Numerical Python By Robert Johansson PDF - Zeldatech

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Sunday, 3 November 2019

Numerical Python By Robert Johansson PDF

Download Book : Numerical Python By Robert Johansson PDF


Informations about the book:

TitleNumerical Python

Author
Robert Johansson

Size: 23 MB

Format: PDF

Year: 2019

Pages: 709

Book Contents:

Chapter 1: Introduction to Computing with Python
Chapter 2: Vectors, Matrices, and Multidimensional Arrays
Chapter 3: Symbolic Computing
Chapter 4: Plotting and Visualization
Chapter 5: Equation Solving
Chapter 6: Optimization
Chapter 7: Interpolation
Chapter 8: Integration
Chapter 9: Ordinary Differential Equations
Chapter 10: Sparse Matrices and Graphs
Chapter 11: Partial Differential Equations
Chapter 12: Data Processing and Analysis
Chapter 13: Statistics
Chapter 14: Statistical Modeling
Chapter 15: Machine Learning
Chapter 16: Bayesian Statistics
Chapter 17: Signal Processing
Chapter 18: Data Input and Output
Chapter 19: Code Optimization


Description:

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.
Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.
After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.


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