Introduction

I am going to go for a Raymond Hettinger style presentation, https://www.cs.odu.edu/~tkennedy/cs330/s21/Public/languageResources/#python-programming-videos.

These materials are web-centric (i.e., do not need to be printed and are available at https://www.cs.odu.edu/~tkennedy/numpy-workshop).

Who am I?

I have taught various courses, including:

• CS 300T - Computers in Society
• CS 333 - Programming and Problem Solving
• CS 330 - Object Oriented Programming and Design
• CS 350 - Introduction to Software Engineering
• CS 410 - Professional Workforce Development I
• CS 411W - Professional Workforce Development II
• CS 417 - Computational Methods & Software

Most of my free time is spent writing Python 3 and Rust code, tweaking my Vim configuration, or learning a new (programming) language. My current language of interests are Rust (at the time of writing) and Python (specifically the NumPy library).

Referenced Courses & Materials

I may reference materials (e.g., lecture notes) and topics from various courses, including:

• CS 330 - Object Oriented Programming & Design
• CS 350 - Introduction to Software Engineering
• CS 417 - Computational Methods & Software
  • Python non-linear solver discussion

I will also reference a couple examples from the previous:

• Git workshop
• Python workshop
• Python follow-up workshop
• Rust workshop

The Broad Strokes

I will focus on:

Overview

What is NumPy?

According to the official NumPy documentation:

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Retrieved from https://numpy.org/doc/stable/user/whatisnumpy.html