2D Data Analysis with PCA and Python

I have a 2D data file (.dat) that ENVI can open and visualize. It is a datacube, in which every pixel has its own graph. It is in that sense a 3D data. I would like to do Principal Component Analyses on this data file to extract meaningful data, such as the variations and representative graphs / regions etc. I need someone who can write a code to do statistical analyses on a .dat file using python in jupyter notebook. Key Requirements: - Experience in Python, specifically in Jupyter Notebook - Proficient especially in using NumPy and SciPy for statistical analysis - Able to interpret and visualize data The data is structured in a gridded format with spatial dimensions. Each pixel in the datacube corresponds to a specific spatial location. I need someone who can write a code to do statistical analyses on this .dat file using Python in Jupyter Notebook. I have a specific interest in the visualizations of dominant major components that explain the majority of the variation in the dataset, the principal components and the summary statistics and variations. The key goal of this project is to extract meaningful data from the PCA. If you are able to help with this project, please get in touch.