Talk Python To Me

Machine learning has made huge advancements in the past couple of years. We now have ML models helping doctors catch disease early. Google is using ML to suggest traffic routes in their maps app that will lesson the amount of gasoline used in the trip. And many more examples. But there is a heavy cost for training machine learning models.

When you think of government software development and projects, do you fast apps and modern tech stacks jump to mind? Probably not. So you'll be delighted to hear from our guest, Laura Beaufort. She's the Tech Lead at the US Federal Election Commission.

Flask is one of the most popular Python web frameworks. And they have huge news to share with us. Flask 2.0 just released after a ton of work. And it's as big of a deal as the version number suggests. Async changes are coming, Python 3.5 and below (including Python 2) support has been dropped and much much more. Join me as I discuss Flask 2.0 with David Lord and Philip Jones.

Have you heard that FastAPI is awesome? We have Michael Herman back on the show to help us make it even more awesome with his FastAPI awesome list. He's categorized many extensions and other libraries working with FastAPI to help you be even more efficient with this framework.

Here's an episode that I did not see coming! Sebastian Witowski and I put together a live stream ask me anything (AMA) as a follow up to some of the ideas around his recent course, Modern Python Projects. We dove deep in comparisons of Poetry vs. pip vs pyenv and answered questions like do you need to use Docker? When should you? and so on.

Data validation and conversion is one of the truly tricky part of getting external data into your app. This might come from a REST API, a file on disk, or somewhere else. This includes checking for required fields, correct data types, converting from compatible types (for example, strings to numbers), and much more. Pydantic is one of the best ways to do this in modern Python using dataclass-like constructs and type annotations to make it all seamless and automatic.

How do you build Python applications that can handling literally billions of requests. I has certainly been done to great success with places like YouTube (handling 1M requests / sec) and Instagram as well as internal pricing APIs at places like PayPal and other banks.

These days Git is synonymous with source control itself. Rare are the current debates of whether to use git vs SVN vs some fossil like SourceSafe vs you name it. But do you know how Git works? What about it's internals? I'm sure you've seen a .git folder in your project's root. But to most folks, it's a black box.

The tables have turned and this time I'm the guest and you all are the hosts. I get a ton of questions over email and twitter asking me about my thoughts on various trends, tools, and behind the scenes questions around Talk Python. So I've enlisted two listeners who are up for hosting a conversation and taking questions from you all.

Machine learning and data science are full of best practices and important workflows. Can we extrapolate these to our broader lives? Eugene Yan and I give it a shot on this slightly more philosophical episode of Talk Python To Me.

Docker is one of the core elements of developing Python applications in consistent ways as well as running them across different hardware universally. On this episode, you'll meet Peter McKee from Docker. He's here to catch us up on what's happening in the Docker universe for Python developers.

Python has changed a lot since its inception 30 years ago. On this episode, you'll meet Paul Everitt and Barry Warsaw. They have both been involved with Python since the very first Python conference (called SPAM1 even). We discuss how it's changed but also how so many of the pressures and ideas from the very early days are still playing out in 2021. I'm sure you'll enjoy all the stories and reminiscing.

When we talk about scaling software threading and async get all the buzz. And while they are powerful, using asynchronous queues can often be much more effective. You might think this means creating a Celery server, maybe running RabbitMQ or Redis as well. What if you wanted this async ability and many more message exchange patterns like pub/sub. But you wanted to do zero of that server work? Then you should check out ZeroMQ.

People often ask me how they can find a Python community to be part of. Maybe discussion forum or slack channel. This week, we look at one of the most active communities in Python Discord. It's Python, on a discord server. But it's more than that too.

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.