Add readme specific to python egobox binding

python/Cargo.toml

@@ -7,6 +7,7 @@ edition.workspace = true
 homepage.workspace = true
 description = "A python binding for egobox crates"
 repository = "https://github.com/relf/egobox/python"
+readme = "README.md"
 
 [lib]
 name = "egobox"

python/README.md

@@ -0,0 +1,45 @@
+# EGObox - Efficient Global Optimization toolbox
+
+[![pytests](https://github.com/relf/egobox/workflows/pytest/badge.svg)](https://github.com/relf/egobox/actions?query=workflow%3Apytest)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.04737/status.svg)](https://doi.org/10.21105/joss.04737)
+
+`egobox` package is the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, from the [EGObox libraries](https://github.com/relf/egobox?tab=readme-ov-file#egobox---efficient-global-optimization-toolbox) written in Rust.
+
+## Installation
+
+```bash
+pip install egobox
+```
+
+### Egor optimizer
+
+```python
+import numpy as np
+import egobox as egx
+
+# Objective function
+def f_obj(x: np.ndarray) -> np.ndarray:
+    return (x - 3.5) * np.sin((x - 3.5) / (np.pi))
+
+# Minimize f_opt in [0, 25]
+res = egx.Egor(egx.to_specs([[0.0, 25.0]]), seed=42).minimize(f_obj, max_iters=20)
+print(f"Optimization f={res.y_opt} at {res.x_opt}")  # Optimization f=[-15.12510323] at [18.93525454]
+```
+
+### Gpx surrogate model
+
+```python
+import numpy as np
+import egobox as egx
+
+# Training
+xtrain = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
+ytrain = np.array([0.0, 1.0, 1.5, 0.9, 1.0])
+gpx = egx.Gpx.builder().fit(xtrain, ytrain)
+
+# Prediction
+xtest = np.linspace(0, 4, 20).reshape((-1, 1))
+ytest = gpx.predict(xtest)
+```
+
+See the [tutorial notebooks](https://github.com/relf/egobox/tree/master/doc/README.md) and [examples folder](https://github.com/relf/egobox/tree/d9db0248199558f23d966796737d7ffa8f5de589/python/egobox/examples) for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.