Talk Python To Me - Python Conversations For Passionate Developers

Data validation and conversion is one of the truly tricky part of getting external data into your app. This might come from a REST API, a file on disk, or somewhere else. This includes checking for required fields, correct data types, converting from compatible types (for example, strings to numbers), and much more. Pydantic is one of the best ways to do this in modern Python using dataclass-like constructs and type annotations to make it all seamless and automatic. We welcome Samuel Colvin, creator of Pydantic, to the show. We'll dive into the history of Pydantic and it's many uses and benefits. Links from the show Samuel on Twitter: @samuel_colvin pydantic: pydantic-docs.helpmanual.io Contributing / help wanted @ pydantic: github.com python-devtools package: python-devtools.helpmanual.io IMPORTANT: PEP 563, PEP 649 and the future of pydantic #2678 GitHub issue on Typing: github.com YouTube live stream video: youtube.com Sponsors 45Drives Talk Python Training

How do you build Python applications that can handling literally billions of requests. I has certainly been done to great success with places like YouTube (handling 1M requests / sec) and Instagram as well as internal pricing APIs at places like PayPal and other banks. While Python can be fast at some operations and slow at others, it's generally not so much about language raw performance as it is about building an architecture for this scale. That's why it's great to have Julian Danjou on the show today. We'll dive into his book "The Hacker's Guide to Scaling Python" as well as some of his performance work he's doing over at Datadog. Links from the show Julian on Twitter: @juldanjou Scaling Python Book: scaling-python.com DD Trace production profiling code: github.com Futurist package: pypi.org Tenacity package: tenacity.readthedocs.io Cotyledon package: cotyledon.readthedocs.io Locust.io Load Testing: locust.io Datadog: datadoghq.com daiquiri package: daiquiri.readthedocs.io YouTube Live Stream Vide

These days Git is synonymous with source control itself. Rare are the current debates of whether to use git vs SVN vs some fossil like SourceSafe vs you name it. But do you know how Git works? What about it's internals? I'm sure you've seen a .git folder in your project's root. But to most folks, it's a black box. In this episode, you'll meet Rob Richardson. He's going pop the lid on that black box as we dive into git internals and the .git folder, among other things source control. Links from the show Rob's Site: robrich.org Rob on Twitter: @rob_rich YouTube Live Stream Recording: youtube.com Talk at PWC: loudswarm.com Git Explorer App: github.com Pre-commit framework: pre-commit.com .gitignore project: github.com git-hooks project: npmjs.com Git Source Control: git-scm.com SVN: subversion.apache.org Oh My Posh Shell: ohmyposh.dev Oh My ZSH Shell: ohmyz.sh Oh !*#! Git Site: oh*!#!git.com Work-safe version: dangitgit.com Sponsors Talk Python Training

The tables have turned and this time I'm the guest and you all are the hosts. I get a ton of questions over email and twitter asking me about my thoughts on various trends, tools, and behind the scenes questions around Talk Python. So I've enlisted two listeners who are up for hosting a conversation and taking questions from you all. Thank you to Patrik Hlobil and Kim van Wyk who guest host this episode where I answer a bunch of audience questions in this ask my anything. Links from the show Patrik on Twitter: @hlobilpatrik pandas-bokeh (Patrik's project): github.com Kim on Twitter: @kim_vanwyk Kim's website: kimvanwyk.co.za YouTube Live Stream Recording: youtube.com Future Talk Python Live Streams: talkpython.fm/stream/live Highlighted packages Click: palletsprojects.com PyVISA: github.com pySerial: github.com Sponsors OutSystems Platform Talk Python Training

Machine learning and data science are full of best practices and important workflows. Can we extrapolate these to our broader lives? Eugene Yan and I give it a shot on this slightly more philosophical episode of Talk Python To Me. The seven lessons: 1. Data cleaning: Assess what you consume 2. Low vs. high signal data: Seek to disconfirm and update 3. Explore-Exploit: Balance for greater long-term reward 4. Transfer Learning: Books and papers are cheat codes 5. Iterations: Find reps you can tolerate, and iterate fast 6. Overfitting: Focus on intuition and keep learning 7. Ensembling: Diversity is strength Links from the show Eugene Yan: @eugeneyan What Machine Learning Can Teach Us About Life - 7 Lessons article: eugeneyan.com Maker's schedule vs. manager's schedule: paulgraham.com Naval Podcast: overcast.fm How to Write Better with The Why, What, How Framework https://eugeneyan.com/writing/writing-docs-why-what-how/ Resources mentioned towards the end of the podcast: eugeneyan.com/resources N

Docker is one of the core elements of developing Python applications in consistent ways as well as running them across different hardware universally. On this episode, you'll meet Peter McKee from Docker. He's here to catch us up on what's happening in the Docker universe for Python developers. Links from the show YouTube Live Stream: youtube.com Peter on Twitter: @pmckee Docker: docker.com Docker Roadmap: github.com It works on my machine certification: codinghorror.com Test Containers Package: github.com Sponsors Datadog Talk Python Training

Python has changed a lot since its inception 30 years ago. On this episode, you'll meet Paul Everitt and Barry Warsaw. They have both been involved with Python since the very first Python conference (called SPAM1 even). We discuss how it's changed but also how so many of the pressures and ideas from the very early days are still playing out in 2021. I'm sure you'll enjoy all the stories and reminiscing. Links from the show Paul on Twitter: @paulweveritt Barry on Twitter: @pumpichank Episode live stream: youtube.com A Python Developer Explores Apple's M1 video: youtube.com Let's Build a Fast, Modern Python API with FastAPI webcast: youtube.com Python.org 1997: python.org Python is eating the world: How one developer's side project became the hottest programming language on the planet: techrepublic.com Some of Barry's music: soundcloud.com Barry’s early history of Python slides from BayPiggies: slides.com Backstory and liner notes for the Zen of Python song: wefearchange.org Zen of Python song: youtube.com

When we talk about scaling software threading and async get all the buzz. And while they are powerful, using asynchronous queues can often be much more effective. You might think this means creating a Celery server, maybe running RabbitMQ or Redis as well. What if you wanted this async ability and many more message exchange patterns like pub/sub. But you wanted to do zero of that server work? Then you should check out ZeroMQ. ZeroMQ is to queuing what Flask is to web apps. A powerful and simple framework for you to build just what you need. You're almost certain to learn some new networking patterns and capabilities in this episode with our guest Min Ragan-Kelley to discuss using ZeroMQ from Python as well as how ZeroMQ is central to the internals of Jupyter Notebooks. Links from the show Min on Twitter: @minrk Simula Lab: simula.no Talk Python Binder episode: talkpython.fm/256 The ZeroMQ Guide: zguide.zeromq.org Binder: mybinder.org IPython for parallel computing: ipyparallel.readthedocs.io Messag

People often ask me how they can find a Python community to be part of. Maybe discussion forum or slack channel. This week, we look at one of the most active communities in Python Discord. It's Python, on a discord server. But it's more than that too. You'll meet Leon Sandøy who, along with a team of folks, runs Python Discord. Links from the show Leon on Twitter: @lemonsaurus_rex Leon on the web: lemonsaur.us Leon on GitHub: github.com Python Discord: pythondiscord.com Python Discord's YouTube: youtube.com Python Discord on Twitter: @pythondiscord Python Discord on GitHub: github.com PEP 8 Song: youtube.com YouTube Live Stream version of this show: youtube.com Sponsors Linode Talk Python Training

The relatively recent introduction of async and await as keywords in Python have spawned a whole area of high performance, highly scalable frameworks and supporting libraries. One such library that has great async building blocks is Omnilib. On this episode, you'll meet John Reese. John is the creator of Omnilib, which includes packages such as aioitertools, aiomultiprocess, and aiosqlite. Join us as we async all the things. Links from the show Omnilib libraries and project: omnilib.dev awesome-asyncio: github.com unsync: asherman.io Live Youtube Stream: youtube.com Charities Power On: poweronlgbt.org The Trevor Project: thetrevorproject.org Sponsors Linode Talk Python Training

If you are involved in science or use computational tools in your work, you should be using code to solve your problem. On this episode, we have Dr. Becky Smethurst who's an astrophysicist at Oxford University. She uses Python to explore galaxies and black holes. Learn how she's using Python to make new discoveries at the cutting edge of research and dive into a couple of her YouTube videos aimed at spreading scientific truth in an entertaining wrapper. Links from the show Dr. Becky on Twitter: @drbecky_ Dr. Becky's YouTube channel: youtube.com 5 ways I use code as an astrophysicist video: youtube.com Astrophysicist reacts to funny space MEMES video: youtube.com A day in the life of an Oxford University Astrophysicist: youtube.com Book: Space: 10 things you should know: amazon.com SpaceMemes Apple maps: image Otter space: image Eclipses: image Steals a cow: image Black holes: image YouTube live stream: youtube.com Sponsors Linode CloudEnv Talk Python Training

I'm sure you're familiar with data science. But what about data engineering? Are these the same or how are they related? Data engineering is dedicated to overcoming data-processing bottlenecks, data cleanup, data flow and data-handling problems for applications that utilize lots of data. On this episode, we welcome back Tobias Macey to give us the 30,000 ft view of the data engineering landscape in 2021. Links from the show Live Stream Recordings: YouTube: youtube.com Tobias Macey: boundlessnotions.com Podcast.__init__: pythonpodcast.com Data Engineering podcast: dataengineeringpodcast.com Designing Data-Intensive Applications Book: amazon.com wally: github.com lakeFS: lakefs.io A Beginner’s Guide to Data Engineering: medium.com Apache Airflow: airflow.apache.org Dagster: dagster.io Prefect: prefect.io #68 Crossing the streams with Podcast.__init__: talkpython.fm/68 dbt: getdbt.com Great Expectations: github.com Dask: dask.org Meltano: meltano.com Languages trends on StackOverflow: insights.stack

Have you been learning Django and now want to get your site online? Not sure the best way to host it or the trade offs between the various options? Maybe you want to make sure your Django site is secure. On this episode, I'm joined by two Django experts Will Vincent and Carlton Gibson to talk about deploying and running Django in production along with recent updates in Django 3.2 and beyond. Links from the show Guests Will Vincent: wsvincent.com Carlton Gibson: @carltongibson Watch the live stream: youtube.com Give me back my monolith: craigkerstiens.com Carlton’s Button hosting platform: btn.dev Django Software Foundation: djangoproject.com Django News newsletter: django-news.com Deployment Checklist: djangoproject.com Environs 3rd party package for environment variables: github.com Django Static Files & Templates: learndjango.com Learn Django: LearnDjango.com Configuring uWSGI for Production Deployment @ Bloomberg: techatbloomberg.com Sponsors Square Linode Talk Python Training

You've heard that software developers and startups go hand-in-hand. But what about data scientists? Of course they! But how do you turn your data science skill set into a data science business skill set? What are some of the areas ripe for launching such a business into? On this episode, I welcome back 4 prior guests who have all walked their own version of this path and are currently running successful Python-based Data Science startups: * Ines Montani from Explosion AI * Matthew Rocklin from Coiled * Jonathon Morgan from Yonder AI * William Stein from Cocalc Links from the show Ines Montani Twitter: @_inesmontani Explosion AI: explosion.ai Matthew Rocklin Twitter: @mrocklin Coiled: coiled.io Jobs @ Coiled: jobs.lever.co/coiled Jonathon Morgan Twitter: @jonathonmorgan Yonder AI: yonder-ai.com William Stein Twitter: @wstein389 CoCalc: cocalc.com Talk Python Live Streams: talkpython.fm/youtube Sentry Promo Code: TALKPYTHON2021 Sponsors Sentry Error Monitoring, Code TALKPYTHON Linode Talk Pyth

In this episode, we'll be discussing two powerful tools for data reporting and exploration: Datasette and Dogsheep. Datasette helps people take data of any shape or size, analyze and explore it, and publish it as an interactive website and accompanying API. Dogsheep is a collection of tools for personal analytics using SQLite and Datasette. Imagine a unified search engine for everything personal in your life such as twitter, photos, google docs, todoist, goodreads, and more, all in once place and outside of cloud companies. On this episode we talk with Simon Willison who created both of these projects. He's also one of the co-creators of Django and we'll discuss some early Django history! Links from the show Datasette: datasette.io Dogsheep: dogsheep.github.io Datasheet newsletter: datasette.substack.com Video: Build your own data warehouse for personal analytics with SQLite and Datasette: youtube.com Examples List: github.com Personal data warehouses: github.com Global power plants: datasettes.co

Are you building or running an internal machine learning team? How about looking for a new ML position? On this episode, I talk with Chip Huyen from Snorkel AI about building ML teams, finding ML positions, and teach ML at Stanford. Links from the show Chip on Twitter: @chipro Snorkel AI: snorkel.ai Chip's Book Preview: twitter.com handcalcs project: github.com IBM Buzzword Bingo: youtube.com Sponsors Datadog Linode Talk Python Training

2020 will be one for the history books, won't it? I've put together a great group to look back on 2020 - from the Python perspective. Join me along with Cecil Phillip, Ines Montani, Jay Miller, Paul Everitt, Reuven Lerner, Matt Harrison, and Brian Okken for a light-hearted and fun look back on the major Python events of 2020. Links from the show Video version of this episode: youtube.com Guests Cecil Phillip: @cecilphillip Ines Montani: @_inesmontani Jay Miller: @kjaymiller Paul Everitt: @paulweveritt Reuven Lerner: @reuvenmlerner Matt Harrison: @__mharrison__ Brian Okken: @brianokken Sponsors Talk Python Training

Quick: Name the 3 most advanced engineering organizations you can think of? Maybe an aerospace company such as SpaceX or Boeing come to mind. Maybe you thought of CERN and the LHC. But in terms of bespoke engineering capabilities, you should certainly put the F1 racing teams on your list. These organizations appear as 20-30 people on a race day shown on TV. But in fact, the number of people back at the home base doing the engineering work can be over 500 employees. Almost every tiny part you see on these cars as well as the tools to maintain them are custom-built. The engineering problems solved are immense. Would it surprise you to know that Python is playing a major role here? On this episode, you'll meet Joe Borg who help pioneer Python's adoption at several F1 teams. Links from the show Joe's website: josephb.org Joe on Twitter: @joedborg Racing Point F1 team: racingpointf1.com Scuderia Alpha Tauri F1 team: scuderiaalphatauri.com MicroK8s: microk8s.io Charmed Kubernetes: ubuntu.com/kubernetes S

Geography is the study of places and the relationships between people and their environments. Often we think of maps, but maps are static. GIS gets interesting when you realize that we're studying and visualizing data flowing through these locations and communities. In this episode, you'll meet Silas Toms. He's an author of several Python GIS books and the host of The Mappist Hour podcast. Are you ready to dive into GIS with Python? Links from the show Silas on twitter Twitter: @loki_president Silas' Books: Mastering Geospatial Analysis with Python: Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter: amazon.com ArcPy and ArcGIS – Geospatial Analysis with Python: amazon.com ArcPy and ArcGIS - Second Edition: Automating ArcGIS for Desktop and ArcGIS Online with Python: amazon.com The Mappyist Hour podcast: themappyisthour.com Packages GeoAlchemy ORM: geoalchemy-2.readthedocs.io Leaflet.js: leafletjs.com Mapbox GL: mapbox.com Deck GL: deck.gl Sponsors Talk Python

When we think about accounts and security, we often think about identity (logging in and proving who you are). But for many applications, especially internal apps at large organizations, that's just step one. The next step is what can you do and what can you not do. In this episode, you'll learn about a new library called oso. It's a declarative way to create policy code that maps to your mental model for who is allowed to do what in your system. We have two guests, Graham Neray and Sam Scott from the oso project to tell us all about it. Links from the show Oso on twitter: @osoHQ Graham on twitter: @grahamneray Sam on twitter: @samososos Oso: osohq.com Django docs: docs.osohq.com Flask docs: docs.osohq.com Python library docs: docs.osohq.com Source code: github.com/osohq Debugger docs: docs.osohq.com Polar Adventure: A text-based adventure game written in Polar: osohq.com Adding authorization to your Flask app with oso: osohq.com Building a Django app with data access controls: osohq.com Django Quer