Abstract
The extension to Python, NumPy, that enables fast calculations using matrices, vectors, and similar containers is detailed. We discuss the creation of objects in NumPy, manipulating them, and applying mathematical functions to them. We also demonstrate operator overloading, complex arithmetic, indexing, and iterating using NumPy objects. All of these concepts are illustrated using worked examples. To make scientific figures and plots the module Matplotlib is introduced. This toolkit has similar syntax to the plotting tools of Matlab. Several examples of plots and their customization is shown using Python codes.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational Nuclear Engineering and Radiological Science Using Python™
Paper Title
Journal
Date
