scientisttools is a Python package dedicated to multivariate Exploratory Data Analysis and clustering analysis.
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It performs classical principal component methods :
- Principal Components Analysis (PCA)
- Principal Components Analysis with partial correlation matrix (PartialPCA)
- Exploratory Factor Analysis (EFA)
- Classical Multidimensional Scaling (CMDSCALE)
- Metric and Non - Metric Multidimensional Scaling (MDS)
- Correspondence Analysis (CA)
- Multiple Correspondence Analysis (MCA)
- Factor Analysis of Mixed Data (FAMD)
- Multiple Factor Analysis (MFA)
- Multiple Factor Analysis for qualitatives/categoricals variables (MFAQUAL)
- Multiple Factor Analysis of Mixed Data (MFAMIX)
- Multiple Factor Analysis of Contingence Tables (MFACT)
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In some methods, it allowed to add supplementary informations such as supplementary individuals and/or variables.
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It provides a geometrical point of view, a lot of graphical outputs.
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It provides efficient implementations, using a scikit-learn API.
Those statistical methods can be used in two ways :
- as descriptive methods ("datamining approach")
- as reduction methods in scikit-learn pipelines ("machine learning approach")
scientisttools also performs clustering analysis
- Clustering analysis:
- Hierarchical Clustering on Principal Components (HCPC)
- Variables Hierarchical Clustering Analysis (VARHCA)
- Variables Hierarchical Clustering Analysis on Principal Components (VARHCPC)
- Categorical Variables Hierarchical Clustering Analysis (CATVARHCA)
Notebooks are availabled.
scientisttools requires
numpy>=1.26.4
matplotlib>=3.5.3
scikit-learn>=1.2.2
pandas>=2.2.2
mapply>=0.1.21
plotnine>=0.10.1
plydata>=0.4.3
pingouin>=0.5.3
scientistmetrics>=0.0.3
ggcorrplot>=0.0.2
You can install scientisttools using pip :
pip install scientisttools
Tutorial are available
Duvérier DJIFACK ZEBAZE (duverierdjifack@gmail.com)