Episoder

  • Do you have data that you pull from external sources or is generated and appears at your digital doorstep? I bet that data needs processed, filtered, transformed, distributed, and much more. One of the biggest tools to create these data pipelines with Python is Dagster. And we are fortunate to have Pedram Navid on the show this episode. Pedram is the Head of Data Engineering and DevRel at Dagster Labs. And we're talking data pipelines this week at Talk Python.

    Episode sponsors

    Talk Python Courses
    Posit

    Links from the show

    Rock Solid Python with Types Course: training.talkpython.fm

    Pedram on Twitter: twitter.com
    Pedram on LinkedIn: linkedin.com
    Ship data pipelines with extraordinary velocity: dagster.io
    dagster-open-platform: github.com
    The Dagster Master Plan: dagster.io
    data load tool (dlt): dlthub.com
    DataFrames for the new era: pola.rs
    Apache Arrow: arrow.apache.org
    DuckDB is a fast in-process analytical database: duckdb.org
    Ship trusted data products faster: www.getdbt.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Have you ever been wait around for pip to do its thing while installing packages or syncing a virtual environment or through some higher level tool such as pip-tools? Then you'll be very excited to hear about the tool just announced from Astral called uv. It's like pip, but 100x faster. Charlie Marsh from Ruff fame and founder of Astral is here to dive in. Let's go.

    Episode sponsors

    Neo4j
    Talk Python Courses

    Links from the show

    Charlie Marsh on Twitter: @charliermarsh
    Charlie Marsh on Mastodon: @charliermarsh
    Astral: astral.sh
    uv: github.com
    Ruff: github.com
    Ruff Rules: docs.astral.sh
    When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev

    Talk Python's free Audio AI Course: training.talkpython.fm
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Manglende episoder?

    Klik her for at forny feed.

  • Have you heard of Quart? It's the fully-async version of Flask created by Philip Jones who is working closely with the Flask team on these parallel projects. The TL;DR; version is that if you want to take advantage of async and await and you're using Flask, you want to give Quart a solid look. We've spoken to Philip previously about Quart. This time around here's here to share his top Quart extensions and libraries you can adopt today.

    Episode sponsors

    Posit
    Talk Python Courses

    Links from the show

    Pallets Team on ExTwitter: @PalletsTeam
    Quart Framework: quart.palletsprojects.com
    Using Quart Extensions: quart.palletsprojects.com

    Quart Tasks: quart-tasks.readthedocs.io
    Quart Minify: github.com
    Quart Db: github.com
    Hypercorn: github.com
    Quart-CORS: github.com
    Quart-Auth: github.com
    Quart-Rate: github.com
    Quart-Schma: github.com
    Flask-Socket: github.com
    Quart-SqlAlchemy: github.com
    Flask-Login: github.com
    greenback: github.com
    secure: github.com
    msgspec: jcristharif.com
    Server-Sent Events: pgjones.gitlab.io
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Are you interested in contributing to Django? Then there is an amazing mentorship program that helps Python and Django enthusiasts, because contributes and potentially core developers of Django. It's called Djangonauts and their slogan is "where contributors launch." On this episode, we have Sarah Boyce from the Django team and former Djangonaut and now Djangonaut mentor, Tushar Gupta. Not only is this excellent for the Django community, many of other open source communities would do well to keep an eye on how this creative project is working.

    Episode sponsors

    Neo4j
    Posit
    Talk Python Courses

    Links from the show

    Sarah on Mastodon: @[email protected]
    Sarah on LinkedIn: linkedin.com
    Tushar on Twitter: @tushar5526
    Djangonaut Space on Mastodon: @[email protected]
    Djangonaut Space on Twitter: @djangonautspace
    Djangonaut Space on LinkedIn: linkedin.com

    Website: djangonaut.space
    Djangonaut Space Launch Video: youtube.com
    Sessions: djangonaut.space
    Djangonaut Space Interest Form: google.com/forms
    Program: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • You've built an awesome set of APIs and you have a wide array of devices and clients using them. Then you need to upgrade an end point or change them in a meaningful way. Now what? That's the conversation I dive into over the next hour with Stanislav Zmiev. We're talking about Versioning APIs.

    Episode sponsors

    Neo4j
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Stanislav Zmiev: github.com
    Monite: monite.com
    Cadwyn: github.com
    Stripe API Versioning: stripe.com
    API Versioning NOtes: github.com
    FastAPI-Versioning: github.com
    Flask-Rebar: readthedocs.io
    Django Rest Framework Versioning: django-rest-framework.org
    pytest-fixture-classes: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Building web UIs in Python has always been in interesting proposition. On one end, we have a the full web design story with artisanal HTML and CSS. On another end there are several Python platforms that aim to the bring RAD, rapid app development, style of building with Python. Those can be great, and I've covered a couple of them, but they usually reach a limit on what they can do or how they integrate with the larger web ecosystem. On this episode, we have Samuel Colvin to share his latest exciting project FastUI. With FastUI, you build responsive web applications using React without writing a single line of JavaScript, or touching npm. Yet designers and other tools can focus on React front-ends for a professional SPA like app experience.

    Episode sponsors

    bright data
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Samuel on Mastodon: fosstodon.org
    Samuel on X: x.com

    FastUI: github.com
    FastUI Demos: fastui-demo.onrender.com
    FastAPI: fastapi.tiangolo.com
    Pydantic: pydantic.dev
    How Did REST Come To Mean The Opposite of REST Article: htmx.org
    Tailwind UI: tailwindui.com
    Dropbase: dropbase.io
    Anvil: anvil.works
    Flutter code example: github.com
    ReactJS code example: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • So you've created a Python-based open source project and it's started to take off. You're getting contributors, lots of buzz in the podcast space, and more. But you have that day job working on Java. How do you make the transition from popular hobby project to full time job? After all, you are giving away your open source project for free, right? Well, on this episode, I have put together an amazing panel of guests who all have done exactly this: Turned their project into full time work and even companies in some cases. We have Samuel Colvin, Gina Häußge, Sebastián Ramírez, Charlie Marsh, Will McGugan and Eric Holscher on to share their stories.

    Episode sponsors

    Basedash
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Will McGugan: @willmcgugan
    Charlie Marsh: @charliermarsh@hachyderm
    Sebastián Ramírez: @tiangolo
    Samuel Colvin: @samuel_colvin
    Gina on Mastodon: chaos.social/@foosel
    Eric Holscher: @ericholscher

    Pydantic: pydantic.dev
    Astral (makes of Ruff): astral.sh
    Octoprint: octoprint.org
    Read the Docs: readthedocs.com
    FastAPI: fastapi.tiangolo.com
    Textual (makes of Rich): textualize.io
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters.

    Episode sponsors

    Pybites PDM
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Guests
    Anthony Shaw: @[email protected]
    Eric Snow: @[email protected]

    PEP 684 – A Per-Interpreter GIL: peps.python.org
    PEP 734 – Multiple Interpreters in the Stdlib: peps.python.org
    Running Python Parallel Applications with Sub Interpreters: fosstodon.org
    pytest subinterpreters: fosstodon.org
    Long-Term Vision for a Parallel Python Programming Model?: fosstodon.org


    Hypercorn Server: github.com
    msgspec: jcristharif.com
    Dill package: pypi.org
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel.

    Episode sponsors

    Posit
    Pybites PDM
    Talk Python Courses

    Links from the show

    Sarah's website: sckaiser.com
    Sarah on Mastodon: @[email protected]

    Get started with Python in Excel: microsoft.com
    Python in SQL Server: microsoft.com
    8 of the Biggest Excel Mistakes of All Time: blog.hurree.co
    Security and Python in Excel: microsoft.com
    Episode transcripts: talkpython.fm

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  • When you run your code in the cloud, how much do you know about where it runs? I mean, the hardware it runs on and the data center it runs in? There are just a couple of hyper-scale cloud providers in the world. This episode is a very unique chance to get a deep look inside one of them: Microsoft Azure. Azure is comprised of over 200 physical data centers, each with 100,000s of servers. A look into how code runs on them is fascinating. Our guide for this journey will be Mark Russinovich. Mark is the CTO of Microsoft Azure and a Technical Fellow, Microsoft's senior-most technical position. He's also a bit of a programming hero of mine. Even if you don't host your code in the cloud, I think you'll enjoy this conversation. Let's dive in.

    Episode sponsors

    Posit
    Pybites PDM
    Talk Python Courses

    Links from the show

    Mark Russinovich: @markrussinovich
    Mark Russinovich on LinkedIn: linkedin.com

    SysInternals: learn.microsoft.com
    Zero Day: A Jeff Aiken Novel: amazon.com
    Inside Azure Datacenters: youtube.com
    What runs chatgpt?: youtube.com
    Azure Cobalt ARM chip: servethehome.com
    Closing talk by Mark at Ignite 2023: youtube.com
    Episode transcripts: talkpython.fm

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  • Are you early in your software dev or data science career? Maybe it hasn't even really started yet and you're still in school. On this episode we have Sydney Runkle who has had a ton of success in the Python space and she hasn't even graduated yet. We sit down to talk about what she's done and might do differently again to achieve that success. It's "The Young Coder's Blueprint to Success" on episode 444 of Talk Python To Me.

    Episode sponsors

    Talk Python Courses

    Links from the show

    Sydney Runkle: linkedin.com
    Pydantic: pydantic.dev
    Code Combat: codecombat.com
    Humanitarian Toolbox: www.htbox.org
    PyCon 2024: pycon.org
    Good first issue example: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Special crossover episode of Python Bytes to wrap up 2023. Topics include:

    Michael #1: Hatch v1.8 Brian #2: svcs : A Flexible Service Locator for Python Michael #3: Steering Council 2024 Term Election Results Brian #4: Python protocols. When to use them in your projects to abstract and decoupling ExtrasJoke: Joke: The dream is dead?

    Episode sponsors

    Posit
    Talk Python Courses

  • If you're a fan of Pydantic or dataclasses, you'll definitely be interested in this episode. We are talking about a super fast data modeling and validation framework called msgspec. Some of the types in here might even be better for general purpose use than Python's native classes. Join me and Jim Crist-Harif to talk about his data exchange framework, mspspec.

    Episode sponsors

    Posit
    Talk Python Courses

    Links from the show

    Jim Crist-Harif: jcristharif.com
    Jim @ GitHub: github.com
    Jim @ Mastdon: @[email protected]

    msgspec: github.com
    Projects using msgspec: github.com
    msgspec on Conda Forge: anaconda.org
    msgspec on PyPI: pypi.org
    Litestar web framework: litestar.dev
    Litestar episode: talkpython.fm
    Pydantic V2 episode: talkpython.fm
    JSON parsing with msgspec article: pythonspeed.com

    msgspec bencharmks: jcristharif.com
    msgspec vs. pydantic v1 and pydantic v2: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • You've probably heard the term "syntactic sugar", that is, syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use. It turns out Brett Cannon has spent 2 years diving into and writing about Python's sweet language features and how they really work down inside CPython. He joins me on the show today to dive into a few of the more relevant posts he's written about it.

    Episode sponsors

    Talk Python Courses

    Links from the show

    Brett Cannon: @[email protected]

    Syntactic sugar series: snarky.ca
    Syntactic sugar: wikipedia.org
    Unravelling attribute access in Python: snarky.ca
    Unravelling binary arithmetic operations: snarky.ca
    Unravelling the import statement: snarky.ca
    record-type: pypi.org
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • We all know that LLMs and generative AI has been working its way into many products. It's Jupyter's turn to get a really awesome integration. We have David Qiu here to tell us about Jupyter AI. Jupyter AI provides a user-friendly and powerful way to apply generative AI to your notebooks. It lets you choose from many different LLM providers and models to get just the help you're looking for. And it does way more than just a chat pane in the UI. Listen to find out.

    Episode sponsors

    Posit
    Talk Python Courses

    Links from the show

    David Qiu: linkedin.com

    Jupyter AI: jupyter-ai.readthedocs.io

    Asking about something in your notebook: jupyter-ai.readthedocs.io
    Generating a new notebook: jupyter-ai.readthedocs.io
    Learning about local data: jupyter-ai.readthedocs.io
    Formatting the output: jupyter-ai.readthedocs.io
    Interpolating in prompts: jupyter-ai.readthedocs.io
    JupyterCon 2023 Talk: youtube.com
    PyData Seattle 2023 Talk: youtube.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • On this episode we have Wolf Vollprecht and Ruben Arts from the pixi project here to talk about pixi, a high performance package manager for Python and other languages that actually manages Python itself too. They have a lot of interesting ideas on where Python packaging should go and are putting their time and effort behind them. Will pixi become your next package manager? Listen in to find out.

    Episode sponsors

    Posit
    Python Tutor
    Talk Python Courses

    Links from the show

    Guests
    Wolf Vollprecht: github.com/wolfv
    Ruben Arts: github.com/ruben-arts

    pixi: prefix.dev
    Prefix: prefix.dev
    Launching pixi: prefix.dev
    Conda: docs.conda.io
    Conda Forge: conda-forge.org
    NixOS: nixos.org
    Packaging Con 2023: packaging-con.org
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Jupyter Notebooks and Jupyter Lab have to be one of the most important parts of Python when it comes to bring new users to the Python ecosystem and certainly for the day to day work of data scientists and general scientists who have made some of the biggest discoveries of recent times. And that platform has recently gotten a major upgrade with JupyterLab 4 released and Jupyter Notebook being significantly reworked to be based on the changes from JupyterLab as well. We have an excellent panel of guests, Sylvain Corlay, Frederic Collonval, Jeremy Tuloup, and Afshin Darian here to tell us what's new in these and other parts of the Jupyter ecosystem.

    Episode sponsors

    Phylum
    Python Tutor
    Talk Python Courses

    Links from the show

    Guests

    Sylvain Corlay
    Frederic Collonval
    Jeremy Tuloup
    Afshin Darian

    JupyterLab 4.0 is Here: blog.jupyter.org
    Announcing Jupyter Notebook 7: blog.jupyter.org
    JupyterCon 2023 Videos: youtube.com
    Jupyterlite: github.com
    Download JupyterLab Desktop: github.com
    Mythical Man Month Book: wikipedia.org
    Blender in Jupyter: twitter.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Are you considering or struggling with replacing much of the interactivity of your Django app with frontend JavaScript frameworks? After all, your users do expect an interactive and modern app, right? Before you make a rash decision, you owe it to yourself to check out HTMX. It goes well with Django. We have Christopher Trudeau to run through a whole awesome list of HTMX and Python and tell us about his new HTMX + Django course.

    Episode sponsors

    IRL Podcast
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Chris on ExTwitter: @cltrudeau
    Django in Action book: manning.com
    Django: djangoproject.com
    HTMX + Django course: talkpython.fm
    HTMX: htmx.org
    awesome-htmx: github.com
    awesome-python-htmx: github.com
    django-js-lib-htmx: github.com
    htmxflask: github.com
    fastapi-sse-htmx: github.com
    django-htmx-patterns: github.com
    jinja2-fragments: github.com
    jinja_partials: github.com
    chameleon_partials: github.com
    django-render-block: github.com
    flask-htmx: github.com
    htmx-flask: github.com
    asgi-htmx: github.com
    hx-requests: github.com
    django-dashboards: github.com
    A Real World React -> htmx Port: htmx.org
    3 IRL use cases for Python and HTMX: bitecode.dev
    owela-club: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • How well do you know your Python packaging tools? These are things like pip which install your project's dependencies and their dependencies and so on. In this mix, we have more modern tools such as Poetry, Flit, Hatch and others. And even tools outside of Python itself which may attempt to manage Python itself in addition to the libraries. To make sense of all of this, we welcome back Anna-Lena Popkes for an unbiased evaluation of environment and packaging tools.

    Episode sponsors

    IRL Podcast
    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Anna-Lena's website: alpopkes.com
    Anna-Lena on GitHub: github.com
    Accompanying Blog Post: alpopkes.com
    Talk from PyCon DE: youtube.com
    Talk from EuroPython: youtube.com

    Talk Python's Data Science Jumpstart with 10 Projects course: talkpython.fm

    Rye: github.com
    Poetry: python-poetry.org
    Material for MkDocs: squidfunk.github.io
    100 Days of Python in a Magical Universe Episode: talkpython.fm
    pip-tools: pip-tools.readthedocs.io
    Hatch: hatch.pypa.io
    PDM: pdm.fming.dev
    Flit: flit.pypa.io
    Conda: docs.conda.io
    Pipenv: pipenv.pypa.io
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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  • Do you worry about your developer / data science supply chain safety? All the packages for the Python ecosystem are much of what makes Python awesome. But the are also a bit of an open door to your code and machine. Luckily the PSF is taking this seriously and hired Mike Fiedler as the full time PyPI Safety & Security Engineer (not to be confused with the Security Developer in Residence staffed by Seth Michael Larson). Mike is here to give us the state of the PyPI security and plans for the future.

    Episode sponsors

    Sentry Error Monitoring, Code TALKPYTHON
    Talk Python Courses

    Links from the show

    Mike on Twitter: @mikefiedler
    Mike on Mastodon: @[email protected]

    Supply Chain examples
    SolarWinds: csoonline.com
    XcodeGhost: wikipedia.org
    Google Ad Malware: medium.com

    PyPI: pypi.org
    OWASP Top 10: owasp.org
    Trusted Publishers: docs.pypi.org
    libraries.io: libraries.io
    GitHub Full 2FA: github.blog
    Mike's Latest Blog Post: blog.pypi.org
    pprintpp package: github.com
    ICDiff: github.com
    Watch this episode on YouTube: youtube.com
    Episode transcripts: talkpython.fm

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