Episódios
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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
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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
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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
New media example - Metal song decomposed by classical musicians
Opera singer: youtube.com
Composer music: youtube.com
YouTube Live Stream: youtube.com
PyCon Ticket Giveaway: talkpython.fm/pycon2021
Sponsors
Retool
Linode
Talk Python Training -
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
PyCon Ticket Giveaway: talkpython.fm/pycon2021
Sponsors
Square
Linode
Talk Python Training -
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
Messaging in Jupyter: jupyter-client.readthedocs.io
DevWheel Package: pypi.org
cibuildwheel: pypi.org
YouTube Live Stream: youtube.com
PyCon Ticket Contest: talkpython.fm/pycon2021
Sponsors
Linode
Mito
Talk Python Training -
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.stackoverflow.com
DVC: dvc.org
Pandas: pandas.pydata.org
Sponsors
Datadog
Retool
Talk Python Training -
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 Python Training -
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.com
SF data: datasettes.com
FiveThirtyEight: fivethirtyeight.datasettes.com
Lahman’s Baseball Database: baseballdb.lawlesst.net
Live demo of current main: datasette.io
Sponsors
Linode
Talk Python Training -
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
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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
Sponsors
Talk Python Training
Linode -
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
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Linode -
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 Queryset filters from oso policies: osohq.com
Recent episode on authentication over at Talk Python: talkpython.fm/292
MongoDB most wanted DB: insights.stackoverflow.com
Talk Python [pro edition]: talkpython.fm/pro
FastAPI course: talkpython.fm/fastapi
Sponsors
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As software developers, we live in a world of uncertainty and flux. Do you need to build a new web app? Well maybe using Django makes the most sense if you've been doing it for a long time. There is Flask, but it's more mix and match being a microframework. But you've also heard that async and await are game changers and FastAPI might be the right choice.
Whatever it is you're building, there is constant pressure to stay on top of a moving target. Learning is not something you do in school then get a job as a developer. No, it a constant and critical part of your career. That's why we all need to be good, very good, at it.
Matt Harrison is back on Talk Python to talk to us about some tips, tricks, and even science about learning as software developers.
Links from the show
Matt on Twitter: @__mharrison__
Matt's Learning Course (use code TALKPYTHON20 for 20% off): mattharrison.podia.com
Friends of the show: talkpython.fm/friends-of-the-show
Streamlit: streamlit.io
Jupyter LSP: github.com/krassowski/jupyterlab-lsp
Sponsors
Brilliant
Linode
Talk Python Training -
So you're excited about that next app you're about to build. You can visualize the APIs with the smooth scalability taking to the mobile apps. You can see how, finally, this time, you'll get deployment right and it'll be pure continuous delivery out of GitHub with zero downtime.
What you're probably not dreaming about is writing yet another password reset form and integrating mail capabilities just for this purpose. Or how you'll securely store user accounts the right way this time.
Don't worry, we got you covered. Our guests, Christos Matskas and John Patrick Dandison are here to cover a bunch of different libraries and techniques we can use for adding identity to our Python applications.
Links from the show
Christos on Twitter: @christosmatskas
John Patrick Dandison on Twitter: @azureandchill
shhgit live: shhgit.com
Twitch channel for Christos and JP: twitch.tv/425show
Passlib & Folding: passlib.readthedocs.io
Microsoft Authentication Library: github.com/AzureAD
authlib - JavaScript Object Signing and Encryption draft implementation: github.com
django-allauth - Authentication app for Django that "just works": github.com
django-oauth-toolkit - OAuth 2 goodies for Django: github.com
python-oauth2 - A fully tested, abstract interface to creating OAuth clients and servers: github.com
python-social-auth - An easy-to-setup social authentication mechanism: github.com
Sponsors
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