#330: Apache Airflow Open-Source Workflow with Python

Do you know what custom GPTs are? They're configurable and shareable chat experiences with a name, logo, custom instructions, conversation starters, access to OpenAI tools, and custom API actions. And, you can build them with Python! Ian Maurer has been doing just that and is here to share his experience building them. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Neo4j Talk Python Courses Links from the show Ian on Twitter: @imaurer Mobile Navigation: openai.com What is a Custom GPT?: imaurer.com Mobile Navigation: openai.com FuzzTypes: Pydantic library for auto-correcting types: github.com pypi-gpt: github.com marvin: github.com instructor: github.com outlines: github.com llamafile: github.com llama-cpp-python: github.com LLM Dataset: llm.datasette.io Plugin directory: llm.datasette.io Data exploration at your fingertips.: visidata.org hottest new programming language is English: twitter.com OpenAI & other LLM API Pricing Calculator: docsbot.ai Vector DB Comparison: vdbs.superlinked.com bpytop: github.com Source Graph: about.sourcegraph.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Interested in data science but you're not quite working in it yet? In software, getting that very first job can truly be the hardest one to land. On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice for getting your first data job. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Posit Talk Python Courses Links from the show Avery Smith: www.linkedin.com Data Career Jumpstart: www.datacareerjumpstart.com Data Nerd Site: datanerd.tech Write C# LINQ queries to query data: learn.microsoft.com A faster way to build and share data apps: streamlit.io Plotly Dash: dash.plotly.com Michael's Keynote: State of Python in 2024: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

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 --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy