Learning Scientific Programming with Python

"Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming"-- Provided by publisher Copyright 5 Contents 6 Acknowledgments 9 Code Listings 10 1 Introduction 14 1.1 About This Book 14 1.2 About Python 15 1.3 Installing Python 18 1.4 The Command Line 19 2 The Core Python Language I 21 2.1 The Python Shell 21 2.2 Numbers, Variables, Comparisons and Logic 22 2.3 Python Objects I: Strings 40 2.4 Python Objects II: Lists, Tuples and Loops 56 2.5 Control Flow 71 2.6 File Input/Output 81 2.7 Functions 84 3 Interlude: Simple Plots and Charts 99 3.1 Basic Plotting 99 3.2 Labels, Legends and Customization 104 3.3 More Advanced Plotting 113 4 The Core Python Language II 118 4.1 Errors and Exceptions 118 4.2 Python Objects III: Dictionaries and Sets 126 4.3 Pythonic Idioms: “Syntactic Sugar” 138 4.4 Operating-System Services 150 4.5 Modules and Packages 156 4.6 An Introduction to Object-Oriented Programming 165 5 IPython and Jupyter Notebook 185 5.1 IPython 185 5.2 Jupyter Notebook 199 6 NumPy 209 6.1 Basic Array Methods 209 6.2 Reading and Writing an Array to a File 241 6.3 Statistical Methods 252 6.4 Polynomials 259 6.5 Linear Algebra 274 6.6 Random Sampling 289 6.7 Discrete Fourier Transforms 300 7 Matplotlib 307 7.1 Line Plots and Scatter Plots 307 7.2 Plot Customization and Refinement 312 7.3 Bar Charts, Pie Charts and Polar Plots 327 7.4 Annotating Plots 336 7.5 Contour Plots and Heatmaps 349 7.6 Three-Dimensional Plots 361 7.7 Animation 365 8 SciPy 371 8.1 Physical Constants and Special Functions 371 8.2 Integration and Ordinary Differential Equations 394 8.3 Interpolation 421 8.4 Optimization, Data-Fitting and Root-Finding 427 9 Data Analysis with pandas 451 9.1 Introduction to pandas 451 9.2 Reading and Writing Series and DataFrames 465 9.3 More Advanced Indexing 475 9.4 Data Cleaning and Exploration 481 9.5 Data Grouping and Aggregation 492 9.6 Examples 496 10 General Scientific Programming 503 10.1 Floating-Point Arithmetic 503 10.2 Stability and Conditioning 511 10.3 Programming Techniques and Software Development 516 Appendix A Solutions 527 Appendix B Differences Between Python Versions 2 and 3 549 Appendix C SciPy’s odeint Ordinary Differential Equation Solver 553 Glossary 556 Index 562 "Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming"-- Learn to master basic Python programming tasks from scratch with real-life, scientifically-relevant examples and solutions drawn from science and engineering. This fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. This Fast-paced Introduction To Python Moves From The Basics To Advanced Concepts, Enabling Readers To Gain Proficiency Quickly.