エピソード
-
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 -
エピソードを見逃しましたか？
-
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
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
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
Talk Python Training
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
Talk Python Training -
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
Talk Python Training
Linode -
Do you have a scientific system that needs optimization or solving? Our guest, on this episode, Clark Petri is here to tell us all about pyomo. This is a library that can solve all sorts of cool problems, linear programming, nonlinear equations, and many other things you can throw at it.
We're gonna solve a really fun diet problem: What is the most nutritious meal that you can eat for the least amount of money? The answer might surprise you a little bit! It's going to be a lot of fun. So listen in to hear about how Clark has used pyomo to do his work and how you might use it in yours.
Links from the show
Pyomo: pyomo.org
Clark on Twitter: @clarkpetri
Center for Infrastructure Defense: nps.edu
Thesis: apps.dtic.mil
I’m not alone in my work post: morenuance.com
handcalcs package: github.com
Diet optimization problem: nbviewer.jupyter.org
Talk Python [Pro Edition]: talkpython.fm/pro
Black Friday at Talk Python: talkpython.fm/blackfriday
Sponsors
Talk Python Training
Linode -
Are you a data scientist looking to branch out on your own and start something new? Maybe you're just looking for a way to work with those exciting libraries that aren't yet in play at the day job. Rather than putting everything on the line, quitting your job, and hoping things work out, maybe you should start with a side-hustle.
On this episode, you'll meet Keith McCormick, a data scientist who has many irons in the fire and he's here to tell us about different types of side hustles and why you may want to try or avoid one.
Links from the show
Keith on Twitter: @kmccormickblog
Keith on LinkedIn: linkedin.com
Keith's courses: linkedin.com
Side Hustle Strategies for Data Science and Analytics Experts course: linkedin.com/learning
Talk Python's Excel to Python course: talkpython.fm/excel
Sponsors
Linode
Talk Python Training -
When I saw the headline "Machine learning algorithm confirms 50 new exoplanets in historic first" I knew the Python angle of this story had to be told! And that's how this episode was born. Join David Armstrong and Jev Gamper as they tell us how they use Python and machine learning to discover not 1, but 50 new exoplanets in pre-existing Keplar satellite data.
Links from the show
Jev Gamper on Twitter: @brutforcimag
Machine learning algorithm confirms 50 new exoplanets in historic first article: techrepublic.com
Sponsors
Brilliant
Talk Python Training -
Excel is one of the most used and most empowering piece of software out there. But that doesn't make it a good fit for every data processing need. And when you outgrow Excel, a really good option for a next step is Python and the data science tech stack: Pandas, Jupyter, and friends.
Chris Moffitt is back on Talk Python to give us concrete tips and tricks for moving from Excel to Python!
Links from the show
Chris on Twitter: @chris1610
Practical Business Python: pbpython.com
Escaping Excel Hell with Python and Pandas Episode 200: talkpython.fm
SideTable package: pbpython.com
Learn more and go deeper
Move from Excel to Python with Pandas Course: training.talkpython.fm
Excel to Python webcast: crowdcast.io
Sponsors
Voyager game
Linode
Talk Python Training -
We know our unit tests should be relatively independent from other parts of the system. For example, running a test shouldn't generally call a credit card possessing API and talk to a database when your goal is just to test the argument validation.
And yet, your method does all three of those and more. What do you do? Some languages use elaborate dependency passing frameworks that go under the banner of inversion of control (IoC) and dependency injections (DI). In Python, the most common fix is to temporarily redefine what those two functions do using patching and mocking.
On this episode, we welcome back Anna-Lena Pokes to talk us through the whole spectrum of test doubles, dummies, mocks, and more.
Links from the show
Anna-Lena's personal site: alpopkes.com
100 Days of Code episode: talkpython.fm/186
Anna-Lena on Github: github.com
PyCon talk from Lisa Road (2018) - “Demystifying the patch function”: youtube.com
PyCon talk from Edwin Jung (2019) - Mocking and Patching Pitfalls: youtube.com
Keynote talk “Finding Magic in Python” (about magical universe
project): youtube.com
Blog post about mocking in Python: alpopkes.com
Stackoverflow post on difference between stubs and mocks: stackoverflow.com
Freezegun project: github.com
KI Macht Schule (AI goes to school): ki-macht-schule.de
Code Combat: codecombat.com
PDB++: github.com
Sponsors
Linode
Monday.com
Talk Python Training -
NASA's Jet Propulsion Laboratory (JPL)'s primary function is the construction and operation of planetary robotic spacecraft, though it also conducts Earth-orbit and astronomy missions. It is also responsible for operating NASA's Deep Space Network.
On this episode, you'll meet Chris Mattman. He's the Division Manager for the Artificial Intelligence, Analytics and Innovation at NASA JPL and he's JPL's first Principal Scientist in the area of Data Science. We cover a wide range of topics, and dive into how Python and open-source are growing in the space exploration field. And he answers the question of whether he thinks we'll have Python running on robots and rovers in space.
Links from the show
Chris on Twitter: @chrismattmann
Chris at JPL: jpl.nasa.gov
Nature: A vision for data science: nature.com
Open source at JPL: github.com
Apache Nutch: nutch.apache.org
7 Minutes of Terror: The Challenges of Getting to Mars: youtube.com
tqdm package: pypi.org
Panama Papers: wikipedia.org
Sponsors
Linode
Monday.com
Talk Python Training -
If you're into data science, you've probably heard about Dask. It's a package that feels like familiar APIs such as Numpy, Pandas, and Scikit-Learn. Yet it can scale that computation across CPU cores on your local machine all the way to distributed grid-based computing in large clusters.
While powerful, this may take some serious setup to execute in its full glory. That's why Matthew Rocklin has teamed up with Hugo Bowne-Anderson and others to launch a business to help Python loving data scientists run Dask workloads in the cloud. And they are here to tell us about they open-source foundation business.
And they must be on to something, between recording and releasing this episode, they raised $5M in VC funding.
Links from the show
Hugo on Twitter: @hugobowne
Matthew on Twitter: @mrocklin
Coiled: coiled.io
Coiled raised $5M in Sept: twitter.com
A brief history of dask article: coiled.io/blog
Coiled: Dask for Everyone, Everywhere: medium.com
The incredible growth of python: stackoverflow.blog
Growth updated (SO Trends current): insights.stackoverflow.com
Coiled Youtube channel: youtube.com
Snorkel package: pypi.org
Sponsors
Brilliant
Monday.com
Talk Python Training - もっと表示する