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.
I made Prince when I was at university, back in 2016. I spent a significant amount of time in 2022 to revamp the entire package. It is thoroughly tested and supports many features, such as supplementary row/columns, as well as row/column weights.
Example usage
>>> 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'
... )
Installation
pip install prince
🎨 Prince uses Altair for making charts.
Methods
flowchart TD
groups?(Groups of columns?) --> |"✅"| MFA
groups? --> |"❌"| cat?(Categorical data?)
cat? --> |"✅"| num_too?(Numerical data too?)
num_too? --> |"✅"| FAMD
num_too? --> |"❌"| multiple_cat?(More than two columns?)
multiple_cat? --> |"✅"| MCA
multiple_cat? --> |"❌"| CA
cat? --> |"❌"| shapes?(Analysing shapes?)
shapes? --> |"✅"| GPA
shapes? --> |"❌"| manifold?(Data on a manifold?)
manifold? --> |"✅"| PGA
manifold? --> |"❌"| PCA
Principal component analysis (PCA)
Correspondence analysis (CA)
Multiple correspondence analysis (MCA)
Multiple factor analysis (MFA)
Factor analysis of mixed data (FAMD)
Generalized procrustes analysis (GPA)
Principal geodesic analysis (PGA)
Correctness
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. PGA is tested against geomstats. See more in the tests directory.
Citation
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}
}
License
The MIT License (MIT). Please see the license file for more information.
1 maintainer
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