A Functional Start to Computing with Python / Edition 1

by Ted Herman

Overview

A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python’s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of Python, the text covers important concepts of computing, including software engineering motivation, algorithms behind syntax rules, advanced functional programming ideas, and, briefly, finite state machines.

Taking a student-friendly, interactive approach to teach computing, the book addresses more difficult concepts and abstractions later in the text. The author presents ample explanations of data types, operators, and expressions. He also describes comprehensions—the powerful specifications of lists and dictionaries—before introducing loops and variables. This approach helps students better understand assignment syntax and iteration by giving them a mental model of sophisticated data first.

Web Resource
The book’s supplementary website at http://functionalfirstpython.com/ provides many ancillaries, including:

• Interactive flashcards on Python language elements
• Links to extra support for each chapter
• Unit testing and programming exercises
• An interactive Python stepper tool
• Chapter-by-chapter points
• Material for lectures

Product Details

ISBN-13:	9781466504554
Publisher:	Taylor & Francis
Publication date:	07/26/2013
Series:	Chapman & Hall/CRC Textbooks in Computing , #10
Edition description:	New Edition
Pages:	429
Product dimensions:	6.90(w) x 9.90(h) x 1.00(d)

Table of Contents

Motivation and Background: Inspirations of Computing. Preview of Computing with Python. General Landscape of Computing Languages. Python Setup. Functional-Style Python: Types. Operators. Expressions. Printing. Functions I. Functions II. Conditional Logic. Slice, Split, Join. Comprehensions. Functional Patterns. Imperative-Style Python: Names for Data. Functions and Variables. Mutation. Modules. Repetition. Documentation. Debugging. Accumulation Loop Patterns. Search Loop Patterns. Drawing. Input and Output. Network Programs. Objects, Classes, and Inheritance. Randomness, Time, and System Modules. Graphical User Interfaces. Appendices. Index.