100 episodes

The weekly podcast about the Python programming language, its ecosystem, and its community. Tune in for engaging, educational, and technical discussions about the broad range of industries, individuals, and applications that rely on Python.

The Python Podcast.__init__ Tobias Macey

    • Technology
    • 4.3 • 82 Ratings

The weekly podcast about the Python programming language, its ecosystem, and its community. Tune in for engaging, educational, and technical discussions about the broad range of industries, individuals, and applications that rely on Python.

    Pants Has Got Your Python Monorepo Covered

    Pants Has Got Your Python Monorepo Covered

    In a software project writing code is just one step of the overall lifecycle. There are many repetitive steps such as linting, running tests, and packaging that need to be run for each project that you maintain. In order to reduce the overhead of these repeat tasks, and to simplify the process of integrating code across multiple systems the use of monorepos has been growing in popularity. The Pants build tool is purpose built for addressing all of the drudgery and for working with monorepos of all sizes. In this episode core maintainers Eric Arellano and Stu Hood explain how the Pants project works, the benefits of automatic dependency inference, and how you can start using it in your own projects today. They also share useful tips for how to organize your projects, and how the plugin oriented architecture adds flexibility for you to customize Pants to your specific needs.

    • 51 min
    Scale Your Data Science Teams With Machine Learning Operations Principles

    Scale Your Data Science Teams With Machine Learning Operations Principles

    Building a machine learning model is a process that requires well curated and cleaned data and a lot of experimentation. Doing it repeatably and at scale with a team requires a way to share your discoveries with your teammates. This has led to a new set of operational ML platforms. In this episode Michael Del Balso shares the lessons that he learned from building the platform at Uber for putting machine learning into production. He also explains how the feature store is becoming the core abstraction for data teams to collaborate on building machine learning models. If you are struggling to get your models into production, or scale your data science throughput, then this interview is worth a listen.

    • 51 min
    Making The Case For A (Semi) Formal Specification Of CPython

    Making The Case For A (Semi) Formal Specification Of CPython

    The CPython implementation has grown and evolved significantly over the past ~25 years. In that time there have been many other projects to create compatible runtimes for your Python code. One of the challenges for these other projects is the lack of a fully documented specification of how and why everything works the way that it does. In the most recent Python language summit Mark Shannon proposed implementing a formal specification for CPython, and in this episode he shares his reasoning for why that would be helpful and what is involved in making it a reality.

    • 36 min
    Bringing Artificial Intelligence Projects From Idea To Production

    Bringing Artificial Intelligence Projects From Idea To Production

    Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.

    • 47 min
    Power Up Your Java Using Python With JPype

    Power Up Your Java Using Python With JPype

    Python and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current lead developer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen.

    • 48 min
    The Journey To Replace Python's Parser And What It Means For The Future

    The Journey To Replace Python's Parser And What It Means For The Future

    The release of Python 3.9 introduced a new parser that paves the way for brand new features. Every programming language has its own specific syntax for representing the logic that you are trying to express. The way that the rules of the language are defined and validated is with a grammar definition, which in turn is processed by a parser. The parser that the Python language has relied on for the past 25 years has begun to show its age through mounting technical debt and a lack of flexibility in defining new syntax. In this episode Pablo Galindo and Lysandros Nikolaou explain how, together with Python's creator Guido van Rossum, they replaced the original parser implementation with one that is more flexible and maintainable, why now was the time to make the change, and how it will influence the future evolution of the language.

    • 1 hr 5 min

Customer Reviews

4.3 out of 5
82 Ratings

82 Ratings

Rintel ,

Goes Deep in a Good Way

I enjoy some episodes of other Python podcasts, but I enjoy every episode of this podcast. Tobias’ expertise and experience allow for a level of depth that makes his podcast stand out.

DaButler89 ,

Love it, great well informed questions.

At first it was hard to get into the podcasts, now it’s one I look forward too. He asks really good questions. It’s easy to tell Tobus does his homework. The only suggestion I’d give is to pause for an answer between each question. Every question seems to be two distinct questions for the guests. Maybe it is so the guests can speak to what they are most comfortable, but each of the questions are great and would be good on their own. Overall, I would highly recommend to anyone with a technical bent and especially a pythonic one.

tspt61 ,

Starts hard but hang in there

I agree that the delivery is a little flat but this guy asks really thoughtful questions. I’ve been listening for over a year and like I said, keep listening. Watch your guests levels though. If the levels are too off, I move on.

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