Programming with Python for Engineers

An interactive book introducing Python to engineers and engineering students. This book is intended to be an accompanying textbook for teaching programming to science and engineering students with no prior programming expertise. This endeavour requires a delicate balance between providing details on computers & programming in a complete manner and the programming needs of science and engineering disciplines. With the hopes of providing a suitable balance, the book uses Python as the programming language, since it is easy to learn and program. Moreover, for keeping the balance, the book is formed of three parts: • Part I: The Basics of Computers and Computing: The book starts with what computation is, introduces both the present-day hardware and software infrastructure on which programming is performed and introduces the spectrum of programming languages. • Part II: Programming with Python: The second part starts with the basic building blocks of Python programming and continues with providing the ground formation for solving a problem in to Python. Since almost all science and engineering libraries in Python are written with an object-oriented approach, a gentle introduction to this concept is also provided in this part. • Part III: Using Python for Science and Engineering Problems: The last part of the book is dedicated to practical and powerful tools that are widely used by various science and engineering disciplines. These tools provide functionalities for reading and writing data from/to files, working with data (using e.g. algebraic, numerical or statistical computations) and plotting data. These tools are then utilized in example problems and applications at the end of the book. This is an ‘interactive’ book with a rather ‘minimalist’ approach: Some details or specialized subjects are not emphasized and instead, direct interaction with examples and problems are encouraged. Therefore, rather than being a ‘complete reference manual’, this book is a ‘first things first’ and ‘hands on’ book. The pointers to skipped details will be provided by links in the book. Bearing this in mind, the reader is strongly encouraged to read and interact all contents of the book thoroughly. The book’s interactivity is thanks to Jupyter notebook. Therefore, the book differs from a conventional book by providing some dynamic content. This content can appear in audio-visual form as well as some applets (small applications) embedded in the book. It is also possible that the book asks the the reader to complete/write a piece of Python program, run it, and inspect the result, from time to time. The reader is encouraged to complete these minor tasks. Such tasks and interactions are of great assistance in gaining acquaintance with Python and building up a self-confidence in solving problems with Python. Preface Basic Computer Organization The von Neumann Architecture John von Neumann Components of the von Neumann Architecture The Memory The CPU The Fetch-Decode-Execute Cycle The Stored Program Concept Pros and Cons of the von Neuman Architecture Peripherals of a computer The running of a computer Start up Process The Operating System Important Concepts Further Reading Exercises A Broad Look at Programming and Programming Languages How do we solve problems with programs? Algorithm How to write algorithms How to compare algorithms Data Representation The World of Programming Languages Low-level Languages High-level Languages Implementing with a High-level Language: Interpreter vs. Compiler Programming-language Paradigms Introducing Python Important Concepts Further Reading Exercise Representation of Data Representing integers Sign-Magnitude Notation Two’s Complement Representation Why does Two’s Complement work? Benefits of the Two’s Complement Representation PRACTICE TIME Representing real numbers The IEEE754 Representation Information loss in floating-point representations PRACTICE TIME Numbers in Python Representing text Characters Strings Containers Representing truth values (Booleans) Important Concepts Further Reading Exercises Dive into Python Basic Data Numbers in Python Boolean Values Container data (str, tuple, list, dict, set) Accessing elements in sequential containers Useful operations common to containers String List and tuple Dictionary Set Expressions Arithmetic, Logic, Container and Comparison Operations Exercise Evaluating Expressions Implicit and Explicit Type Conversion Basic Statements Assignment Statement and Variables Variables & Aliasing Naming variables Other Basic Statements Compound Statements Basic actions for interacting with the environment Actions for input Actions for output Actions that are ignored Comments Pass statements Actions and data packaged in libraries Providing your actions to the interpreter Directly interacting with the interpreter Writing actions in a file (script) Writing your actions as libraries (modules) Important Concepts Further Reading Exercises Conditional and Repetitive Execution Conditional execution if statement Exercise Nested if statements Practice Conditional expression Repetitive execution while statement Examples with while statement for statement Examples with for statement continue and break statements Set and list comprehension Important Concepts Further Reading Exercises Functions Why define functions? Defining functions Passing parameters to functions Default Parameters Scope of variables Higher-order functions Functions in programming vs. functions in Mathematics Recursion Function Examples Programming Style Important Concepts Further Reading Exercises A Gentle Introduction to Object-Oriented Programming Properties of Object-Oriented Programming Encapsulation Inheritance Polymorphism Basic OOP in Python The Class Syntax Special Methods/Operator Overloading Example 1: Counter Example 2: Rational Number Inheritance with Python Interactive Example: A Simple Shape Drawing Program Useful Short Notes on Python’s OOP Widely-used Member Functions of Containers Important Concepts Further Reading Exercises File Handling First Example Files and Sequential Access Data Conversion and Parsing Accessing Text Files Line by Line Termination of Input Example: Processing CSV Files Formatting Files Binary Files Note on Files, Directory Organization and Paths List of File Class Member Functions Important Concepts Further Reading Exercises Error Handling and Debugging Types of Errors Syntax Errors Type errors Run-Time Errors Logical Errors How to Work with Errors Program with Care Place Controls in Your Code Handle Exceptions Write verification code and raise exceptions Debug Your Code Write Test Cases Debugging Debugging Using Debugging Outputs Handle the Exception to Get More Information Use Python Debugger Important Concepts Further Reading Scientific and Engineering Libraries Numerical Computing with NumPy Arrays and Their Basic Properties Working with Arrays Linear Algebra with NumPy Why Use NumPy? Efficiency Benefits Scientific Computing with SciPy Data handling & analysis with Pandas Supported File Formats Data Frames Accessing Data with DataFrames Modifying Data with DataFrames Analyzing Data with DataFrames Presenting Data in DataFrames Plotting data with Matplotlib Parts of a Figure Preparing your Data for Plotting Drawing Single Plots Drawing Multiple Plots in a Figure Changing elements of a plot Important Concepts Further Reading Exercises An Application: Approximation and Optimization Approximating Functions with Taylor Series Taylor Series Example in Python Finding the Roots of a Function Newton’s Method for Finding the Roots Misc Details on Newton’s Method for the Curious Newton’s Method in Python Newton’s Method in SciPy Finding a Minimum of Functions Newton’s Method for Finding the Minimum of a Function Misc Details for the Curious Newton’s Method in Python Newton’s Method for Finding Minima in SciPy Important Concepts Further Reading Exercises An Application: Solving a Simple Regression Problem Introduction Why is regression important? The form of the function Least-Squares Regression Linear Regression with SciPy Create Artificial Data Download and Visualize Data Fit a Linear Function with SciPy Analyze the Solution Non-linear Regression with SciPy Create Artificial Data Download and Visualize Data Fitting a Non-linear Function with SciPy Analyzing the Solution Important Concepts Further Reading Exercises