: github.com BPython: bpython-interpreter.org Flask REST-Plus: readthedocs.io RustUp: rustup.rs Rye: github.com PEP 711 - Distributing Python Binaries episode: talkpython.fm Portable Python: github.com Python Build Standalone: github.com How Netflix does failovers in 7 minutes flat: opensource.com Security Monkey: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm influxdata Talk Python Training

When you use a SQL database like Postgres, you have to understand the subtleties of isolation levels from "read committed" to "serializable." And distributed databases like MongoDB offer a range of consistency levels, from "eventually consistent" to "linearizable" and many options in between. Plus, it's easy enough to confuse "isolation" with "consistency!" We have A. Jesse Jiryu Davis from MongoDB back on the podcast to break it all down for us. Links from the show Jesse on Twitter: @jessejiryudavis Jesse on Mastodon: @[email protected] Files related to PyCon Talk: github.com Consistency and Isolation for Python Programmers blog post: emptysqua.re Consistency Models and Visuals: jepsen.io MongoDB Replication: mongodb.com MongoDB Transactions: mongodb.com Jesse's PyCon Talk: youtube.com Database Types: mongodb.com MongoDB Labs: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON influxdata Talk Python Training

Here's the situation. You have a Python app that is locked or even has completely crashed and all you're left with is a core dump on the server. Now what? It's time for PyStack! You can capture a view of your app as if you've set a breakpoint and even view the callstack and locals across language calls (for example from Python to C++ and back). We have the maintainers, Pablo Galindo Salgado and Matt Wozniski, here to dive into PyStack. You'll definitely want to have this tool in your toolbox. Links from the show Pablo Galindo Salgado: @pyblogsal Matt Wozniski: github.com pystack: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON RedHat Talk Python Training

Think about the different APIs and databases your application works with. Every one of them requires either an API key or a database connection string that itself contains a password. How do you let your application access this sensitive information without storing it in source code or putting in other compromising locations? We have Glyph Lefkowitz on the show to share his security fable as well as just good advice for keeping secrets out of Python code. Links from the show Glyph on Mastodon: @[email protected] ShhGit: github.com Encrust: github.com GitHub Security Alerts: github.com CIA Triad: fortinet.com pinpal: github.com XKCD Authorization: xkcd.com Tokenring: github.com AWS Vault: github.com Gimme-AWS-creds: github.com Secrets in GitHub Actions: github.com Python Client for HashiCorp Vault: python-hvac.org Pomodouroboros app: github.com DateType: pypi.org Haveibeenpwned: haveibeenpwned.com PEP 541: peps.python.org Glyph's security talk at PyCon: us.pycon.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm RedHat Talk Python Training

Large language models and chat-based AIs are kind of mind blowing at the moment. Many of us are playing with them for working on code or just as a fun alternative to search. But others of us are building applications with AI at the core. And when doing that, the slightly unpredictable nature and probabilistic nature of LLMs make writing and testing Python code very tricky. Enter promptimize from Maxime Beauchemin and Preset. It's a framework for non-deterministic testing of LLMs inside our applications. Let's dive inside the AIs with Max. Links from the show Max on Twitter: @mistercrunch Promptimize: github.com Introducing Promptimize ("the blog post"): preset.io Preset: preset.io Apache Superset: Modern Data Exploration Platform episode: talkpython.fm ChatGPT: chat.openai.com LeMUR: assemblyai.com Microsoft Security Copilot: blogs.microsoft.com AutoGPT: github.com Midjourney: midjourney.com Midjourney generated pytest tips thumbnail: talkpython.fm Midjourney generated radio astronomy thumbnail: talkpython.fm Prompt engineering: learnprompting.org Michael's ChatGPT result for scraping Talk Python episodes: github.com Apache Airflow: github.com Apache Superset: github.com Tay AI Goes Bad: theverge.com LangChain: github.com LangChain Cookbook: github.com Promptimize Python Examples: github.com TLDR AI: tldr.tech AI Tool List: futuretools.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm RedHat Talk Python Training

If you're looking for fun data sets for learning, for teaching, maybe a conference talk, or even if you're just really into them, sports offers up a continuous stream of rich data that many people can relate to. Yet, accessing that data can be tricky. Sometimes it's locked away in obscure file formats. Other times, the data exists but without a clear API to access it. On this episode, we talk about PySport - something of an awesome list of a wide range of libraries (mostly but not all Python) for accessing a wide variety of sports data from the NFL, NBA, F1, and more. We have Koen Vossen, maintainer of PySport to talk through some of the more popular projects. Links from the show Koen on Twitter: @mr_le_fox PySport on Twitter: @PySportOrg Calling R from Python: medium.com DuckDB: duckdb.org PySport Playground: playground.pysport.org NFLVerse: github.com NBA Stats: nba.com Sports Databases: opensource.pysport.org Data sets: opensource.pysport.org Visualizations: opensource.pysport.org I/O: opensource.pysport.org Models: opensource.pysport.org Scrapers/APIs: opensource.pysport.org Fast F1: docs.fastf1.dev Fast F1 graphics: docs.fastf1.dev Pysport Intro: pysport.org New Talk Python Training Apps: talkpython.fm Michael's blog post about the apps: mkennedy.codes Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors PyCharm influxdata Talk Python Training

The release of Pydantic 2.0, its partial rewrite in Rust, and its refactoring into Pydantic core and top-level Pydantic in Python is big news. In fact, the alpha of Pydantic 2 was just released. Of course, these changes will have potentially wide ranging (and positive!) effects on libraries that are built upon Pydantic such as FastAPI, Beanie, and others. That's why this chance I had to catch up with Samuel Colvin from Pydantic and Sebastián Ramírez from FastAPI together, live from PyCon 2023. It's a super fun and wide ranging interview I'm sure you'll enjoy. Plus, there is a bit of an easter egg in the middle. Links from the show Sebastián Ramírez: @tiangolo Samuel Colvin: @samuel_colvin FastAPI: fastapi.tiangolo.com Pydantic: pydantic.dev Pydantic V2 Pre Release: pydantic.dev Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON influxdata Talk Python Training

At PyCon 2023, there was a section of the expo floor dedicated to new Python-based companies called Startup Row. I wanted to bring their stories and the experience of talking with these new startups to you. So in this episode, we'll talk with founders from these companies for 5 to 10 minutes each. Links from the show Ponder: ponder.io generally intelligent: generallyintelligent.com Wherobots: wherobots.ai Neptyne: neptyne.com Nixtla: nixtla.io Predibase: predibase.com Pynecone: pynecone.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Did you make this year's PyCon event in the US? There was a lot of excitement this time around in Salt Lake City. In this episode I'll bring you a bunch of experiences we had this year. It starts where frequent guest Jay Miller turns the tables and interviews me at the Microsoft booth on the expo hall floor in front of a live audience. Then you'll hear from Mario Munoz, Nick Muoh, Chris Williams, Ray McLendon, and Sean Tibor about their time at the conference. Links from the show Jay Miller: @kjaymiller Mario Munoz: @[email protected] Ray McLendon: linkedin.com Nick Muoh: @[email protected] Sean Tibor: @[email protected] Chris Williams: @[email protected] Python Community News: youtube.com The Birth & Death of JavaScript: destroyallsoftware.com Talk Python episode with Rivers Cuomo: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors airplane Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

What if we distributed CPython, the runtime, in the same way we distributed Python packages - as prebuilt binary wheels that only need to be downloaded and unzipped to run? For starters, that would mean we could ship and deploy Python apps without worrying whether Python itself is available or up-to-date on the platform. Nathaniel Smith has just proposed a PEP to do just that, PEP 711. And we'll dive into that with him next. Links from the show Nathaniel: @[email protected] [announce] Pybi and Posy: discuss.python.org PEP 711: peps.python.org Py2App: readthedocs.io PyInstaller: pyinstaller.org py-spy: github.com Anthropic: anthropic.com Trio: github.com Trio on Talk Python: talkpython.fm Zip Documentary: The Dark History of Zip Files: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

What advice would you give someone just getting into Python? What did you learn over time through hard work and a few tears that would have really helped you? It's a fun game to play and we have Jason McDonald on the podcast to give us his take. Enjoy! Links from the show Jason C. McDonald: @[email protected] Dead Simple Python: nostarch.com Coroutines and Tasks: docs.python.org Duck Typing: wikipedia.org Static Duck Typing in Python with Protocols: daan.fyi PEP 709: peps.python.org PEP 289: peps.python.org Python Packaging Strategy Discussion - Part 1: discuss.python.org Branch-detective: github.com Hypothesis: readthedocs.io Pydantic v2 announcement: pydantic.dev Michael's venv alias: digitaloceanspaces.com Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

AI has taken the world by storm. It's gone from near zero to amazing in just a few years. We have ChatGPT, we have Stable Diffusion. But what about Jupyter Notebooks and pandas? In this episode, we meet Justin Waugh, the creator of Sketch. Sketch adds the ability to have conversational AI interactions about your pandas data frames (code and data). It's pretty powerful and I know you'll enjoy the conversation. Links from the show Sketch: github.com Lambdapromp: github.com Python Bytes 320 - Coverage of Sketch: pythonbytes.fm ChatGPT: chat.openai.com Midjourney: midjourney.com Github Copilot: github.com GitHub Copilot Litigation site: githubcopilotlitigation.com Attention is All You Need paper: research.google.com Live Colab Demo: colab.research.google.com AI Panda from Midjourney: digitaloceanspaces.com Ray: pypi.org Apache Arrow: arrow.apache.org Python Web Apps that Fly with CDNs Course: talkpython.fm Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Brilliant 2023 Talk Python Training

We all know that privacy regulations are getting more strict. And that many of our users no longer believe that "privacy is dead". But for even medium-sized organizations, actually tracking how we are using personal info in our myriad of applications and services is very tricky and error prone. On this episode, we have Thomas La Piana from the Fides project to discuss privacy in our applications and how Fides can enforce and track privacy requirements in your Python apps. Links from the show California Consumer Privacy Act (CCPA): oag.ca.gov 30 Biggest GDPR Fines So Far: tessian.com Website fined for Google Fonts: theregister.com Fides on Github: github.com Fides: ethyca.com Bunny.net Fonts: fonts.bunny.net DBT: getdbt.com eBFP Kernel tools: ebpf.io nox: nox.thea.codes rich-click: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Microsoft Founders Hub 2023 Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

In recent years, there has been a lot of experimenting how we work with dependencies and external libraries for our Python code. There is pip, pip-tools, Poetry, pdm, pyenv, pipenv, Hatch and others workflows. We dove into this deeply back on episode 406: Reimagining Python's Packaging Workflows. We're back with Ofek Lev to take a deeper look at Hatch. Links from the show Hatch: hatch.pypa.io Ofek on Twitter: @Ofekmeister Mamba: github.com Hatch env management: hatch.pypa.io Packaging a Python project tutorial: packaging.python.org Customize project generation: hatch.pypa.io Textual: textualize.io Ruff on Talk Python: talkpython.fm RustUp: rustup.rs Conda: docs.conda.io import antigravity: xkcd.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Microsoft Founders Hub 2023 Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

If you're like most people, the simplicity and easy of getting started is a big part of pytest's appeal. But beneath that simplicity, there is a lot of power and depth. We have Brian Okken on this episode to dive into his latest pytest tips and tricks for beginners and power users. Links from the show pytest tips and tricks article: pythontest.com Getting started with pytest Course: training.talkpython.fm pytest book: pythontest.com Python Bytes podcast: pythonbytes.fm Brian on Mastodon: @[email protected] Hypothesis: readthedocs.io Hypothesis: Reproducability: readthedocs.io Get More Done with the DRY Principle: zapier.com "The Key" Keyboard: stackoverflow.blog pytest plugins: docs.pytest.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Microsoft Founders Hub 2023 Brilliant 2023 Talk Python Training

The great power of Python is its over 400,000 packages on PyPI to serve as building blocks for your app. How do you get those needed packages on to your dev machine and managed within your project? What about production and QA servers? I don't even know where to start if you're shipping built software to non-dev end users. There are many variations on how this works today. And where we should go from here has become a hot topic of discussion. So today, that's the topic for Talk Python. I have a great panel of guests: Steve Dower, Pradyun Gedam, Ofek Lev, and Paul Moore. Links from the show Python Packaging Strategy Discussion - Part 1: discuss.python.org Thoughts on the Python packaging ecosystem: pradyunsg.me Python Packaging Authority: pypa.io Hatch: hatch.pypa.io Pyscript: pyscript.net Dark Matter Developers: The Unseen 99%: hanselman.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Cox Automotive Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

So you know about dependencies and testing, right? If you're talking to a DB in your app, you have to decide how to approach that with your tests. There are lots of solid options you might pick and they vary by goals. Do you mock out the DB layer for isolation or do you use a test DB to make it as real as possible? Do you just punt and use the real DB for expediency? What if your dependency was a huge array of radio telescopes and a rack of hundreds of bespoke servers? That's the challenge on deck today were we discuss testing radio astronomy with pytest with our guest James Smith. He's a Digital Signal Processing engineer at the South African Radio Astronomy Observatory and has some great stories and tips to share. Links from the show GPU-based correlator for MeerKAT: github.com Meerkat: sarao.ac.za SARAO: sarao.ac.za Skarab server: peralex.com pycuda: documen.tician.de Commercial Telescopes: telescope.com PyLaTeX: github.com Linearity Test Code: talkpython.fm Correlator Context: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Taipy Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

Clean code is one of those aspects of your programming career that's easy to put on the back burner (sometimes by management more than yourself). But it's important in the short term for writing more debuggable and readable code. And important in the long run for avoiding having your program take on the dreaded "legacy code" moniker. We're fortunate to have Bob Belderbos back on the show. He's been thinking and writing about clean code and Python a lot lately and we'll dive into a bunch of tips you can use right away to make your code cleaner. Links from the show Bob on Mastodon: @[email protected] PyBites: pybit.es Tips for clean code in Python article: pybit.es Refactoring book: pybitesbooks.com Final type: docs.python.org Sentinels pattern: python-patterns.guide Black formater: pypi.org Guarding clauses: medium.com ChatGPT: chat.openai.com Git Precommit: pre-commit.com #100DaysOfCode in Python course: training.talkpython.fm #100DaysOfWeb in Python course: training.talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Taipy Brilliant 2023 Talk Python Training

Imagine a world with free and unlimited clean energy. That's the musings of a great science fiction story. But nuclear fusion (the kind that powers the sun) has always been close at hand, we see the sun every day, and yet impossibly far away as a technology. We took a major step towards this becoming a reality with the folks at the Lawrence Livermore National Labratory in the US achieved "ignition" where they got significantly more energy out than they put in. And Python played a major role in this research and experiment. We have Jay Salmonson here to give us a look at the science and the Python code of this discovery. Links from the show Jay on Mastodon: hachyderm.io/@jdsalmonson Jay on Twitter: @JaySalmonson Official Announcement: lasers.llnl.gov QnD Package: github.com PlasmaPy: plasmapy.org ML in Fusion: llnl.gov National Ignition Facility Achieves Ignition in Historic Nuclear Fusion Experiment: newenergytimes.net Video demonstrating the fusion lab: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Taipy User Interviews Talk Python Training

When you think about processing tabular data in Python, what library comes to mind? Pandas, I'd guess. But there are other libraries out there and Polars is one of the more exciting new ones. It's built in Rust, embraces parallelism, and can be 10-20x faster than Pandas out of the box. We have Polars' creator, Ritchie Vink here to give us a look at this exciting new data frame library. Links from the show Ritchie on Mastodon: @[email protected] Ritchie on Twitter: @RitchieVink Ritchie's website: ritchievink.com Polars: pola.rs Apache Arrow: arrow.apache.org Polars Benchmarks: pola.rs Coming from Pandas Guide: github.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Taipy User Interviews Talk Python Training

At some point, you've probably migrated an app from one framework or major runtime version to another. For example, Django to Flask, Python 2 to Python 3, or even Angular to Vue.js. This can be a big challenge. If you had 100s of active devs and millions of lines of code, it's a huge challenge. We have Ben Bariteau from Yelp here to recount their story moving 3.8M lines of code from Python 2 to Python 3. But this is not just a 2-to-3 story. It has many lessons on how to migrate code in many situations. There are plenty of gems to take from his experience. Links from the show Ben on Twitter: @benbariteau Ben's Talk at PyCon 2022: youtube.com python-modernize: github.com python-future: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Cox Automotive User Interviews Talk Python Training

Our code quality tools (linters, test frameworks, and others) play an important role in keeping our code error free and conforming to the rules our teams have chosen. But when these tools become sluggish and slow down development, we often avoid running them or even turn them off. On this episode, we have Charlie Marsh here to introduce Ruff, a fast Python linter, written in Rust. To give you a sense of what he means with fast, common Python linters can take 30-60 seconds to lint the CPython codebase. Ruff takes 300 milliseconds. I ran it on the 20,000 lines of Python code for our courses web app at Talk Python Training, and it was instantaneous. It's the kind of tool that can change how you work. I hope you're excited to learn more about it. Links from the show Charlie on Twitter: @charliermarsh Charlie on Mastodon: @charliermarsh@hachyderm Ruff: github.com PyCharm Developer Advocate Job: jetbrains.com/careers Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy Sponsors Cox Automotive User Interviews Talk Python Training

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