Numerical Python Scientific Computing and Data Science Applications with Numpy,SciPy and Matplotlib,2nd Edition

Numerical Python: Scientific Computing and Data Science Applications with Numpy,SciPy and Matplotlib
Authors: Robert Johansson
ISBN-10: 1484242459
ISBN-13: 9781484242452
Edition 版次: 2nd ed.
Publication Date 出版日期: 2018-12-25
Print Length 页数: 700 pages
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.
What You’ll Learn
Work with vectors and matrices using NumPy
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Review statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
Cover

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