Practical Numerical Computing Using Python

Preface
Chapter One
	Introduction to Computing
	Computer Hardware
	Supercomputers & Computation Complexity
	Computer Software
	Brief Overview of Python
	Anaconda Python, Notebooks, and Prutor
	Applications of Computers
Chapter Two
	Integers
	Floating Point and Complex Numbers
	Python Variables and Logical Variables
Chapter Three
	Character & Strings
	List
	Numpy Arrays
	Dictionary
	Mutable and Immutable Objects in Python
Chapter Four
	Simple Statements
	Conditional Flows in Python
	Looping in Python
Chapter Five
	Functions in Python
	Python Namespace and Scope
	Recursive Functions
Chapter Six
	Good Progamming Practices
	Prime Numbers
	Searching and Sorting
Chapter Seven
	Matplotlib & Field Plots
	Miscellaneous Plots
	Animation using Python
Chapter Eight
	Reading & Writing Text Files in Python
	Reading & Writing Numerical Data in Python
Chapter Nine
	Error Analysis
	Nondimensionalization of Equations
Numerical Methods
Chapter Ten
	Lagrange Interpolation
	Splines
Chapter Eleven
	Newton-Cotes Formulas
	Gaussian Quadrature
	Python's Quad & Multidimensional Integrals
Chapter Twelve
	Computing Numerical Derivatives
Chapter Thirteen
	General Overview
	Euler Forward Method, Accuracy & Stability
	Implicit Schemes
	Higher-order Methods
	Multistep Method
	Solving a System of Equations
	Stiff Equations
Chapter Fourteen
	Fourier Transform
	One-dimensional Discrete Fourier Transforms
	Mutlidimensional Fourier Transform
Chapter Fifteen
	Solving PDEs Using Spectral Method: Diffusion Equation
	Solving Wave, Burgers, and KdV Equations
	Spectral Solution of Naiver-Stokes Equation
	Spectral Solution of Schrödinger Equation
Chapter Sixteen
	General Overview & Diffusion Equation Solver
	Solving Wave Equation
	Burgers and Navier-Stokes Equations
	Schrodinger equation
Chapter Seventeen
	Root Finders
Chapter Eighteen
	Shooting Method
	Eigenvalue Calculation
Chapter Nineteen
	Solving Laplace Equation
	Solving Poisson Equation
Chapter Twenty
	Solution of Algebraic Equations
	Eigenvalues and Eigenvectors
Chapter Twenty-One
	Random numbers
	Integration Using Random Numbers
	Regression Analysis
	Applications in Statmech
	Machine Learning
Epilogue
Appendix A: Errors in Lagrange Interopolation
Appendix B: Improving Accuracy Using Richardson Method
References