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You may have heard of the Bloomberg terminal. It's expensive software that can monitor and analyze real-time financial market data and place trades on the electronic trading platform. But have you heard of OpenBB? It's similar software for real-time and long term analysis for finance and investing. The difference is it's open source and built entirely with Python and gives you access to analyze a massive amount of real-time and historical data using the full Python data science stack. On this episode, we have one of the cofounders, James Maslek here to give us a look inside this cool piece of Python-based software.
Python is undergoing a performance renaissance. We already have Python 3.11 20-40% faster than even Python 3.10. On this episode, we'll dive into a new proposal to make Python even more efficient using lazy imports laid out in PEP 690. We have all three folks involved on the episode: Carl Meyer, Germán Méndez Bravo, and Barry Warsaw. Are you ready to get into making Python faster still? Let's dive in.
How do you test whether your web sites are working well? Unit tests are great. But for web apps, the number of pieces that have to click together "just so" are many. You have databases, server code (such as a Flask app), server templates (Jinja for example), CSS, Javascript, and even deployment topologies (think nginx + uvicorn). Unit tests won't cover all of that integration. But Playwright does. Playwright is a modern, Pythonic take on testing webs apps using code driving a browser core to interact with web apps the way real users and API clients do. I think you'll find a lot to like there. And we have Pandy Knight from Automation Panda here to break it down for us.
Despite Python being overwhelmingly popular and positive, there are major areas of computing where Python is not present. Most notably on mobile and on the frontend side of the web. PyScript, a new project launched by Fabio Pliger from Anaconda, just might change that. It was made public and announced at PyCon just two weeks ago by Peter Wang and now has over 10,000 GitHub stars. But what is hype vs. reality vs. projected hopes and dreams? We're going to find out on this episode. Fabio is here to tell us all about his new project.
Does your app have a database? Does that database play an important role in how the app operations and users perceive its quality? Most of you probably said yes to the first, and definitely to the second. But what if your DB isn't doing as well as it should? How would you know? And once you know, what do you do about it? On this episode, we're joined by Michael Christofides, co-creator of pgMustard, to discuss and explore the EXPLAIN command for Postgres and other databases as well as all the recommendations you might dig into as a result of understanding exactly what's happening with you queries.
How much time do you spend solving negative engineering problems? And can a framework solve them for you? Think of negative engineering as things you do to avoid bad outcomes in software. At the lowest level, this can be writing good error handling with try / except. But it's broader than that: logging, observability (like Sentry tools), retries, failover (as in what you might get from Kubernetes), and so on. We have a great chat with Chris White about Prefect, a tool for data engineers and data scientists meaning to solve many of these problems automatically. But it's a conversation applicable to a broader software development community as well.
We're all familiar with the data science tools like numpy, pandas, and others. These are numerical tools working with floating point numbers, often to represent real-world systems. But what if you exactly specify the equations, symbolically like many of us did back in Calculus and Differential Equations courses? With SymPy, you can do exactly that. Create equations, integrate, differentiate, and solve them. Then you can convert those solutions into Python (or even C++ and Fortran code). We're here with two of the core maintainer: Ondřej Čertík and Aaron Meurer to learn all about SymPy.
Are you coming to Python from another language and ecosystem? It can seem a bit daunting at first. But Python is very welcoming and has a massive array of tools and libraries. In this episode, I speak to my friend Cecil Philip who does both Python and .NET development. We discuss what it's like coming to Python from .NET as well as a whole bunch of compare and contrasts across the two ecosystems.
What would a modern Python project look like? Maybe it would use Poetry rather than pip directly for its package management. Perhaps its test automation would be controlled with Nox. You might automate its release notes with Release Drafter. The list goes on and on. And that list is the topic of this episode. Join me and Claudio Jolowicz as we discuss his Hypermodern Python project and template.
Python's place in climate research is an important one. In this episode, you'll meet Joe Hamman and Ryan Abernathey, two researchers using powerful cloud computing systems and Python to understand how the world around us is changing. They are both involved in the Pangeo project which brings a great set of tools for scaling complex compute with Python.
Python has come a long way since it was released in 1991. It originally released when the Standard Library was primary the totality of functionality you could leverage when building your applications. With the addition of pip and the 368,000 packages on PyPI, it's a different world where what we need and expect from the Standard Library. Brett Cannon and Christian Heimes have introduced PEP 594 which is the first step in trimming outdated and unmaintained older modules from the Standard Library. Join us to dive into the history and future of Python's Standard Library.
Are you working on or considering a machine learning project? On this episode, we'll meet three people from the MLOps community: Demetrios Brinkmann, Kate Kuznecova, and Vishnu Rachakonda. They are here to tell us about the lifecycle of a machine learning project. We'll talk about getting started with prototypes and choosing frameworks, the development process, and finally moving into deployment and production.
Pandas is a great library that allows you to accomplish a ton of filtering and processing in condensed syntax. But how well do you understand what's happening? Sam Lau and Philip Guo built a great site to help use visually explore how Pandas is processing your dataset with your specific syntax. It's called PandasTutor, and Sam is here to tell us about it.
Have you been considering launching a product or even a business based on Python's AI / ML stack? We have a great guest on the episode this week, Dylan Fox, who is the cofounder of AssemblyAI and has been building his startup successfully over the past few years. He has interesting stories of 100s of GPUs in the cloud, evolving ML models, and much more that I know you'll enjoy hearing.
What database are you using in your apps these days? If you like most Python people, it's probably PostgreSQL. If you roll with NoSQL like me, you're probably using MongoDB. Maybe you're even using a graph database focused more on relationships. But there's a new Python database in town, and as you learn in during this episode, many critical Python libraries have come into existence because of it. This database is called EdgeDB. EdgeDB is built upon Postgres, implemented mostly in python, and is something of a marriage of a traditional relational database and an ORM.
When you think about the power of Python, the clean language or powerful standard library may come to mind. You might certainly point to the external packages too. But what about the relative ease of picking up new libraries or even parts of the standard library? Documentation plays an important role there. And the tools in the Python space for building solid documentation and even publishing articles and books involving live code are huge assets.
Two frameworks that have taken the Python world by storm are FastAPI and Pydantic. Once you already have your data exchange modeled in Pydantic, you might want to use that code for storing it in the database. And, if you have DB models you might want to somehow use them to power and document the APIs built with FastAPI. But the popular ORMs, such as SQLAlchemy and others, far predate Pydantic. But could they be put together?
Do we talk about running Python in production enough? I can tell you that the Talk Python infrastructure (courses, podcasts, APIs, etc.) get a fair amount of traffic, but they look nothing like what Google, or Instagram, or insert [BIG TECH NAME] here's deployments do. Yet, mostly, we hear about interesting feats of engineering at massive scale that is impressive but often is also outside of the world most Python devs need for their companies and services.
The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own.
Comments (32)

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
Reply

Floyd

nix the intro music

Feb 1st
Reply

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

Gino DAnimal

What ide does she use? audio choppy.

Nov 20th
Reply (1)

Naufal

Mantul gan

Oct 7th
Reply

Nihan Dip

A great episode, lot's of information to digest. Glad to know how one of the tools that i use daily actually works.

Sep 21st
Reply
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