Talk Python To Me

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.

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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

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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.

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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

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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
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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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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.

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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

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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

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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

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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?

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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

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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

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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

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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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