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Contents of the Book

1 — Getting Started with Python

• The first example: Hello, World!
• Different ways to use Python

2 — Computing with Formulas

• Programming simple mathematics
• Variables and variable types
• Formatting text output
• Importing modules
• Pitfalls when programming mathematics

3 — Loops and Lists

• Loops for automating repetitive tasks
• Boolean expressions
• Using lists to store sequences of data
• Iterating over a list with a for loop
• Nested lists and list slicing
• Tuples

4 — Functions and Branching

• Programming with functions
• Function arguments and local variables
• Default arguments and doc strings
• If-tests for branching program flow
• Functions as arguments to functions
• Solving equations with Python functions
• Writing test functions to verify programs

5 — User Input and Error Handling

• Reading user input data
• Flexible user input using eval and exec
• Reading data from files
• Writing data to files
• Handling errors in programs
• Making modules

6 — Arrays and Plotting

• NumPy and array computing
• Plotting curves with Matplotlib
• Plotting discontinuous and piecewise-defined functions
• Making a movie of a plot
• More useful array operations

7 — Dictionaries and Strings

• Dictionaries
• Example: A dictionary for polynomials
• Example: Reading file data into a dictionary
• String manipulation

8 — Classes

• Basics of classes
• Protected class attributes
• Special methods
• Example: Automatic differentiation of functions
• Test functions for classes
• Example: A polynomial class

9 — Object-Oriented Programming

• Class hierarchies and inheritance
• Example: Classes for numerical differentiation
• Example: Classes for numerical integration

Book Description

If you’ve ever wondered how scientists crunch numbers or run simulations, you’re in the right place! Introduction to Scientific Programming with Python by Joakim Sundnes breaks down the basics of scientific computing in a way that’s both fun and approachable. This book takes you from zero to hero with clear language, lots of code samples, and a gentle learning curve. I mean, who knew programming could be this friendly?

Book Overview

This PDF isn’t just another dry programming manual. It’s packed with real-world problemsthink climate modeling, physics calculations, and even a few puzzles that’ll make you scratch your head (in a good way). Sundnes does a fantastic job connecting theory to practice. You’ll learn why Python is such a superstar in the science world and how to wield it for your own projects. If you’re curious about how scientific computing works in practice, this book offers an easy entry point without overwhelming jargon.

Why Read This Book

Let’s be honestlearning programming can feel intimidating at first. But this book? It makes scientific programming downright enjoyable! Sundnes keeps things light without sacrificing depth. Each chapter builds confidence through hands-on examples and encourages experimentation (I’m all for breaking things and fixing them again!). If you’ve ever peeked at MATLAB resources for scientific applications but wanted something more open-source and flexible, Python is your new best friend.

Who This Book Is For

This book welcomes everyonefrom total beginners to seasoned coders looking to brush up on their scientific chops. Are you a student tackling data analysis for the first time? Maybe you’re a researcher tired of wrestling with spreadsheets? Or perhaps you’re just a curious soul who loves puzzles and problem-solving? Either way, Sundnes’ approach ensures nobody gets left behind. The pace is comfortable, the tone is encouraging, and heyif you get stuck, there’s always another example right around the corner.

What You Will Learn

• The basics of Python programming (variables, loops, functionsyou know the drill)
• How to use libraries like NumPy for powerful math operations
• Plotting graphs and visualizing data (because who doesn’t love pretty charts?)
• Writing readable code that actually makes sense later (your future self will thank you)
• Tackling real scientific problems step by step
• Debugging techniques that save time and sanity
• An introduction to working with larger datasets
• The confidence to start your own science projects using Python

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