skrub-tutorials/index.qmd at main · skrub-data/skrub-tutorials · GitHub

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---
title: "Skrub - machine learning with dataframes"
author: "Riccardo Cappuzzo"
---

This website hosts the material used for teaching how to use
the [skrub package](https://skrub-data.org/stable/): it contains all the material
used for the course, including the datasets and exercises used during the session.

Choose your learning format:

::: {.grid}

::: {.g-col-6}
## 📖 Book Format
Complete course material in book format with detailed explanations. The book
includes more detail that isn't covered in the slides.

[Access the Book →](book/index.html){.btn .btn-primary}
:::

::: {.g-col-6}
## 🎯 Presentation Slides
Slide-based presentation for instructor-led sessions.

[View Slides →](slides/index.html){.btn .btn-primary}
:::

:::

## 💻 Interactive Exercises
Try the exercises in your browser with JupyterLite (no installation required).

[Launch JupyterLite →](jupyterlite/lab/){.btn .btn-success}

If you want to run the code locally, refer to the [Preparation and setup](#prepration-and-setup)
section for detailed instructions.

## Contacts
If you have questions, contact me at `riccardo[dot]cappuzzo[at]inria.fr`


## Structure of the course
The course covers the main features of skrub, from data exploration to pipeline
construction, with a short discussion about Data Ops.

Each chapter includes a section that describes how a specific feature may assist
in building a machine learning pipeline, along with practical code examples.

Some chapters include exercises for participants to work with the explained features.
These exercises are made available in `content/exercises`, as well as at the end
of the respective lesson in `content/notebooks`.

# Prepration and setup
First of all, clone the [GitHub repo](https://github.com/skrub-data/skrub-tutorials/tree/main)
of this book to have access to the exercises. In a future version, Jupyterlite
support will be added.

## Setting up a local environment

:::  {.callout-important}
## Navigating the repository
Depending on how you launch the instance of Jupyter lab, you might start it in
the root folder.

All notebooks used in the course are found in `content/notebooks`, while the
exercises are in `content/exercises`.
:::


### Using `pip`
Create the and activate the environment:

```sh
python -m venv skrub-tutorial
source skrub-tutorial/bin/activate
```

Install the required dependencies using the `requirements.txt` file:
```sh
pip install -r requirements.txt
```

Start the Jupyter lab instance:
```sh
jupyter lab
```

### Using conda
An `environment.yaml` file is provided to create a conda environment.

Create and activate the environment with

```sh
conda env create -f environment.yaml
conda activate skrub-tutorial
```

Then, start a jupyter lab instance:

```sh
jupyter lab
```

### Using `uv`
Create the environment using `pyproject.toml` as the requirement file.

```sh
uv venv
uv pip install -r pyproject.toml
```

Activate the environment that was created in the folder.
```sh
source .venv/bin/activate
```

Start the Jupyter lab instance:
```sh
jupyter lab
```

### Using pixi
Follow the platform-specific
instructions in the link to install [pixi](https://pixi.sh/latest/installation/),
then open a terminal window in the folder of the repository you cloned.

Run
```sh
pixi install
```
to create the environment, followed by

```sh
pixi run lab
```
to start a Jupyter lab instance.