Michael Chow
885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp
#Python #Polars #Pandas
Jeroen Janssens and Thijs Nieuwdorp are data frame library Polars’ greatest advocates in this episode with @JonKrohnLearns where they discuss their book, Python Polars: The Definitive Guide, best practice for using Polars, why Pandas users are switching to Polars for data frame operations in Python, and how the library reduces memory usage and compute time up to 10x more than Pandas. Listen to the episode to be a part of an O’Reilly giveaway!
This episode is brought to you by: • Trainium2, the latest AI chip from AWS: https://aws.amazon.com/ai/machine-learning/trainium/ • Adverity, the conversational analytics platform: https://eu1.hubs.ly/H0jxK210 • Dell AI Factory with NVIDIA: https://www.dell.com/superdatascience
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
• (00:00:00) Introduction
• (00:04:46) Why Jeroen and Thijs wrote Python Polars: The Definitive Guide
• (00:18:18) Best practices in Polars
• (00:25:08) Why Polars has so many users
• (00:32:37) The benefits of the Great Tables package
• (00:50:05) Jeroen and Thijs’ partnership with NVIDIA and Dell for Python Polars: The Definitive Guide
Additional materials: https://www.superdatascience.com/885
Build Captivating Display Tables in Python With Great Tables | Real Python Podcast #214
Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library.
Links from the show: https://realpython.com/podcasts/rpp/214/
Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library.
We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday.
This episode is sponsored by Mailtrap.
Topics:
- 00:00:00 – Introduction
- 00:02:00 – Michael’s background in open source
- 00:04:07 – Rich’s background in open source
- 00:05:27 – Advice for someone starting out
- 00:08:55 – What do you mean by the term “display” table
- 00:11:32 – What components were missing from other tables?
- 00:13:31 – Using examples to explain features
- 00:16:09 – Why was there an absence of this functionality in Python?
- 00:19:35 – A progressive approach and the grammar of tables
- 00:21:26 – Sponsor: Mailtrap
- 00:22:01 – The design philosophy of great tables
- 00:25:31 – Nanoplots, spark lines, and column spanners
- 00:27:06 – Building a gallery of examples
- 00:28:56 – Heat mapping cells and automatically adjusting text color
- 00:32:54 – Output formats for the tables
- 00:34:46 – Building in accessibility
- 00:36:55 – Dependencies
- 00:37:42 – What is the common workflow?
- 00:41:39 – Video Course Spotlight
- 00:43:15 – Adding graphics
- 00:46:41 – Using a table contest to get examples
- 00:49:47 – quartodoc and documenting the project
- 00:55:00 – Tidy Tuesday and data science community
- 01:00:29 – What are you excited about in the world of Python?
- 01:03:46 – What do you want to learn next?
- 01:08:05 – How can people follow the work you do online?
- 01:09:57 – Thanks and goodbye
Links from the show: https://realpython.com/podcasts/rpp/214/
Wrangling data for a Shiny app in Python || Michael Chow || Posit
Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack.
Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/
Content: Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)
Hey Shiny Team, what are some of your biggest learnings from 2022? || Shiny Developers || RStudio
BIG THINGS happened on the Shiny team in 2022! Our team built out a new Shiny UI Editor, Shiny for Python, and Shiny for Python in the browser using WebAssembly. So we asked some of our Developers what their biggest learnings have been from building these products!
Learn more about Shiny for Python: https://shiny.rstudio.com/py/
Content: Winston Chang (@winston_chang), Carson Sievert (@cpsievert), Nick Strayer (), Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Video editing + motion design: Tony Pelleriti (@TonyPelleriti)
