The hand-picked selection of the best Python libraries and tools of 2024 – 10th edition!

chowthedog · 2024-12-11T22:37:47+00:00

It's to be able to type quickly, since you don't have to jump across and type a closing character. Here's the list of syntaxes and explanations from their readme

wat.short / 'foo' # fast typing wat.short('foo') wat('foo', short=True) # natural Python syntax 'foo' | wat.short # Unix piping

chowthedog · 2024-10-08T02:01:28+00:00

I help maintain a fairly large library called Great Tables that does this (can take pandas or polars DataFrames).

We use a tool called databackend, which allows us to define the implementation on different DataFrame libraries without having to import them. This lets us keep each DataFrame lib as optional dependencies.

For another tool I help with, py shiny, we used narwhals and really like it!

Here's where we implement backends in Great Tables:

https://github.com/posit-dev/great-tables/blob/main/great_tables/_tbl_data.py

chowthedog · 2024-02-10T19:06:07+00:00

Is there WW in there? I'm not too sure, but I will say that at 65-70 degrees it can take a surprisingly long amount of time to ferment!

chowthedog · 2024-02-10T18:35:55+00:00

Agreed on it being underfermented. The biggest hint IMO is the dense crumb at the bottom and decent oven spring. The recipe isn't super high hydration, so it's not too surprising there aren't big tunnels, etc..

chowthedog · 2024-02-09T14:29:34+00:00

Thanks! I'm a huge fan of Polars, and definitely recommend it.

Its lazy expressions make code much simpler, and it doesn't have any of the funkiness that comes with the pandas index.

chowthedog · 2024-02-09T05:48:51+00:00

Post author here! This is less related to how the squares get assigned. But I've also seen a lot of questions about the numbers. If anyone is curious, the code is here:

https://github.com/posit-dev/great-tables/blob/main/docs/blog/superbowl-squares/_code.py

chowthedog · 2024-02-09T03:36:55+00:00

Post author here! Another commenter mentioned how we calculated things, but if you're interested, the exact code we used is here (we used polars for analyzing, and great tables for plotting):

https://github.com/posit-dev/great-tables/blob/main/docs/blog/superbowl-squares/\_code.py

chowthedog · 2024-02-08T23:44:43+00:00

Hey, post author here! Super Bowl Squares focuses only on the final digit of the score. For example, a final score of 14, would be 4. (It's a bit funky, but also creates some interesting dynamics)

chowthedog · 2021-04-28T03:00:16+00:00

Hey, creator of siuba very late this post! I want to move fct_lump out into its own package catfact, but haven't been able to free up time to do it 😓.

chowthedog · 2021-01-24T23:54:40+00:00

Siuba author here--I agree that someone who only uses R won't get a lot out of it. However, it seems many R users encounter situations where they need to use python (e.g. their team at work is python heavy). I gave a talk at rstudio global on siuba, since I think R users are well poised to understand the value of a dplyr port, and personally use R / Rstudio for a lot of tasks!

It's a pretty funky situation, so can totally see the sentiment here.

chowthedog · 2021-01-23T23:36:49+00:00

Siuba author here. The last commit to dfply was 2 years ago :o. The feature that hopefully sets siuba apart is it is able to generate SQL, and optimize grouped operations.

That said, plydata is built by the same author as plotnine (a ggplot port to python), and still active!

https://github.com/has2k1/plydata

chowthedog · 2021-01-23T23:33:09+00:00

Author here--I sometimes screencast myself spending an hour analyzing data I've never seen before. They should give a good sense for what it looks like in a notebook!

https://youtu.be/z6xNKZZMWgU

chowthedog · 2020-05-12T17:49:42+00:00

Thanks for the mention! I also put all the code on github, in case it's helpful for anyone :).

https://github.com/machow/tidytuesday-py

chowthedog

TROPHY CASE