Metadata-Version: 2.3
Name: skore
Version: 0.1.1
Summary: Tooling and assistance for data scientists to "Own Your Data Science"
Project-URL: Homepage, https://probabl.ai
Project-URL: Repository, https://github.com/probabl-ai/skore
Project-URL: Download, https://pypi.org/project/skore/#files
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Maintainer-email: skore developers <skore@signal.probabl.ai>
License: MIT License
        
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Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: <3.13,>=3.9
Requires-Dist: diskcache
Requires-Dist: fastapi
Requires-Dist: rich
Requires-Dist: skops
Requires-Dist: uvicorn
Provides-Extra: test
Requires-Dist: altair; extra == 'test'
Requires-Dist: httpx; extra == 'test'
Requires-Dist: matplotlib; extra == 'test'
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Requires-Dist: scikit-learn; extra == 'test'
Description-Content-Type: text/markdown

# 👋 Welcome to skore

![ci](https://github.com/probabl-ai/skore/actions/workflows/ci.yml/badge.svg?event=push)
![python](https://img.shields.io/badge/python-3.11%20|%203.12-blue?style=flat&logo=python)

`skore` allows data scientists to create tracking and visualization from their Python code:
1. Users can store objects of different types: python lists and dictionaries, `numpy` arrays, `scikit-learn` fitted models, `matplotlib`, `altair`, and `plotly` figures, etc. Storing some values over time allows one to perform **tracking** and also to **visualize** them:
2. They can visualize these stored objects on a dashboard. The dashboard is user-friendly: objects can easily be organized.
3. This dashboard can be exported into a HTML file.

These are only the first features of `skore`'s roadmap.
`skore` is a work in progress and, on the long run, it aims to be an all-inclusive library for data scientists.
Stay tuned!

<p align="center">
    <img width="100%" src="https://github.com/sylvaincom/sylvaincom.github.io/blob/master/files/probabl/skore/2024_10_08_skore_demo.gif"/>
</p>

## ⚙️ Installation

You can install `skore` by using `pip`:
```bash
pip install -U skore
```

## 🚀 Quick start
=======

In your shell, run the following to create a project file `project.skore` (the default) in your current working directory:
```bash
python -m skore create 'project.skore'
```

Run the following in your Python code (in the same working directory) to load the project, store some objects, delete them, etc:
```python
from skore import load

# load the project
project = load("project.skore")

# save an item you need to track in your project
project.put("my int", 3)

# get an item's value
project.get("my int")

# by default, strings are assumed to be Markdown:
project.put("my string", "Hello world!")

# `put` overwrites previous data
project.put("my string", "Hello again!")

# list all the keys in a project
print(project.list_item_keys())

# delete an item
project.delete_item("my int")
```

Then, in the directory containing your project, run the following command in your shell to start the UI locally:
```bash
python -m skore launch project.skore
```
This will automatically open a browser at the UI's location.
In the `Elements` tab on the left, you can visualize the stored items.
Create a new `View`, then you can then add items into this view.

💡 Note that after launching the dashboard, you can keep modifying current items or store new ones, and the dashboard will automatically be refreshed.

👨‍🏫 For a complete introductory example, see our [basic usage notebook](https://github.com/probabl-ai/skore/blob/main/examples/basic_usage.ipynb).
It shows you how to store all types of items: python lists and dictionaries, `numpy` arrays, `scikit-learn` fitted models, `matplotlib`, `altair`, and `plotly` figures, etc.
The resulting `skore` report has been exported to [this HTML file](https://sylvaincom.github.io/files/probabl/skore/basic_usage.html).

## 🔨 Contributing

Thank you for your interest!
See [CONTRIBUTING.md](https://github.com/probabl-ai/skore/blob/main/CONTRIBUTING.md).

## 💬 Where to ask questions

| Type                                | Platforms                        |
|-------------------------------------|----------------------------------|
| 🐛 Bug reports                  | [GitHub Issue Tracker]           |
| ✨ Feature requests and ideas      | [GitHub Issue Tracker] & [Discord] |
| 💬 Usage questions, discussions, contributions, etc              | [Discord]   |

[GitHub Issue Tracker]: https://github.com/probabl-ai/skore/issues
[Discord]: https://discord.gg/scBZerAGwW

---

Brought to you by:

<a href="https://probabl.ai" target="_blank">
    <img width="120" src="https://sylvaincom.github.io/files/probabl/logo_probabl.svg" alt="Probabl logo">
</a>
