Prince is a Python library for multivariate exploratory data analysis in Python. It includes a variety of methods for summarizing tabular data, including principal component analysis (PCA) and correspondence analysis (CA). Prince provides efficient implementations, using a scikit-learn API.
>>> import prince
>>> dataset = prince.datasets.load_decathlon()
>>> decastar = dataset.query('competition == "Decastar"')
>>> pca = prince.PCA(n_components=5)
>>> pca = pca.fit(decastar, supplementary_columns=['rank', 'points'])
>>> pca.eigenvalues_summary
eigenvalue % of variance % of variance (cumulative)
component
0 3.114 31.14% 31.14%
1 2.027 20.27% 51.41%
2 1.390 13.90% 65.31%
3 1.321 13.21% 78.52%
4 0.861 8.61% 87.13%
>>> pca.transform(dataset).tail()
component 0 1 2 3 4
competition athlete
OlympicG Lorenzo 2.070933 1.545461 -1.272104 -0.215067 -0.515746
Karlivans 1.321239 1.318348 0.138303 -0.175566 -1.484658
Korkizoglou -0.756226 -1.975769 0.701975 -0.642077 -2.621566
Uldal 1.905276 -0.062984 -0.370408 -0.007944 -2.040579
Casarsa 2.282575 -2.150282 2.601953 1.196523 -3.571794>>> chart = pca.plot(dataset)
This chart is interactive, which doesn't show on GitHub. The green points are the column loadings.
>>> chart = pca.plot(
... dataset,
... show_row_labels=True,
... show_row_markers=False,
... row_labels_column='athlete',
... color_rows_by='competition'
... )
pip install prince🎨 Prince uses Altair for making charts.
flowchart TD
cat?(Categorical data?) --> |"✅"| num_too?(Numerical data too?)
num_too? --> |"✅"| FAMD
num_too? --> |"❌"| multiple_cat?(More than two columns?)
multiple_cat? --> |"✅"| MCA
multiple_cat? --> |"❌"| CA
cat? --> |"❌"| groups?(Groups of columns?)
groups? --> |"✅"| MFA
groups? --> |"❌"| shapes?(Analysing shapes?)
shapes? --> |"✅"| GPA
shapes? --> |"❌"| PCA
Prince is tested against scikit-learn and FactoMineR. For the latter, rpy2 is used to run code in R, and convert the results to Python, which allows running automated tests. See more in the tests directory.
Please use this citation if you use this software as part of a scientific publication.
@software{Halford_Prince,
author = {Halford, Max},
license = {MIT},
title = {{Prince}},
url = {https://github.com/MaxHalford/prince}
}I made Prince when I was at university, back in 2016. I've had very little time over the years to maintain this package. I spent a significant amount of time in 2022 to revamp the entire package. Prince has now been downloaded over 1 million times. I would be grateful to anyone willing to sponsor me. Sponsorships allow me to spend more time working on open source software, including Prince.
The MIT License (MIT). Please see the license file for more information.