From Classic Computer Science Problems in Python by David Kopec
A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit
From Modern Fortran by Milan Curcic
Stock price analysis and prediction has been an increasingly popular topic since the early days of high-level programming, and Fortran has been used in the bowels of many financial trading and banking systems, mainly thanks to its robustness, reliability, and efficiency. In this article, we’ll work with a dataset that is freely available, small enough to be easily downloaded, and yet large enough to demonstrate the power of Fortran arrays.
With Naomi Ceder, author of The Quick Python Book, Third Edition.
Naomi Ceder earned a Ph.D in Classics, but, since 2001, has been learning, teaching, and using Python. An elected fellow of the Python Software Foundation, Naomi currently serves as chair of its board of directors. She also speaks internationally about the Python community, and on inclusion and diversity in technology in general. By day she leads a team of Python programmers for Dick Blick Art Materials, and in her spare time she enjoys sketching, knitting, and deep philosophical conversations with her dog.