Python library for multidimensional analysis
Project description
scientisttools : Python library for multidimensional analysis
About scientisttools
scientisttools is a Python
package dedicated to multivariate Exploratory Data Analysis.
Why use scientisttools?
- It performs classical principal component methods :
- Principal Components Analysis (PCA)
- Principal Components Analysis with partial correlation matrix (PPCA)
- Weighted Principal Components Analysis (WPCA)
- Expectation-Maximization Principal Components Analysis (EMPCA)
- Exploratory Factor Analysis (EFA)
- Classical Multidimensional Scaling (CMSCALE)
- Metric and Non - Metric Multidimensional Scaling (MDS)
- Correspondence Analysis (CA)
- Multiple Correspondence Analysis (MCA)
- Factor Analysis of Mixed Data (FAMD)
- In some methods, it allowed to add supplementary informations such as supplementary individuals and/or variables.
- It provides a geometrical point of view, a lot of graphical outputs.
- 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")
Installation
Dependencies
scientisttools requires
Python >=3.10
Numpy >= 1.23.5
Matplotlib >= 3.5.3
Scikit-learn >= 1.2.2
Pandas >= 1.5.3
mapply >= 0.1.21
Plotnine >= 0.10.1
Plydata >= 0.4.3
User installation
You can install scientisttools using pip
:
pip install scientisttools
Tutorial are available
https://github.com/enfantbenidedieu/scientisttools/blob/master/ca_example2.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/classic_mds.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/efa_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/famd_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/ggcorrplot.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mca_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mds_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/partial_pca.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/pca_example.ipynb
Author
Duvérier DJIFACK ZEBAZE (duverierdjifack@gmail.com)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
scientisttools-0.0.5.tar.gz
(16.5 MB
view hashes)
Built Distribution
Close
Hashes for scientisttools-0.0.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2b96138d81040248f72119ebe10b86398bfe1bb2093f10a37a9fb46b028ef3d8 |
|
MD5 | 237eadc5e561a25f897877d17f75768c |
|
BLAKE2b-256 | 7a05d8927cbd3fca17dcabd50a1c913e2a5e2be00303f11f9e4e26e545221f02 |