This booklet tells you how to use the Python ecosystem to carry out some simple multivariate analyses, with a focus on principal components analysis (PCA) and linear discriminant analysis (LDA).
Jump to the notebook, or read it on Read the Docs.
This booklet assumes that the reader has some basic knowledge of multivariate analyses, and the principal focus of the booklet is not to explain multivariate analyses, but rather to explain how to carry out these analyses using Python.
The naming conventions in the functions were kept like in the original source for compliance. The variables were renamed to more generic names, so it would be possible to load your own dataset and run the notebook as it is if the first column of your data contains the data classes. See the cell that does the reading of the data.
The Python code was aimed to be easy to understand, like the R code in the original source, rather than be computationally and memory efficient.
A Little Book of Python for Multivariate Analysis by Yiannis Gatsoulis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at A Little Book of R for Multivariate Analysis by Avril Coghlan licensed under CC-BY-3.0.
