Michael Waskom
@michaelwaskom
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Machine pedagogue and data aesthete. Creator of https://t.co/imXawO1Iwk. Currently @modal_labs, building a new way to develop in the cloud.
New York, NY
Joined March 2012
Some professional news: excited to say that I’ve joined @modal_labs to help build a completely new approach for developing data and AI applications in the cloud!
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My talk on the past and future of seaborn from #positconf2023 is now available on YouTube
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Logistic regression is AI when it’s regularized by default ;)
LOL, techcrunch just called @scikit_learn, XGBoost and the full data science stack "good old fashioned AI". I don't know who'll be more upset, @GaryMarcus or @tunguz and @GaelVaroquaux? https://t.co/Mh0cgSK4Vj
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This is a well targeted ad in the sense that I also mostly use my Mac to make @matplotlib plots
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@fperez_org It’s interesting to look back at the example notebook linked there, which has many of the core ideas almost as they are today
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But then it got a little blip of attention on twitter from influential people like @fperez_org and a release felt harder to avoid (seaborn is so old, from the age of manual quote tweets)
Well, I guess I should release Seaborn... RT @vandy_biostat @fperez_org Graphical Representations of Linear Models http://t.co/bBJFq0uFHr
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As a codebase, seaborn had already existed for ≈1.5 years at this point, but I had been pretty reluctant about committing to any “official” releases / maintenance ( https://t.co/mCyrUsOURz). I find that surprises people who ask me about how it started.
github.com
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Apparently it is ten years today since the first release of seaborn on PyPI 🎂
Since it's been getting attention, I tagged and released seaborn 0.1. You can get it with `pip install seaborn moss` (moss has stat utils)
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There’s a fresh new release of seaborn available on PyPI: pip install seaborn==0.13.0 New features include some major improvements to categorical plots, support for alternate dataframe libraries, default theme/display configuration for so.Plot, and lots more.
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Please help test the release candidate for seaborn v0.13.0. pip install --pre seaborn==0.13.0rc0 This is a major update with some deep changes and help surfacing any issues before the final release would be really appreciated.
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See more details about the rc on github: https://t.co/DjwkRVjC4V Current release notes are here:
github.com
This is a release candidate for seaborn v0.13.0, a major release with a complete overhaul of seaborn's categorical plotting functions. Please test the release candidate, especially the categori...
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Please help test the release candidate for seaborn v0.13.0. pip install --pre seaborn==0.13.0rc0 This is a major update with some deep changes and help surfacing any issues before the final release would be really appreciated.
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Sure, why not
Python in Excel (in Beta) @Microsoft🤝@anacondainc Default imported libraries: matplotlib numpy pandas seaborn statsmodels only for Windows, needs internet access, code executed on MS servers without network or file access see https://t.co/ff3zAOyDRn & https://t.co/vGtN23d930
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People sometimes ask what the “sns” is for, well, here you go, the truth is finally revealed
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Questions on public fora are also an important source of feedback for software creators about where users are getting confused or running up against limitations. A closed loop with Bard or ChatGPT is maybe better for them in the short term, but what about over time?
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Like, I think it’s cool that GPT seems to know a lot about seaborn, and also really annoying that it knows nothing about improvements I’ve worked hard to introduce in the past two years.
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This probably underrates the raw human drive to poast, but I think it’s a more realistic and imminent risk than most doom scenarios. It’s probably even more urgent in the domain of software…
The disconnection between knowledge creators & knowledge consumers in Gen AI models is a pressing problem that's not yet discussed enough Nobody will be excited to share knowledge on the internet if the default way to consume it becomes through anonymizing/unreferencing chatbots
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The disconnection between knowledge creators & knowledge consumers in Gen AI models is a pressing problem that's not yet discussed enough Nobody will be excited to share knowledge on the internet if the default way to consume it becomes through anonymizing/unreferencing chatbots
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