If you're like most people, the simplicity and easy of getting started is a big part of pytest's appeal. But beneath that simplicity, there is a lot of power and depth. We have Brian Okken on this episode to dive into his latest pytest tips and tricks for beginners and power users.

Links from the show

pytest tips and tricks article: pythontest.com
Getting started with pytest Course: training.talkpython.fm
pytest book: pythontest.com
Python Bytes podcast: pythonbytes.fm
Brian on Mastodon: @brianokken@fosstodon.org

Hypothesis: readthedocs.io
Hypothesis: Reproducability: readthedocs.io
Get More Done with the DRY Principle: zapier.com
"The Key" Keyboard: stackoverflow.blog
pytest plugins: docs.pytest.org
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#407: pytest tips and tricks for better testing

The great power of Python is its over 400,000 packages on PyPI to serve as building blocks for your app. How do you get those needed packages on to your dev machine and managed within your project? What about production and QA servers? I don't even know where to start if you're shipping built software to non-dev end users. There are many variations on how this works today. And where we should go from here has become a hot topic of discussion. So today, that's the topic for Talk Python. I have a great panel of guests: Steve Dower, Pradyun Gedam, Ofek Lev, and Paul Moore.

Links from the show

Python Packaging Strategy Discussion - Part 1: discuss.python.org
Thoughts on the Python packaging ecosystem: pradyunsg.me
Python Packaging Authority: pypa.io
Hatch: hatch.pypa.io
Pyscript: pyscript.net
Dark Matter Developers: The Unseen 99%: hanselman.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#406: Reimagining Python's Packaging Workflows

So you know about dependencies and testing, right? If you're talking to a DB in your app, you have to decide how to approach that with your tests. There are lots of solid options you might pick and they vary by goals. Do you mock out the DB layer for isolation or do you use a test DB to make it as real as possible? Do you just punt and use the real DB for expediency? What if your dependency was a huge array of radio telescopes and a rack of hundreds of bespoke servers? That's the challenge on deck today were we discuss testing radio astronomy with pytest with our guest James Smith. He's a Digital Signal Processing engineer at the South African Radio Astronomy Observatory and has some great stories and tips to share.


Links from the show

GPU-based correlator for MeerKAT: github.com
Meerkat: sarao.ac.za
SARAO: sarao.ac.za
Skarab server: peralex.com
pycuda: documen.tician.de
Commercial Telescopes: telescope.com
PyLaTeX: github.com
Linearity Test Code: talkpython.fm
Correlator Context: talkpython.fm
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#405: Testing in Radio Astronomy with Python and pytest

Clean code is one of those aspects of your programming career that's easy to put on the back burner (sometimes by management more than yourself). But it's important in the short term for writing more debuggable and readable code. And important in the long run for avoiding having your program take on the dreaded "legacy code" moniker. We're fortunate to have Bob Belderbos back on the show. He's been thinking and writing about clean code and Python a lot lately and we'll dive into a bunch of tips you can use right away to make your code cleaner.


Links from the show

Bob on Mastodon: @bbelderbos@fosstodon.org
PyBites: pybit.es
Tips for clean code in Python article: pybit.es
Refactoring book: pybitesbooks.com
Final type: docs.python.org
Sentinels pattern: python-patterns.guide
Black formater: pypi.org
Guarding clauses: medium.com
ChatGPT: chat.openai.com
Git Precommit: pre-commit.com
#100DaysOfCode in Python course: training.talkpython.fm
#100DaysOfWeb in Python course: training.talkpython.fm
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#404: Clean Code in Python

Imagine a world with free and unlimited clean energy. That's the musings of a great science fiction story. But nuclear fusion (the kind that powers the sun)  has always been close at hand, we see the sun every day, and yet impossibly far away as a technology. We took a major step towards this becoming a reality with the folks at the Lawrence Livermore National Labratory in the US achieved "ignition" where they got significantly more energy out than they put in. And Python played a major role in this research and experiment. We have Jay Salmonson here to give us a look at the science and the Python code of this discovery.

Links from the show

Jay on Mastodon: hachyderm.io/@jdsalmonson
Jay on Twitter: @JaySalmonson

Official Announcement: lasers.llnl.gov

QnD Package: github.com
PlasmaPy: plasmapy.org
ML in Fusion: llnl.gov
National Ignition Facility Achieves Ignition in Historic Nuclear Fusion Experiment: newenergytimes.net
Video demonstrating the fusion lab: youtube.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#403: Fusion Ignition Breakthrough and Python

When you think about processing tabular data in Python, what library comes to mind? Pandas, I'd guess. But there are other libraries out there and Polars is one of the more exciting new ones. It's built in Rust, embraces parallelism, and can be 10-20x faster than Pandas out of the box.



We have Polars' creator, Ritchie Vink here to give us a look at this exciting new data frame library.

Links from the show

Ritchie on Mastodon: @ritchie46@fosstodon.org
Ritchie on Twitter: @RitchieVink
Ritchie's website: ritchievink.com

Polars: pola.rs
Apache Arrow: arrow.apache.org
Polars Benchmarks: pola.rs
Coming from Pandas Guide: github.io
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#402: Polars: A Lightning-fast DataFrame for Python [updated audio]

At some point, you've probably migrated an app from one framework or major runtime version to another. For example, Django to Flask, Python 2 to Python 3, or even Angular to Vue.js. This can be a big challenge. If you had 100s of active devs and millions of lines of code, it's a huge challenge. We have Ben Bariteau from Yelp here to recount their story moving 3.8M lines of code from Python 2 to Python 3. But this is not just a 2-to-3 story. It has many lessons on how to migrate code in many situations. There are plenty of gems to take from his experience.


Links from the show

Ben on Twitter: @benbariteau
Ben's Talk at PyCon 2022: youtube.com
python-modernize: github.com
python-future: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#401: Migrating 3.8 Million Lines of Python

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.

Links from the show

Charlie on Twitter: @charliermarsh
Charlie on Mastodon: @charliermarsh@hachyderm
Ruff: github.com

PyCharm Developer Advocate Job: jetbrains.com/careers
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#400: Ruff - The Fast, Rust-based Python Linter

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.  



On this episode, we have David Vujic here to give us his perspective on monorepos as well as highlight an architectural pattern and set of tools for accomplishing this in Python.

Links from the show

David on Twitter: @davidvujic
David on Mastodon: @davidvujic@mastodon.nu
Monorepo definition: wikipedia.org
git-sizer tool for large repos: github.com
git partial clones: docs.gitlab.com
git sparse checkout: git-scm.com
Polylith architecture: polylith.gitbook.io
Article: A simple & scalable Python project structure: davidvujic.blogspot.com
The last Python Architecture you will ever need?: davidvujic.blogspot.com
python-polylith plugin for poetry: github.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#399: Monorepos in Python

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.

Links from the show

Sara's PyCon keynote: youtube.com
Sara on Twitter: @saraissaoun
Event Horizon Telescope: eventhorizontelescope.org
Black Hole Image Makes History; NASA Telescopes Coordinated Observations: nasa.gov
Event Horizon Data: eventhorizontelescope.org
Imaging, analysis, and simulation software for radio interferometry Package: github.com
Initial data showing ring (matplotlib) (video at time): youtube.com
Mars 2020 Helicopter GitHub Badge: github.blog
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#398: Imaging Black Holes with Python

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.

Links from the show

Guests
Emily Morehouse-Valcarcel: @emilyemorehouse
Cecil Phillip: @cecilphillip@hachyderm.io
Kim van Wyk: @kimvanwyk@fosstodon.org
Łukasz Langa: @ambv@mastodon.social
Gareth Thomas: @thestub@pythonist.as
Dan Gerlanc: @dgerlanc@fosstodon.org

Does the GitHub page need to be pretty?: github.com/tiangolo/fastapi

Do fancy "marketing pages" matter?
Poetry: python-poetry.org
Tailwind: tailwindcss.com
VueJS: vuejs.org

Aesara: github.com/aesara-devs/

Story of creating Michael's Site: mkennedy.codes
Went to the community: fosstodon.org
Hugo Site Generator: gohugo.io
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#397: Evaluating New Open Source Tech Panel

See the full show notes for this episode on the website at talkpython.fm/396

#396: AI Goes on Trial For Writing Code (crossover)

See the full show notes for this episode on the website at talkpython.fm/395

#395: Tools for README.md Creation and Maintenance

See the full show notes for this episode on the website at talkpython.fm/394

#394: Awesome Jupyter Libraries and Extensions in 2022

See the full show notes for this episode on the website at talkpython.fm/393

#393: Space Science with Python

See the full show notes for this episode on the website at talkpython.fm/392

#392: Data Science from the Command Line

See the full show notes for this episode on the website at talkpython.fm/391

#391: Pyscript powered by MicroPython

See the full show notes for this episode on the website at talkpython.fm/390

#390: Mastodon for Python Devs

See the full show notes for this episode on the website at talkpython.fm/389

#389: 18 awesome asyncio packages in Python

See the full show notes for this episode on the website at talkpython.fm/388

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