Episódios
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Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course.
Episode sponsors
Agntcy
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses
Links from the showNew Theme Song (Full-Length Download and backstory): talkpython.fm/blog
Polars for Power Users Course: training.talkpython.fm
Awesome Polars: github.com
Polars Visualization with Plotly: docs.pola.rs
Dataframely: github.com
Patito: github.com
polars_iptools: github.com
polars-fuzzy-match: github.com
Nucleo Fuzzy Matcher: github.com
polars-strsim: github.com
polars_encryption: github.com
polars-xdt: github.com
polars_ols: github.com
Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org
polars-pairing: github.com
Pairing Function: en.wikipedia.org
polars_list_utils: github.com
Harley Schema Helpers: tomburdge.github.io
Marimo Reactive Notebooks Episode: talkpython.fm
Marimo: marimo.io
Ahoy Narwhals Podcast Episode Links: talkpython.fm
Watch this episode on YouTube: youtube.com
Episode #510 deep-dive: talkpython.fm/510
Episode transcripts: talkpython.fm
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Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python.
Episode sponsors
Posit
Agntcy
Talk Python Courses
Links from the showBryce Adelstein Lelbach on Twitter: @blelbach
Episode Deep Dive write up: talkpython.fm/blog
NVIDIA CUDA Python API: github.com
Numba (JIT Compiler for Python): numba.pydata.org
Applied Data Science Podcast: adspthepodcast.com
NVIDIA Accelerated Computing Hub: github.com
NVIDIA CUDA Python Math API Documentation: docs.nvidia.com
CUDA Cooperative Groups (CCCL): nvidia.github.io
Numba CUDA User Guide: nvidia.github.io
CUDA Python Core API: nvidia.github.io
Numba (JIT Compiler for Python): numba.pydata.org
NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com
Google Colab: colab.research.google.com
Compiler Explorer (“Godbolt”): godbolt.org
CuPy: github.com
RAPIDS User Guide: docs.rapids.ai
Watch this episode on YouTube: youtube.com
Episode #509 deep-dive: talkpython.fm/509
Episode transcripts: talkpython.fm
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Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
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If you've heard the phrase "Automate the boring things" for Python, this episode starts with that idea and takes it to another level. We have Glyph back on the podcast to talk about "Programming YOUR computer with Python." We dive into a bunch of tools and frameworks and especially spend some time on integrating with existing platform APIs (e.g. macOS's BrowserKit and Window's COM APIs) to build desktop apps in Python that make you happier and more productive. Let's dive in!
Episode sponsors
Posit
Agntcy
Talk Python Courses
Links from the showGlyph on Mastodon: @[email protected]
Glyph on GitHub: github.com/glyph
Glyph's Conference Talk: LceLUPdIzRs: youtube.com
Notify Py: ms7m.github.io
Rumps: github.com
QuickMacHotkey: pypi.org
QuickMacApp: pypi.org
LM Studio: lmstudio.ai
Coolify: coolify.io
PyWin32: pypi.org
WinRT: pypi.org
PyObjC: pypi.org
PyObjC Documentation: pyobjc.readthedocs.io
Watch this episode on YouTube: youtube.com
Episode #508 deep-dive: talkpython.fm/508
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph.
Episode sponsors
Posit
Auth0
Talk Python Courses
Links from the showSydney Runkle: linkedin.com
LangGraph: github.com
LangChain: langchain.com
LangGraph Studio: github.com
LangGraph (Web): langchain.com
LangGraph Tutorials Introduction: langchain-ai.github.io
How to Think About Agent Frameworks: blog.langchain.dev
Human in the Loop Concept: langchain-ai.github.io
GPT-4 Prompting Guide: cookbook.openai.com
Watch this episode on YouTube: youtube.com
Episode #507 deep-dive: talkpython.fm/507
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
The folks over at Astral have made some big-time impacts in the Python space with uv and ruff. They are back with another amazing project named ty. You may have known it as Red-Knot. But it's coming up on release time for the first version and with the release it comes with a new official name: ty. We have Charlie Marsh and Carl Meyer on the show to tell us all about this new project.
Episode sponsors
Posit
Auth0
Talk Python Courses
Links from the showTalk Python's Rock Solid Python: Type Hints & Modern Tools (Pydantic, FastAPI, and More) Course: training.talkpython.fm
Charlie Marsh on Twitter: @charliermarsh
Charlie Marsh on Mastodon: @charliermarsh
Carl Meyer: @carljm
ty on Github: github.com/astral-sh/ty
A Very Early Play with Astral’s Red Knot Static Type Checker: app.daily.dev
Will Red Knot be a drop-in replacement for mypy or pyright?: github.com
Hacker News Announcement: news.ycombinator.com
Early Explorations of Astral’s Red Knot Type Checker: pydevtools.com
Astral's Blog: astral.sh
Rust Analyzer Salsa Docs: docs.rs
Ruff Open Issues (label: red-knot): github.com
Ruff Types: types.ruff.rs
Ruff Docs (Astral): docs.astral.sh
uv Repository: github.com
Watch this episode on YouTube: youtube.com
Episode #506 deep-dive: talkpython.fm/506
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature.
Episode sponsors
Posit
Auth0
Talk Python Courses
Links from the showGuests:
Paul on X: @paulweveritt
Paul on Mastodon: @[email protected]
Dave Peck on Github: github.com
Jim Baker: github.com
PEP 750 – Template Strings: peps.python.org
tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com
PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org
How To Teach This: peps.python.org
PEP 501 – General purpose template literal strings: peps.python.org
Python's new t-strings: davepeck.org
PyFormat: Using % and .format() for great good!: pyformat.info
flynt: A tool to automatically convert old string literal formatting to f-strings: github.com
Examples of using t-strings as defined in PEP 750: github.com
htm.py issue: github.com
Exploits of a Mom: xkcd.com
pyparsing: github.com
Watch this episode on YouTube: youtube.com
Episode #505 deep-dive: talkpython.fm/505
Episode transcripts: talkpython.fm
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Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
What trends and technologies should you be paying attention to today? Are there hot new database servers you should check out? Or will that just be a flash in the pan? I love these forward looking episodes and this one is super fun. I've put together an amazing panel: Gina Häußge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker. We dive into the recent Stack Overflow Developer survey results as a sounding board for our thoughts on rising and falling trends in the Python and broader developer space.
Episode sponsors
NordLayer
Auth0
Talk Python Courses
Links from the showThe Stack Overflow Survey Results: survey.stackoverflow.co/2024
Panelists
Gina Häußge: chaos.social/@foosel
Ines Montani: ines.io
Richard Campbell: about.me/richard.campbell
Calvin Hendryx-Parker: github.com/calvinhp
Explosion: explosion.ai
spaCy: spacy.io
OctoPrint: octoprint.org
.NET Rocks: dotnetrocks.com
Six Feet Up: sixfeetup.com
Stack Overflow: stackoverflow.com
Python.org: python.org
GitHub Copilot: github.com
OpenAI ChatGPT: chat.openai.com
Claude: anthropic.com
LM Studio: lmstudio.ai
Hetzner: hetzner.com
Docker: docker.com
Aider Chat: github.com
Codename Goose AI: block.github.io/goose/
IndyPy: indypy.org
OctoPrint Community Forum: community.octoprint.org
spaCy GitHub: github.com
Hugging Face: huggingface.co
Watch this episode on YouTube: youtube.com
Episode #504 deep-dive: talkpython.fm/504
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Pandas is at a the core of virtually all data science done in Python, that is virtually all data science. Since it's beginning, Pandas has been based upon numpy. But changes are afoot to update those internals and you can now optionally use PyArrow. PyArrow comes with a ton of benefits including it's columnar format which makes answering analytical questions faster, support for a range of high performance file formats, inter-machine data streaming, faster file IO and more. Reuven Lerner is here to give us the low-down on the PyArrow revolution.
Episode sponsors
NordLayer
Auth0
Talk Python Courses
Links from the showReuven: github.com/reuven
Apache Arrow: github.com
Parquet: parquet.apache.org
Feather format: arrow.apache.org
Python Workout Book (45% off with code talkpython45): manning.com
Pandas Workout Book (45% off with code talkpython45): manning.com
Pandas: pandas.pydata.org
PyArrow CSV docs: arrow.apache.org
Future string inference in Pandas: pandas.pydata.org
Pandas NA/nullable dtypes: pandas.pydata.org
Pandas `.iloc` indexing: pandas.pydata.org
DuckDB: duckdb.org
Pandas user guide: pandas.pydata.org
Pandas GitHub issues: github.com
Watch this episode on YouTube: youtube.com
Episode #503 deep-dive: talkpython.fm/503
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Do you or your company need accounting software? Well, there are plenty of SaaS products out there that you can give your data to. but maybe you also really like Django and would rather have a foundation to build your own accounting system exactly as you need for your company or your product. On this episode, we're diving into Django Ledger, created by Miguel Sanda, which can do just that.
Episode sponsors
Auth0
Talk Python Courses
Links from the showMiguel Sanda on Twitter: @elarroba
Miguel on Mastodon: @[email protected]
Miguel on GitHub: github.com
Django Ledger on Github: github.com
Django Ledger Discord: discord.gg
Get Started with Django MongoDB Backend: mongodb.com
Wagtail CMS: wagtail.org
Watch this episode on YouTube: youtube.com
Episode #502 deep-dive: talkpython.fm/502
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Have you ever spent an afternoon wrestling with a Jupyter notebook, hoping that you ran the cells in just the right order, only to realize your outputs were completely out of sync? Today's guest has a fresh take on solving that exact problem. Akshay Agrawal is here to introduce Marimo, a reactive Python notebook that ensures your code and outputs always stay in lockstep. And that's just the start! We'll also dig into Akshay's background at Google Brain and Stanford, what it's like to work on the cutting edge of AI, and how Marimo is uniting the best of data science exploration and real software engineering.
Episode sponsors
Worth Search
Talk Python Courses
Links from the showAkshay Agrawal: akshayagrawal.com
YouTube: youtube.com
Source: github.com
Docs: marimo.io
Marimo: marimo.io
Discord: marimo.io
WASM playground: marimo.new
Experimental generate notebooks with AI: marimo.app
Pluto.jl: plutojl.org
Observable JS: observablehq.com
Watch this episode on YouTube: youtube.com
Episode #501 deep-dive: talkpython.fm/501
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
We're sitting down with Eric Matthes, the educator, author, and developer behind Django Simple Deploy. If you've ever struggled with taking that final step of getting your Django app onto a live server (without spending days wrestling with DevOps complexities), then give Django Simple Deploy a look. Eric shares how Django Simple Deploy automates away the boilerplate parts of deployment, so you can focus on building features instead of deciphering endless configs. We'll talk about this new project's journey to 1.0, the range of hosting platforms it supports, and why it's not just for beginners.
Episode sponsors
Worth Search
Talk Python Courses
Links from the showdjango-simple-deploy documentation: readthedocs.io
django-simple-deploy repository: github.com
Python Crash Course book: ehmatthes.github.io
Code Red: codered.cloud
Docker: docker.com
Caddy: caddyserver.com
Bunny.net CDN: bunny.net
Platform.sh: platform.sh
fly.io: fly.io
Heroku: heroku.com
Watch this episode on YouTube: youtube.com
Episode #500 deep-dive: talkpython.fm/500
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
This episode is all about Beeware, the project that working towards true native apps built on Python, especially for iOS and Android. Russell's been at this for more than a decade, and the progress is now hitting critical mass. We'll talk about the Toga GUI toolkit, building and shipping your apps with Briefcase, the newly official support for iOS and Android in CPython, and so much more. I can't wait to explore how BeeWare opens up the entire mobile ecosystem for Python developers, let's jump right in.
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showAnaconda open source team: anaconda.com
PEP 730 – Adding iOS: peps.python.org
PEP 738 – Adding Android: peps.python.org
Toga: beeware.org
Briefcase: beeware.org
emscripten: emscripten.org
Russell Keith-Magee - Keynote - PyCon 2019: youtube.com
Watch this episode on YouTube: youtube.com
Episode #499 deep-dive: talkpython.fm/499
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
In this episode, we welcome back Will McGugan, the creator of the wildly popular Rich library and founder of Textualize. We'll dive into Will's latest article on "Algorithms for High Performance Terminal Apps" and explore how he's quietly revolutionizing what's possible in the terminal, from smooth animations and dynamic widgets to full-on TUI (or should we say GUI?) frameworks. Whether you're looking to supercharge your command-line tools or just curious how Python can push the limits of text-based UIs, you'll love hearing how Will's taking a modern, web-inspired approach to old-school terminals.
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showAlgorithms for high performance terminal apps post: textual.textualize.io
Textual Demo: github.com
Textual: textualize.io
Zero ver: 0ver.org
memray: github.com
Posting app: posting.sh
Bulma CSS framewokr: bulma.io
JP Term: davidbrochart.github.io
Rich: github.com
btop: github.com
starship: starship.rs
Watch this episode on YouTube: youtube.com
Episode #498 deep-dive: talkpython.fm/498
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Have you ever wondered why certain data points stand out so dramatically? They might hold the key to everything from fraud detection to groundbreaking discoveries. This week on Talk Python to Me, we dive into the world of outlier detection with Python with Brett Kennedy. You'll learn how outliers can signal errors, highlight novel insights, or even reveal hidden patterns lurking in the data you thought you understood. We'll explore fresh research developments, practical use cases, and how outlier detection compares to other core data science tasks like prediction and clustering. If you're ready to spot those game-changing anomalies in your own projects, stay tuned.
Discount code for Outlier Detection in Python book: talkpython45 (45% off, no expiration date).
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showData-morph: github.com
PyOD: github.com
Prophet: github.com
Outlier Detection in Python Book: manning.com
Episode #497 deep-dive: talkpython.fm/497
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Today we explore the wild world of Python deployment with my friend, Calvin Hendryx-Parker from Six Feet Up. We’ll tackle some of the biggest challenges in taking a Python app from “it works on my machine” to production, covering inconsistent environments, conflicting dependencies, and sneaky security pitfalls. Along the way, Calvin shares how containerization with Docker and Kubernetes can both simplify and complicate deployments, especially for smaller teams. Finally, we’ll introduce Scaf, a powerful project blueprint designed to give developers a rock-solid start on Python web projects of all sizes.
Get notified when the Talk Python in Production book goes live and read the first third online right now.
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showCalvin Hendryx-Parker: github.com
Scaf on GitHub: github.com
Scaf on GitHub (duplicate): github.com
"Deploy the Dream" song: deploy-the-dream-talk-python.mp3
CloudDevEngineering YouTube Channel: youtube.com
TechWorld with Nana YouTube Channel: youtube.com
Tilt (Kubernetes Dev Tool): tilt.dev
Talos (Minimal OS for Kubernetes): talos.dev
Traefik Reverse Proxy: traefik.io
Sealed Secrets on GitHub: github.com
Argo CD Documentation: readthedocs.io
MailHog on GitHub: github.com
Next.js: nextjs.org
Cloud Custodian: cloudcustodian.io
Valkey (Redis Replacement): valkey.io
“The ‘Works on My Machine’ Certification Program” (Coding Horror): blog.codinghorror.com
NVIDIA’s First Desktop AI PC (Ars Technica): arstechnica.com
Kind (Kubernetes in Docker): kind.sigs.k8s.io
Updated Effective PyCharm Course: training.talkpython.fm
Talk Python in Production book: talkpython.fm/books/python-in-production
Watch this episode on YouTube: youtube.com
Episode #496 deep-dive: talkpython.fm/496
Episode transcripts: talkpython.fm
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Talk Python on Bluesky: @talkpython.fm at bsky.app
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Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
On this episode, I'm joined by Dr. Geoff Boeing, an assistant professor at the University of Southern California whose research spans urban planning, spatial analysis, and data science. We explore why OpenStreetMap is such a powerful source of global map data—and how Geoff's Python library, OSMnx, makes that data easier to download, model, and visualize. Along the way, we talk about what shapes city streets around the world, how urban design influences everything from daily commutes to disaster resilience, and why turning open data into accessible tools can open up completely new ways of understanding our cities. If you've ever wondered how to build or analyze your own digital maps in Python, or what it takes to manage a project that transforms raw geographic data into meaningful research, you won't want to miss this conversation.
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showCity Street Orientations World: geoffboeing.com
OSMnx Documentation: readthedocs.io
OSMnx GitHub: github.com
OpenStreetMap: openstreetmap.org
Open Database License: opendatacommons.org
ID Editor (Web Editor): wiki.openstreetmap.org
Planet OSM: planet.openstreetmap.org
Overpass API: wiki.openstreetmap.org
GeoPandas: geopandas.org
NetworkX: networkx.org
Shapely: shapely.readthedocs.io
Watch this episode on YouTube: youtube.com
Episode #495 deep-dive: talkpython.fm/495
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
As Python developers, we're incredibly lucky to have over half a million packages that we can use to build our applications with over at PyPI. However, when it comes to choosing a UI framework, the options get narrowed down very quickly. Intersect those choices with the ones that work on mobile, and you have a very short list. Flutter is a UI framework for building desktop and mobile applications, and is in fact the one that we used to build the Talk Python courses app, you'd find at talkpython.fm/apps. That's why I'm so excited about Flet. Flet is a Python UI framework that is distributed and executed on the Flutter framework, making it possible to build mobile apps and desktop apps with Python. We have Feodor Fitsner back on the show after he launched his project a couple years ago to give us an update on how close they are to a full featured mobile app framework in Python.
Episode sponsors
Posit
Python in Production
Talk Python Courses
Links from the showFlet: flet.dev
Flet on Github: github.com
Packaging apps with Flet: flet.dev/docs/publish
Flutter: flutter.dev
React vs. Flutter: trends.stackoverflow.co
Kivy: kivy.org
Beeware: beeware.org
Mobile forge from Beeware: github.com
The list of built-in binary wheels: flet.dev/docs/publish/android#binary-python-packages
Difference between dynamic and static Flet web apps: flet.dev/docs/publish/web
Integrating Flutter packages: flet.dev/docs/extend/integrating-existing-flutter-packages
serious_python: pub.dev/packages/serious_python
Watch this episode on YouTube: youtube.com
Episode #494 deep-dive: talkpython.fm/494
Episode transcripts: talkpython.fm
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Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
In this episode, I'm joined by JJ Allaire, founder and executive chairman at Posit, and Carlos Scheidegger, a software engineer at Posit, to explore Quarto, an open-source tool revolutionizing technical publishing. We discuss how Quarto empowers users to seamlessly transform Jupyter notebooks into polished reports, dashboards, e-books, websites, and more. JJ shares his journey from creating RStudio to developing Quarto as a versatile, multi-language tool, while Carlos delves into its roots in reproducibility and the challenges of academic publishing. Don't miss this deep dive into a tool that's shaping the future of data-driven storytelling!
Episode sponsors
Talk Python Courses
Podcast Later
Links from the showJJ Allaire
JJ on LinkedIn: linkedin.com
JJ on GitHub: github.com
Carlos Scheidegger
Personal site: cscheid.net
Mastodon: @scheidegger
Fast AI: fast.ai
nbdev: nbdev.fast.ai
nbsanity - Share Notebooks as Polished Web Pages in Seconds: answer.ai
Pandoc: pandoc.org
Observable: github.com
Quarto Pub: quartopub.com
Deno: deno.com
Real World Data Science site: realworlddatascience.net
Typst: typst.app
Github Actions for Quarto: github.com
Watch this episode on YouTube: youtube.com
Episode #493 deep-dive: talkpython.fm/493
Episode transcripts: talkpython.fm
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Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy -
Join me as I chat with Rich Iannone and Michael Chow from Posit where we explore the transformative power of data tables with the Great Tables library. We'll cover practical applications of Great Tables, showcasing how thoughtful design and advanced formatting can elevate your data presentations. And you'll learn about innovative features like nano plots and interactive elements and the importance of structure, format, and style in crafting tables that both inform and inspire. Whether you're a seasoned data scientist or just starting out, this episode is packed with valuable tips and inspiring examples to enhance your data storytelling.
Episode sponsors
Posit
Talk Python Courses
Links from the showMichael Chow: github.com/machow
Richard Iannone: github.com/rich-iannone
Episode Deep Dives Writeup: talkpython.fm/blog
Great Tables: github.com
Making Beautiful, Publication Quality Tables PyCon talk: youtube.com
Andrew Weatherman's Visualization Gallery: aweatherman.com
Bureau of the Census Manual of Tabular Presentation: census.gov
Table Contest: posit.co
Watch this episode on YouTube: youtube.com
Episode #492 deep-dive: talkpython.fm/492
Episode transcripts: talkpython.fm
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Join me for an insightful conversation with Alex Monahan, who works on documentation, tutorials, and training at DuckDB Labs. We explore why DuckDB is gaining momentum among Python and data enthusiasts, from its in-process database design to its blazingly fast, columnar architecture. We also dive into indexing strategies, concurrency considerations, and the fascinating way MotherDuck (the cloud companion to DuckDB) handles large-scale data seamlessly. Don’t miss this chance to learn how a single pip install could totally transform your Python data workflow!
Episode sponsors
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Data Citizens Podcast
Talk Python Courses
Links from the showAlex on Mastodon: @__Alex__
DuckDB: duckdb.org
MotherDuck: motherduck.com
SQLite: sqlite.org
Moka-Py: github.com
PostgreSQL: www.postgresql.org
MySQL: www.mysql.com
Redis: redis.io
Apache Parquet: parquet.apache.org
Apache Arrow: arrow.apache.org
Pandas: pandas.pydata.org
Polars: pola.rs
Pyodide: pyodide.org
DB-API (PEP 249): peps.python.org/pep-0249
Flask: flask.palletsprojects.com
Gunicorn: gunicorn.org
MinIO: min.io
Amazon S3: aws.amazon.com/s3
Azure Blob Storage: azure.microsoft.com/products/storage
Google Cloud Storage: cloud.google.com/storage
DigitalOcean: www.digitalocean.com
Linode: www.linode.com
Hetzner: www.hetzner.com
BigQuery: cloud.google.com/bigquery
DBT (Data Build Tool): docs.getdbt.com
Mode: mode.com
Hex: hex.tech
Python: www.python.org
Node.js: nodejs.org
Rust: www.rust-lang.org
Go: go.dev
.NET: dotnet.microsoft.com
Watch this episode on YouTube: youtube.com
Episode #491 deep-dive: talkpython.fm/491
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy - Mostrar mais
