TUM Library

Cover; Half Title; Series in Computational Physics; Title; Copyrights; Dedication; Contents; Preface; About the Author; 1 Introduction; 1.1 Introduction to Python for Science and Engineering .; 2 Launching Python; 2.1 Interacting with Python; 2.2 Installing Python on Your Computer; 2.3 The Spyder Window; 2.4 The IPython Pane; 2.4.1 Magic commands; 2.4.2 System shell commands; 2.4.3 Tab completion; 2.4.4 Recap of commands; 2.5 Interactive Python as a Calculator; 2.5.1 Binary arithmetic operations in Python; 2.5.2 Types of numbers; 2.5.3 Important note on integer division in Python .

2.6 Variables2.6.1 Names and the assignment operator; 2.6.2 Legal and recommended variable names; 2.6.3 Reserved words in Python; 2.7 Script Files and Programs; 2.7.1 First scripting example: The Editor pane; 2.8 Python Modules; 2.8.1 Python modules and functions: A first look . .; 2.8.2 Some NumPy functions; 2.8.3 Scripting Example 2; 2.8.4 Different ways of importing modules; 2.9 Getting Help: Documentation in IPython; 2.10 Stand-alone IPython; 2.10.1 Writing Python scripts in a text editor; 2.11 Programming Errors; 2.11.1 Pyflakes; 2.11.2 Error checking; 2.12 Exercises

3 Strings, Lists, Arrays, and Dictionaries3.1 Strings; 3.2 Lists; 3.2.1 Slicing lists; 3.2.2 The range function: Sequences of numbers; 3.2.3 Tuples; 3.2.4 Multidimensional lists and tuples; 3.3 NumPy Arrays; 3.3.1 Creating arrays (1-d); 3.3.2 Mathematical operations with arrays; 3.3.3 Slicing and addressing arrays; 3.3.4 Fancy indexing: Boolean masks; 3.3.5 Multi-dimensional arrays and matrices; 3.3.6 Differences between lists and arrays; 3.4 Dictionaries; 3.5 Objects; 3.6 Exercises; 4 Input and Output; 4.1 Keyboard Input; 4.2 Screen Output; 4.2.1 Formatting output with str.format()

4.2.2 Printing arrays4.3 File Input; 4.3.1 Reading data from a text file; 4.3.2 Reading data from an Excel file: CSV files; 4.4 File Output; 4.4.1 Writing data to a text file; 4.4.2 Writing data to a CSV file; 4.5 Exercises; 5 Conditionals and Loops; 5.1 Conditionals; 5.1.1 if, elif, and else statements; 5.1.2 Logical operators; 5.2 Loops; 5.2.1 for loops; 5.2.2 while loops; 5.2.3 Loops and array operations; 5.3 List Comprehensions; 5.4 Exercises; 6 Plotting; 6.1 An Interactive Session with PyPlot; 6.2 Basic Plotting; 6.2.1 Specifying line and symbol types and colors . .; 6.2.2 Error bars

6.2.3 Setting plotting limits and excluding data6.2.4 Subplots; 6.3 Logarithmic Plots; 6.3.1 Semi-log plots; 6.3.2 Log-log plots; 6.4 More Advanced Graphical Output; 6.4.1 An alternative syntax for a grid of plots; 6.5 Plots with multiple axes; 6.6 Mathematics and Greek symbols; 6.7 The Structure of matplotlib: OOP and All That; 6.7.1 The backend layer; 6.7.2 The artist layer; 6.7.3 The PyPlot (scripting) layer; 6.8 Contour and Vector Field Plots; 6.8.1 Making a 2D grid of points; 6.8.2 Contour plots; 6.8.3 Streamline plots; 6.9 Three-Dimensional Plots; 6.10 Exercises; 7 Functions

Series in Computational PhysicsSteven A. Gottlieb and Rubin H. Landau, Series Editors Introduction to Python for Science and Engineering This guide offers a quick and incisive introduction to Python programming for anyone. The author has carefully developed a concise approach to using Python in any discipline of science and engineering, with plenty of examples, practical hints, and insider tips. Readers will see why Python is such a widely appealing program, and learn the basics of syntax, data structures, input and output, plotting, conditionals and loops, user-defined functions, curve fitting, numerical routines, animation, and visualization. The author teaches by example and assumes no programming background for the reader. David J. Pine is the Silver Professor and Professor of Physics at New York University, and Chair of the Department of Chemical and Biomolecular Engineering at the NYU Tandon School of Engineering. He is an elected fellow of the American Physical Society and American Association for the Advancement of Science (AAAS), and is a Guggenheim Fellow.

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