README.md

👋 Welcome to skore

With skore, data scientists can:

1. Store objects of different types from their Python code: python lists, scikit-learn fitted pipelines, plotly figures, and more.
2. Track and visualize these stored objects on a user-friendly dashboard.
3. Export the dashboard to a HTML file.

These are only the first features: skore is a work in progress and aims to be an end-to-end library for data scientists. Stay tuned, and join our Discord if you want to give us feedback!

⚙️ Installation

First of all, we recommend using a virtual environment (venv). You need python>=3.9.

Then, you can install skore by using pip:

pip install -U skore

🚨 For Windows users, the encoding must be set to UTF-8: see PYTHONUTF8.

🚀 Quick start

1. From your shell, initialize a skore project, here named project.skore, that will be in your current working directory:

python -m skore create "project.skore"

2. Then, from your Python code (in the same directory), load the project and store an integer for example:

from skore import load
project = load("project.skore")
project.put("my_int", 3)

3. Finally, from your shell (in the same directory), start the UI locally:

python -m skore launch "project.skore"

This will automatically open a browser at the UI's location:

1. On the top left, create a new View.
2. From the Elements section on the bottom left, you can add stored items to this view, either by double-cliking on them or by doing drag-and-drop.

👨‍💻 More examples

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

Storing a pandas dataframe:

import numpy as np
import pandas as pd

my_df = pd.DataFrame(np.random.randn(3, 3))
project.put("my_df", my_df)

Storing a matplotlib figure:

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4, 5]
fig, ax = plt.subplots(figsize=(5, 3), layout="constrained")
ax.plot(x)
project.put("my_figure", fig)

Storing a scikit-learn fitted pipeline:

from sklearn.datasets import load_diabetes
from sklearn.linear_model import Lasso
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

diabetes = load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
my_pipeline = Pipeline(
    [("standard_scaler", StandardScaler()), ("lasso", Lasso(alpha=2))]
)
my_pipeline.fit(X, y)
project.put("my_fitted_pipeline", my_pipeline)

👨‍🏫 For a complete introductory example, see our basic usage notebook. It shows you how to store all types of items: python lists and dictionaries, numpy arrays, pandas dataframes, scikit-learn fitted models, figures (matplotlib, altair, and plotly), etc. The resulting skore report has been exported to this HTML file.

🔨 Contributing

Thank you for your interest! See 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

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