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Talk Python To Me

Author: Michael Kennedy (@mkennedy)

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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.
399 Episodes
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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.
#399: Monorepos in Python

#399: Monorepos in Python

2023-01-1801:10:31

Monorepos are contrary to how many of us have been taught to use source control. To start a project or app, the first thing we do is create a git repo for it. This leads to many focused and small repositories. A quick check of my GitHub account shows there are 179 non-fork repositories. That's a lot but I think many of us work that way. But it's not like this with monorepos. There you create one (or a couple) repositories for your entire company. This might have 100s or 1,000s of employees working on multiple projects within the single repo. Famously, Google, Meta, Microsoft, and Airbnb all employ very large monorepos with varying strategies of coordination.
The iconic and first ever image of a black hole was recently released. It took over a decade of work and is a major achievement for astronomy and broadens our understanding of the universe for all of us. Would it surprise you to know that Python played a major part in this discovery? Of course it did, and Dr. Sara Issaoun is here to give us the full story.
The beauty of open source software and libraries is that you're not stuck with a single option some vendor is offering. This is especially true when that support is poor and antiquated. Almost any capability you think of has multiple options even for a single language such as Python. Just think about how many web frameworks you can pick today.
For links and very detailed show notes, please view [the original episode page](https://pythonbytes.fm/episodes/show/312/ai-goes-on-trial-for-writing- code) over on Python Bytes. Thanks for listening!
If you maintain projects on places like GitHub, you know that having a classy readme is important and that maintaining a change log can be helpful for you and consumers of the project. It can also be a pain. That's why I'm excited to welcome back Ned Batchelder to the show. He has a lot of tools to help here as well as some opinions we're looking forward to hearing. We cover his tools and a bunch of others he and I found along the way.
Jupyter is an amazing environment for exploring data and generating executable reports with Python. But there are many external tools, extensions, and libraries to make it so much better and make you more productive. On this episode, we are going to cover a ton of them. We have Markus Schanta, the maintainer of the awesome-jupyter list on the show and we'll highlight a bunch of Jupyter gems.
Space science is one of the few sciences that can spark wonder and imagining in almost anyone. It also happens to be the domain of Python with many missions, telescopes, and analysis happening with Python playing a major role.
When you think data science, Jupyter notebooks and associated tools probably come to mind. But I want to broaden your toolset a bit and encourage you to look around at other tools that are literally at your fingertips. The terminal and shell command line tools. On this episode, you'll meed Jeroen Janssens. He wrote the book Data Science on The Command Line Book and there are a bunch of fun and useful small utilities that will make your life simpler that you can run immediately in the terminal. For example, you can query a CSV file with SQL right from the command line.
No Python announcement of 2022 was met with more fanfare than pyscript. This project, announced at PyCon 2022, allows you to write Python files and run them in your browser in place of JavaScript or even with interactions between Python and JavaScript. There was just one catch: The runtime download was a 9MB WebAssembly file. That made its uses quite limited.
Wondering what Mastodon is all about? More importantly, what does it offer Python developers and other open source folks compared to Twitter? There is a huge amount of interest in the tech community about what's happening at Twitter and whether they should expand to or even move to a new location. So I decided to put together a set of experienced Python developers who have been Mastodon inhabitants for a long time to discuss what this unexpected shift means for one of our important online watering holes.
If you're a fan of Python's async and await keywords and the powers they unlock, then this episode is for you. We have Timo Furrer here to share a whole bunch of asyncio related Python packages. Timo runs the awesome-asyncio list and he and I picked out some of our favorites to share with you.
Python 3.11 is here! Keeping with the annual release cycle, the Python core devs have released the latest version of Python. And this one is a big one. It has more friendly error messages and is massively faster than 3.10 (between 10 to 60% faster) which is a big deal for a year over year release of a 30 year old platform. On this episode, we have Irit Katriel, Pablo Galindo Salgado, Mark Shannon, and Brandt Bucher all of whom participated in releasing Python this week on the show to tell us about that process and some of the highlight features.
Do you have a large or growing Python code base? If you struggle to run builds, tests, linting, and other quality checks regularly or quickly, you'll want to hear what Benjy Weinberger has to say. He's here to introduce Pants Build to us. Pants is a fast, scalable, user-friendly build system for codebases of all sizes. It's currently focused on Python, Go, Java, Scala, Kotlin, Shell, and Docker.
Python's data science and data visualization capabilities are certainly one of the reasons for Python's meteoric rise over the past 10 years. But often thens visuals have been corralled into notebooks used by data scientists themselves or into static web pages. Recently, a host of excellent dashboard build and hosting frameworks have come along to turn these visuals into interactive apps for everyone. On this episode, we'll talk about H20 Wave. One of these excellent dashboard frameworks. We have Martin Turoci from H2O.ai here to tell us about Wave.
Do you love Python's async and await but feel that you could use more flexibility and higher-order constructs like running a group of tasks and child tasks as a single operation, or streaming data between tasks, combining async tasks with multiprocessing or threads, or even async file support? You should check out AnyIO. On this episode we have Alex Grönholm the creator of AnyIO here to give us the whole story.
Do you struggle to know where to start in the wide range of Python's visualization frameworks? Not sure when to use Plotly vs. Matplotlib vs. Altair? Then this episode is for you. We have Chris Moffitt, a Talk Python course author and founder of Practical Business Python, back on the show to discuss getting started with Python's data visualization frameworks.
For all the amazing powers of Python, deploying packaged apps that leverage native OS-level capabilities isn't one of them. But it can be done and we have a great guest, Rhet Turnbull, here to tell us how he built his distributable macOS app Textinator that uses macOS's native vision recognition framework through Python.
When you think data exploration using Python, Jupyter notebooks likely come to mind. They are excellent for those of us who gravitate towards Python. But what about your everyday power user? Think of that person who is really good at Excel but has never written a line of code? They can still harness the power of modern Python using a cool application called Superset.
We are on the edge of a major jump in Python performance. With the work done by the Faster CPython team and Python 3.11 due out in around a month, your existing Python code might see an increase of well over 25% in speed with no changes. One of the main reasons is its new specializing, adaptive interpreter. This episode is about that new feature and a great tool called Specialist which lets you visualize how Python is speeding up your code and where it can't unless you make minor changes. Its creator, Brandt Bucher is here to tell us all about.
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Comments (35)

TInfinite

Interesting

Dec 25th
Reply

Vlad Bezden

Great podcast! The best part was about deployment tools py2app and PyInstaller. That is exactly what I was looking for. After listening about it, I just used PyInstaller at the company and it worked like a charm. Thank you for doing it and keep up a good work!

Oct 4th
Reply

Javad Hamidi

voice quality is terrible

Jul 29th
Reply

Hamza Senhaji Rhazi

this episode is gold, the article submitted with it is gold too

Apr 27th
Reply

Joshua Tasker

yo so I'm barely starting to get into this or I really want to learn how to code what do you recommend for me to start I have very little knowledge just being honest

Feb 10th
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Floyd

nix the intro music

Feb 1st
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Antonio Andrade

It was fun, thanks for having me over

Dec 28th
Reply

Homa

awesome!

Feb 24th
Reply

Magnus Lamont

Carlton's talk is on YouTube as "DjangoCon 2019 - Using Django as a Micro-Framework: Hacking on the HTTP handlers.. by Carlton Gibson" https://2019.djangocon.us/talks/using-django-as-a-micro-framework-on-the/ Couldn't find it in the show notes.

Feb 3rd
Reply

Kit Macleod

notes

Dec 31st
Reply

Pat Decker

Michael, At the end of each episode you could ask "Is it Gif or Jif?" Just for the fun of it.

Sep 9th
Reply

Carl Littlejohns

great podcast - testing your tests all night (without even being there) - some good coding discipline there for us noobs

Jun 20th
Reply

J Bit

great episode! I've been using Python on Windows for the past two years and I love it. I've never had any problems specific to Windows.

Dec 19th
Reply (1)

Hossein Fakhari

at the 53:12 what is the package name? pip install eo? eil?

Sep 16th
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Dan Stromberg

Pyodide is undeniably cool. There's also a micropython port to wasm that might make sense for basic webapps.

May 18th
Reply

Antonio Andrade

ummm. But the mic sounds terrible hahah

Apr 22nd
Reply

Kelechi Emenike

you remind me of me! excellent Googler, master of science, business-related experience, passionate about teaching... the only thing I've not done like you is actually create my own course... you wanna take on a mentee? I'm game please ^--^

Apr 6th
Reply

Patryk Siewiera

I listen for a year, I fell like Michael Kennedy is my best friend, im so grateful for showing me that excitement and possibilities with this language, this is my new road in life. thanks so much 10/10

Mar 7th
Reply

ねじまきラジオ

Python勉強中の方は必聴!

Feb 16th
Reply

Ketan Ramteke

Stackoverflow users are really mean but I still love it, there is no better alternative to it and the meanness keeps bad contents at bay. So it's good to be mean I guess.

Dec 11th
Reply
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