Michael Chow(@chowthedog) 's Twitter Profileg
Michael Chow

@chowthedog

cog psych phd. statstronaut. stitch witch. (he / him)

data science tools @RStudio.
senior advisor @CodeForPhilly.

previously: @DataCamp, @Princeton

ID:320543516

linkhttp://mchow.com calendar_today20-06-2011 03:11:01

1,1K Tweets

1,5K Followers

596 Following

The {gt}/great_tables Packages(@gt_package) 's Twitter Profile Photo

{gt} has a 🐍-based cousin now, in the form of the Great Tables (v0.1.0) package. It’s a *great* new Python package that simplifies the process of making tabular data presentable for any sort of publication task. Check out the docs site at posit-dev.github.io/great-tables/ and talk to us!!

{gt} has a 🐍-based cousin now, in the form of the Great Tables (v0.1.0) package. It’s a *great* new Python package that simplifies the process of making tabular data presentable for any sort of publication task. Check out the docs site at posit-dev.github.io/great-tables/ and talk to us!!
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Arthur Turrell(@arthurturrell) 's Twitter Profile Photo

Sharing this because it's not getting nearly enough love: quartodoc, by Michael Chow

Build quarto websites integrated with Sphinx autodoc. This means hosting documentation that is beautiful & consistent with code just got a lot easier


github.com/machow/quartod…

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Davis Vaughan(@dvaughan32) 's Twitter Profile Photo

dplyr 1.1.0 is coming soon!! πŸŽ‰πŸŽ‰

We are so excited to introduce you to the new features we've been working on, including:
- Temporary inline grouping with `.by`
- Non-equi joins
- Faster `arrange()`

And SO much more!

tidyverse.org/blog/2022/11/d…

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Michael Chow(@chowthedog) 's Twitter Profile Photo

Hello! I maintain a port of dplyr to python called siuba.

If you'll be at and are interested, would love to chat!

siuba.org

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Community Legal Services of Philadelphia(@CLSphila) 's Twitter Profile Photo

As this article by Nate File shows, small steps go a long way toward making powerful change. With minor tweaks & innovation by people with tech expertise from #SJHPHL Philly's First Social Justice Hackathon, we can use our legal expertise to serve more people who are struggling to get by. inquirer.com/news/philadelp…

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Rachael Tatman @rctatman@mastodon.rctatman.com(@rctatman) 's Twitter Profile Photo

Hello friends! For my livestream tomorrow I'm going to be trying out Michael Chow's Siuba package for the first time. Come join us! :)

youtube.com/watch?v=VEc9IJ…

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polars data(@DataPolars) 's Twitter Profile Photo

Next release of polars will ship with automatic subplan elimination. Complicated self-referencing plans will likely run much faster if your threads were contending.

LazyFrame.cache() is an operation a user can start to forget.

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Kyle Barron @kylebarron@mapstodon.space(@kylebarron2) 's Twitter Profile Photo

Imagine a geospatial DataFrame library that's

- written in Rust
- binding to Python and WebAssembly
- multithreaded
- built on GeoArrow

With Stuart Lynn I've been working on a *prototype* of achieving this by extending polars data for geospatial support
github.com/geopolars/geop…

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Shannon Pileggi... @PipingHotData@fosstodon.org(@PipingHotData) 's Twitter Profile Photo

πŸ‘‹Hi! I am a real live person on the global teams who helps keep the We are R-Ladies curator account active 😎

I love hearing & learning from all over the world πŸŒŽπŸ’œπŸŒπŸ’œπŸŒ

If you do too, please sign up ✍️ or nominate someone whose work you admire 😍!

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Hadley Wickham(@hadleywickham) 's Twitter Profile Photo

If you're like to see my rstudio::conf() talk about R7, its now online: rstudio.com/conference/202…. R7 is a new OO system that takes the best parts from S3 and S4. It's designed by folks from R Core, BioC, ROpenSci, RStudio, and more

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polars data(@DataPolars) 's Twitter Profile Photo

We ran the first 7 TPCH queries on scale factor 10 to compare different dataframe solutions on a single machine.

This shows that scaling doesn't always make it faster. See that small purple bar? That's how much faster polars is!

We ran the first 7 TPCH queries on scale factor 10 to compare different #python dataframe solutions on a single machine. This shows that scaling #pandas doesn't always make it faster. See that small purple bar? That's how much faster polars is!
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