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Talk Python To Me

Talk Python To Me

Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.


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#304 asyncio all the things with Omnilib

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:

Live Youtube Stream:

Power On:
The Trevor Project:


Talk Python Training
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#303 Python for Astronomy with Dr. Becky

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:
5 ways I use code as an astrophysicist video:
Astrophysicist reacts to funny space MEMES video:
A day in the life of an Oxford University Astrophysicist:
Book: Space: 10 things you should know:

Apple maps: image
Otter space: image
Eclipses: image
Steals a cow: image
Black holes: image

YouTube live stream:


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#302 The Data Engineering Landscape in 2021

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:

Tobias Macey:

Data Engineering podcast:

Designing Data-Intensive Applications Book:
A Beginner?s Guide to Data Engineering:
Apache Airflow:
#68 Crossing the streams with Podcast.__init__:
Great Expectations:
Languages trends on StackOverflow:


Talk Python Training
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#301 Deploying and running Django web apps in 2021

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

Will Vincent:
Carlton Gibson: @carltongibson

Watch the live stream:

Give me back my monolith:
Carlton?s Button hosting platform:
Django Software Foundation:
Django News newsletter:
Deployment Checklist:
Environs 3rd party package for environment variables:
Django Static Files & Templates:
Learn Django:

Configuring uWSGI for Production Deployment @ Bloomberg:


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#300 Building a data science startup (panel)

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:

Matthew Rocklin
Twitter: @mrocklin
Jobs @ Coiled:

Jonathon Morgan
Twitter: @jonathonmorgan
Yonder AI:

William Stein
Twitter: @wstein389

Talk Python Live Streams:

Sentry Promo Code: TALKPYTHON2021


Sentry Error Monitoring, Code TALKPYTHON
Talk Python Training
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#299 Personal search engine with datasette and dogsheep

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

Datasheet newsletter:
Video: Build your own data warehouse for personal analytics with SQLite and Datasette:

Personal data warehouses:
Global power plants:
SF data:
Lahman?s Baseball Database:
Live demo of current main:


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#298 Building ML teams and finding ML jobs

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:
Chip's Book Preview:
handcalcs project:
IBM Buzzword Bingo:


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#297 Python year in review (2020 edition)

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:

Cecil Phillip: @cecilphillip
Ines Montani: @_inesmontani
Jay Miller: @kjaymiller
Paul Everitt: @paulweveritt
Reuven Lerner: @reuvenmlerner
Matt Harrison: @__mharrison__
Brian Okken: @brianokken


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#296 Python in F1 racing

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:
Joe on Twitter: @joedborg

Racing Point F1 team:
Scuderia Alpha Tauri F1 team:

Charmed Kubernetes:


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#295 GIS + Python

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:
ArcPy and ArcGIS ? Geospatial Analysis with Python:
ArcPy and ArcGIS - Second Edition: Automating ArcGIS for Desktop and ArcGIS Online with Python:

The Mappyist Hour podcast:

GeoAlchemy ORM:
Mapbox GL:
Deck GL:


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#294 oso authorizes 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


Django docs:
Flask docs:
Python library docs:
Source code:
Debugger docs:

Polar Adventure: A text-based adventure game written in Polar:

Adding authorization to your Flask app with oso:
Building a Django app with data access controls:
Django Queryset filters from oso policies:

Recent episode on authentication over at Talk Python:
MongoDB most wanted DB:
Talk Python [pro edition]:
FastAPI course:


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#293 Learning how to learn as a developer

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):

Friends of the show:
Jupyter LSP:


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#292 Pythonic identity (auth in Python ecosystem)

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:
Twitch channel for Christos and JP:

Passlib & Folding:
Microsoft Authentication Library:
authlib - JavaScript Object Signing and Encryption draft implementation:
django-allauth - Authentication app for Django that "just works":
django-oauth-toolkit - OAuth 2 goodies for Django:
python-oauth2 - A fully tested, abstract interface to creating OAuth clients and servers:
python-social-auth - An easy-to-setup social authentication mechanism:


Talk Python Training
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#291 Operational Resilience with Pyomo

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

Clark on Twitter: @clarkpetri
Center for Infrastructure Defense:
I?m not alone in my work post:
handcalcs package:
Diet optimization problem:

Talk Python [Pro Edition]:
Black Friday at Talk Python:


Talk Python Training
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#290 Side Hustles for Data Scientists

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:
Keith's courses:
Side Hustle Strategies for Data Science and Analytics Experts course:

Talk Python's Excel to Python course:


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#289 Discovering exoplanets with Python

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:


Talk Python Training
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#288 10 tips to move from Excel to Python

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:
Escaping Excel Hell with Python and Pandas Episode 200:
SideTable package:

Learn more and go deeper
Move from Excel to Python with Pandas Course:
Excel to Python webcast:


Voyager game
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#287 Testing without dependencies, mocking in Python

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:
100 Days of Code episode:
Anna-Lena on Github:
PyCon talk from Lisa Road (2018) - ?Demystifying the patch function?:
PyCon talk from Edwin Jung (2019) - Mocking and Patching Pitfalls:
Keynote talk ?Finding Magic in Python? (about magical universe
Blog post about mocking in Python:
Stackoverflow post on difference between stubs and mocks:
Freezegun project:
KI Macht Schule (AI goes to school):
Code Combat:


Talk Python Training
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#286 Python and ML at NASA Jet Propulsion Laboratory (JPL)

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:
Nature: A vision for data science:
Open source at JPL:
Apache Nutch:
7 Minutes of Terror: The Challenges of Getting to Mars:
tqdm package:
Panama Papers:


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#285 Dask as a Platform Service with Coiled

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 raised $5M in Sept:
A brief history of dask article:
Coiled: Dask for Everyone, Everywhere:
The incredible growth of python:
Growth updated (SO Trends current):
Coiled Youtube channel:
Snorkel package:


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#284 Modern and fast APIs with FastAPI

As Python 3 has eclipsed the old constrains of Python 2 and web frameworks that adopted them, we have seen a big jump in new frameworks appearing on the scene taking full advantage of things like type hints, async and await, and more.

No framework has done this more successfully than FastAPI recently. That's why we are here with the creator of FastAPI, Sebastián Ramírez to dive into this exciting new Python web framework.

Links from the show

Sebastian: @tiangolo
FastAPI: One of the fastest Python frameworks available:
FastAPI for Flask Users:
FastAPI Docker image:
Traffic server:


Talk Python Training
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#283 Web scraping, the 2020 edition

Web scraping is pulling the HTML of a website down and parsing useful data out of it. The use-cases for this type of functionality are endless. Have a bunch of data on governmental sites that are only listed online in HTML without a download? There's an API for that! Do you want to keep abreast of what your competitors are featuring on their site? There's an API for that. Need alerts for changes on a website, for example enrollment is now open at your college and you want to be first to get in and avoid the 8am Monday morning course slot? There's an API for that.

That API is screen scraping and Attila Tóth from ScrapingHub is here to tell us all about it.

Links from the show

Attila Tóth on LinkedIn:
Scrapy project:
Scrapinghub on Twitter: @scrapinghub
cookiecutter template for Scrapy projects:
Splash: headless browser designed specifically for web scraping:
Awesome Web Scraping list:

Talk Python episode 50 on web scraping:
How Web Scraping is Revealing Lobbying and Corruption in Peru:
Web Data Extraction Summit event:


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#282 pre-commit framework

Git hook scripts are useful for identifying simple issues before committing your code. Hooks run on every commit to automatically point out issues in code such as trailing whitespace and debug statements. By pointing these issues out before code review, this allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks.

As we created more libraries and projects we recognized that sharing our pre-commit hooks across projects is painful. That's why I'm happy to welcome Anthony Sottile to the show to discuss pre-commit, a framework for managing and maintaining multi-language pre-commit hooks.

Links from the show

Anthony at Twitter: @codewithanthony
pre-commit continuous integration:
pre-commit hooks:
pre-commit on GitHub:
shhgit secret discovery project:
babi editor:
Twitch stream:

Anthony on GitHub:


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#281 Python in Car Racing

I love to bring you stories of Python being used in amazing places outside the traditional tech silos of pure web development and data science.

On this episode, you'll meet Robert "Kane" Replogle, who works on the simulation and test software at Richard Childress Racing. The NASCAR team that just finished #1 and 2 in at the Texas Motor Speedway.

You'll hear how Python is allowing them to model car behavior, air flow, and more much faster than others using outdated tools.

Watch the hot lap around Brands Hatch

Links from the show

Kane on LinkedIn:
Kane on Twitter: @ReplogleRk
Richard Childress Racing:
Windshear, the 180 mph tunnel:
blackcellmagic package:


SonarQube linting tools
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#280 Python and AI in Journalism

If there has ever been a time in history that journalism is needed to shine a light on what's happening in the world, it's now. Would it surprise you to hear that Python and machine learning are playing an increasingly important role in discovering and bringing us the news? On this episode, you'll meet Carolyn Stansky, a journalist and developer who's been researching this intersection.

Links from the show

Carolyn on Twitter: @carolstran
Carolyn on LinkedIn:
Carolyn's site:
Carolyn's talk: How AI is enhancing journalism:

Examples of AI / automation in use
LA Homicide Tracker:
Buzzfeed Finding and tracking secret spy planes:
NY Times comment moderation:
Al Jazzira: Drones in warzones:

Tools - Quartz AI Studio, focused on helping smaller papers and journalists:
Google News Initiative:

Newspaper3k Package:
Google News and Australia fight:
Twitter thread on American news overwhelming other countries:


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#279 Modern Python Developer's Toolkit

Python is quick and easy to learn. And yet, there is a massive gap between knowing the common aspects of the language (loops, variables, functions, and so on) and how to write a well-factored application using modern tools and libraries. That's where learning Python is a never-ending journey.

Sebastian Witowski is here to give us his take on a modern Python developer's toolkit. There are a bunch of great tips in store for us.

Links from the show

Sebastian on Twitter: @SebaWitowski
The tutorial recording from PyCon:
Sebastian?s website:
Workshop resources site:
Writing Faster Python talk:
Hugo Static Site Theme:

Announcements at Talk Python
Python Memory Course:
Excel to Python and Pandas Course:
Excel to Python Webcast:
Team Cohorts:


Talk Python Training
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