Replace MkDocs with Marimo and rename to scikit-play

Makefile

@@ -5,17 +5,25 @@ install:
 	python -m pip install polars pandas pytest ruff
 
 docs:
-	mkdocs serve
+	marimo edit docs.py
+
+docs-serve:
+	marimo run docs.py
+
+docs-build:
+	marimo export html-wasm docs.py -o docs/index.html
+	@echo "WASM export complete. Documentation available at docs/index.html"
 
 docs-deploy:
-	mkdocs gh-deploy
+	make docs-build
+	@echo "Documentation built for deployment. Commit and push to deploy via GitHub Pages."
 
 clean:
 	rm -rf .pytest_cache build dist scikit_lego.egg-info .ipynb_checkpoints .coverage* .mypy_cache .ruff_cache
 
 lint:
-	ruff format playtime tests
-	ruff check playtime tests --fix
+	ruff format skplay tests
+	ruff check skplay tests --fix
 
 check: lint precommit test clean
 

README.md

@@ -1,8 +1,8 @@
-# scikit-playtime
+# scikit-play
 
 > Rethinking machine learning pipelines a bit.
 
-## What does `scikit-playtime` do?
+## What does `scikit-play` do?
 
 I was wondering if there might be an easier way to construct scikit-learn pipelines. Don't get me wrong, scikit-learn is amazing when you want elaborate pipelines ([exhibit A](https://www.youtube.com/watch?v=7TZ7j4HSzmE), [exhibit B](https://www.youtube.com/watch?v=lhMqqauXtW0)) but maybe there is also a place for something more lightweight and playful. This library is all about exploring that.
 
@@ -45,12 +45,12 @@ This pipeline takes the **age**, **fare**, **sibsp** and **parch** features as-i
 
 The pipeline works, and it's fine, but you could wonder if this is *easy*. After all, you do need to know scikit-learn fairly well in order to build a pipeline this way and you may also need to appreciate Python. There's some nesting happening in here as well, so for a novice or somebody who just immediately wants to make a quick model ... there's some stuff that gets in the way. All of this is fine when you consider that scikit-learn needs to allow for elaborate pipelines ... but if you just want something dead simple ... then you may appreciate another syntax instead.
 
-## Enter playtime.
+## Enter skplay.
 
-Playtime offers an API that allows you to declare the aforementioned pipeline by doing this instead:
+Skplay offers an API that allows you to declare the aforementioned pipeline by doing this instead:
 
 ```python
-from playtime import feats, onehot
+from skplay import feats, onehot
 
 formula = feats("age", "fare", "sibsp", "parch") + onehot("sex", "pclass")
 ```
@@ -62,16 +62,16 @@ This `formula` object is just an object that can accumulate components.
 formula
 ```
 
-![playtime](docs/imgs/pipe-demo.png)
+![skplay](docs/imgs/pipe-demo.png)
 
 It's pretty much the same pipeline as before, but it's a lot easier to go ahead and declare. You're mostly dealing with column names and how to encode them, instead of thinking about how scikit-learn constructs a pipeline.
 
-This is what `scikit-playtime` is all about, but this is just the start of what it can do. If that sounds interest you can read more on the [documentation page](https://koaning.github.io/scikit-playtime/).
+This is what `scikit-play` is all about, but this is just the start of what it can do. If that sounds interest you can read more on the [documentation page](https://koaning.github.io/scikit-play/).
 
 Alternative you may also explore this tool by installing it via:
 
 ```
-python -m pip install scikit-playtime
+python -m pip install scikit-play
 ```
 
 ## Thanks