Key Features
- Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice
- Summaries at the end of each chapter allow for quick access to important information
- Includes code in Jupyter notebook format that can be directly run online
PART 1 INTRODUCTION TO PYTHON PROGRAMMING
CHAPTER 1 Python Basics
1.1 Getting Started With Python
1.2 Python as a Calculator
1.3 Managing Packages
1.4 Introduction to Jupyter Notebook
1.5 Logical Expressions and Operators
1.6 Summary and Problems
CHAPTER 2 Variables and Basic Data Structures
2.1 Variables and Assignment
2.2 Data Structure – String
2.3 Data Structure – List
2.4 Data Structure – Tuple
2.5 Data Structure – Set
2.6 Data Structure – Dictionary
2.7 Introducing Numpy Arrays
2.8 Summary and Problems
CHAPTER 3 Functions
3.1 Function Basics
3.2 Local Variables and Global Variables
3.3 Nested Functions
3.4 Lambda Functions
3.5 Functions as Arguments to Functions
3.6 Summary and Problems
CHAPTER 4 Branching Statements
4.1 If-Else Statements
4.2 Ternary Operators
4.3 Summary and Problems
CHAPTER 5 Iteration
5.1 For-Loops
5.2 While Loops
5.3 Comprehensions
5.4 Summary and Problems
CHAPTER 6 Recursion
6.1 Recursive Functions
6.2 Divide-and-Conquer
6.3 Summary and Problems
CHAPTER 7 Object-Oriented Programming
7.1 Introduction to OOP
7.2 Class and Object
7.3 Inheritance, Encapsulation, and Polymorphism
7.4 Summary and Problems
CHAPTER 8 Complexity
8.1 Complexity and Big-ONotation
8.2 Complexity Matters
8.3 The Profiler
8.4 Summary and Problems
CHAPTER 9 Representation of Numbers
9.1 Base-N and Binary
9.2 Floating Point Numbers
9.3 Round-Off Errors
9.4 Summary and Problems
CHAPTER 10 Errors, Good Programming Practices, and Debugging
10.1 Error Types
10.2 Avoiding Errors
10.3 Try/Except
10.4 Type Checking
10.5 Debugging
10.6 Summary and Problems
CHAPTER 11 Reading and Writing Data
11.1 TXT Files
11.2 CSVFiles
11.3 Pickle Files
11.4 JSONFiles
11.5 HDF5 Files
11.6 Summary and Problems
CHAPTER 12 Visualization and Plotting
12.1 2D Plotting
12.2 3D Plotting
12.3 Working With Maps
12.4 Animations and Movies
12.5 Summary and Problems
CHAPTER 13 Parallelize Your Python
13.1 Parallel Computing Basics
13.2 Multiprocessing
13.3 Using Joblib
13.4 Summary and Problems
PART 2 INTRODUCTION TO NUMERICAL METHODS
CHAPTER 14 Linear Algebra and Systems of Linear Equations
14.1 Basics of Linear Algebra
14.2 Linear Transformations
14.3 Systems of Linear Equations
14.4 Solutions to Systems of Linear Equations
14.5 Solving Systems of Linear Equations in Python
14.6 Matrix Inversion
14.7 Summary and Problems
CHAPTER 15 Eigenvalues and Eigenvectors
15.1 Eigenvalues and Eigenvectors Problem Statement
15.2 The Power Method
15.3 The QR Method
15.4 Eigenvalues and Eigenvectors in Python
15.5 Summary and Problems
CHAPTER 16 Least Squares Regression
16.1 Least Squares Regression Problem Statement
16.2 Least Squares Regression Derivation (Linear Algebra)
16.3 Least Squares Regression Derivation (Multivariate Calculus)
16.4 Least Squares Regression in Python
16.5 Least Squares Regression for Nonlinear Functions
16.6 Summary and Problems
CHAPTER 17 Interpolation
17.1 Interpolation Problem Statement
17.2 Linear Interpolation
17.3 Cubic Spline Interpolation
17.4 Lagrange Polynomial Interpolation
17.5 Newton’s Polynomial Interpolation
17.6 Summary and Problems
CHAPTER 18 Taylor Series
18.1 Expressing Functions Using a Taylor Series
18.2 Approximations Using Taylor Series
18.3 Discussion About Errors
18.4 Summary and Problems
CHAPTER 19 Root Finding
19.1 Root Finding Problem Statement
19.2 Tolerance
19.3 Bisection Method
19.4 Newton–Raphson Method
19.5 Root Finding in Python
19.6 Summary and Problems
CHAPTER 20 Numerical Differentiation
20.1 Numerical Differentiation Problem Statement
20.2 Using Finite Difference to Approximate Derivatives
20.3 Approximating of Higher Order Derivatives
20.4 Numerical Differentiation With Noise
20.5 Summary and Problems
CHAPTER 21 Numerical Integration
21.1 Numerical Integration Problem Statement
21.2 Riemann Integral
21.3 Trapezoid Rule
21.4 Simpson’s Rule
21.5 Computing Integrals in Python
21.6 Summary and Problems
CHAPTER 22 Ordinary Differential Equations (ODEs) Initial-Value Problems
22.1 ODE Initial Value Problem Statement
22.2 Reduction of Order
22.3 The Euler Method
22.4 Numerical Error and Instability
22.5 Predictor–Corrector and Runge–Kutta Methods
22.6 Python ODE Solvers
22.7 Advanced Topics
22.8 Summary and Problems
CHAPTER 23 Boundary-Value Problems for Ordinary Differential Equations (ODEs)
23.1 ODE Boundary Value Problem Statement
23.2 The Shooting Method
23.3 The Finite Difference Method
23.4 Numerical Error and Instability
23.5 Summary and Problems
CHAPTER 24 Fourier Transform
24.1 The Basics of Waves
24.2 Discrete Fourier Transform (DFT)
24.3 Fast Fourier Transform (FFT)
24.4 FFT in Python
24.5 Summary and Problems
Appendix A Getting Started With Python in Windows
Index
9780124202283; 9780123869425; 9780128154793
Ce livre est indispensable pour quelqu'un qui veut maitrisé l'utilisation de Python, comme outil d'Analyse numérique, et de Simulation de différent phénomènes physique.
Python Programming and Numerical Methods, Edition 1 Kong, Siauw & Bayen I was looking forward to seeing these books by authors I know from previous works because I have been disappointed the programming language books I used in the past to be quite inefficient. While they explain the subject they want to tell clearly and clearly, their addition of python solutions at the same time makes reading fluent. I find it important for the book to solve numerical method issues with python as it is very guiding for engineering students. While this book can be a textbook with its current content, it is also a good resource for those who want to learn on their own. Increasing the number of case studies at the end of the chapters and I think it is more beneficial for students to give the appendix section (Getting started with Python in Windows) as the first section.
Me ha gustado el libro. Está lleno de ejemplos y problemas útiles. No obstante, echo en falta algo que uso actualmente en mi curso y que está presente, por ejemplo, en el libro de Robert Johansson, "Numerical Python", publicado por Apress, sobre cálculo simbólico, con el paquete "sympy".