scientistshiny 0.0.2

Python library to easily improve multivariate Exploratory Data Analysis graphs

scientistshiny : Perform Factorial Analysis from scientisttools with a Shiny for Python Application

1 About scientistshiny

scientistshiny is a Python package to easily improve multivariate Exploratory Data Analysis graphs.

2 Why used scientistshiny?

scientistshiny provided functions for :

• Principal Component Analysis (PCA) with scientistshiny (PCAshiny)
• Correspondence Analysis (CA) with scientistshiny (CAshiny)
• Multiple Correspondence Analysis (MCA) with scientistshiny (MCAshiny)
• Factor Analysis for Mixed Data (FAMD) with scientistshiny (FAMDshiny)
• Multiple Factor Analysis (MFA) with scientistshiny (MFAshiny)
• Multiple Factor Analysis for qualitative variables (MFAQUAL) with scientistshiny (MFAQUALshiny)
• Multiple Factor Analysis for Mixed Data (MFAMIX) with scientistshiny (MFAMIXshiny)
• Multiple Factor Analysis for Contingence Tables (MFACT) with scientistshiny (MFACTshiny)

3 Installation

3.1 Dependencies

scientistshiny requires :

scientisttools>=0.1.6
numpy>=1.26.4
matplotlib>=3.8.4
scikit-learn>=1.2.2
pandas>=2.2.3
plotnine>=0.10.1

3.2 User installation

You can install scientisttools using pip :

pip install scientistshiny

4 Example with PCAshiny

# Load dataset and functions
from scientisttools import PCA, load_decathlon2
from scientistshiny import PCAshiny
decathlon = load_decathlon2()

# PCA with scientistshiny
res_shiny = PCAshiny(model = decathlon)
res_shiny.run()

# PCAshiny on a result of a PCA
res_pca = PCA(ind_sup=list(range(23,27)),quanti_sup=[10,11],quali_sup=12)
res_pca.fit(decathlon)
res_shiny = PCAshiny(model = res_pca)
res_shiny.run()

4 Author(s)

Duvérier DJIFACK ZEBAZE ([e-mail])