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Michael Kennedy

Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive
 deep into the popular packages and software developers, data scientists, and incredible hobbyists doing
 amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community
 by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite
 packages and the hot new ones coming out of open source.

Talk Python To Me

See the full show notes for this episode on the website at talkpython.fm/488

#488: Multimodal data with LanceDB

See the full show notes for this episode on the website at talkpython.fm/489

#489: Anaconda Toolbox for Excel and more with Peter Wang

If you're a Django developer, I'm sure you've heard so many people raving about FastAPI and Pydantic. But you really love Django and don't want to switch. Then you might want to give Django Ninja a serious look. Django Ninja is highly inspired by FastAPI, but is also deeply integrated into Django itself. We have Vitaliy Kucheryaviy the creator of Django Ninja on this show to tell us all about it.

Episode sponsors

Sentry Error Monitoring, Code TALKPYTHON

Bluehost

Talk Python Courses

Links from the show
Vitaly: github.com/vitalik

Vitaly on X: @vital1k

Top 5 Episodes of 2024: talkpython.fm/blog/posts/top-talk-python-podcast-episodes-of-2024

Django Ninja: django-ninja.dev

Motivation section we talked through: django-ninja.dev/motivation

LLM for Django Ninja: llm.django-ninja.dev

Nano Django: github.com/vitalik/nano-django

Episode #490 deep-dive: talkpython.fm/490

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

#490: Django Ninja

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

Sentry Error Monitoring, Code TALKPYTHON

Data Citizens Podcast

Talk Python Courses

Links from the show
Alex 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

#491: DuckDB and Python: Ducks and Snakes living together

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

Python in Production

Talk Python Courses

Links from the show
Michael 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

--- 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

#492: Great Tables

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 show
JJ 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

--- 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

#493: Quarto: Open-source technical publishing

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 show
Flet: 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

--- 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

#494: Update on Flet: Python + Flutter UIs

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 show
City 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

--- 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

#495: OSMnx: Python and OpenStreetMap

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 show
Calvin 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

--- 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

#496: Scaf: Complete blueprint for new Python Kubernetes projects

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 show
Data-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

--- 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

#497: Outlier Detection with Python

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 show
Reuven: 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

--- 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

#503: The PyArrow Revolution

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