Metadata-Version: 2.1
Name: scientistshiny
Version: 0.0.2
Summary: Python library to easily improve multivariate Exploratory Data Analysis graphs
Author: Duverier DJIFACK ZEBAZE
Author-email: duverierdjifack@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: shiny (>=0.9)
Requires-Dist: numpy (>=1.26.4)
Requires-Dist: matplotlib (>=3.8.4)
Requires-Dist: scikit-learn (>=1.2.2)
Requires-Dist: pandas (>=2.2.2)
Requires-Dist: numexpr (>=2.10.0)
Requires-Dist: plotnine (>=0.10.1)
Requires-Dist: scientisttools (>=0.1.6)

<p align="center">
	<img src="./scientistshiny.svg" height=300></img>
</p>
<div align="center">

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# 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 :

```bash
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` :

```bash
pip install scientistshiny
```

## 4 Example with `PCAshiny`

```python
# 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 ([djifacklab@gmail.com](djifacklab@gmail.com))
