Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

by Robert Johansson

Overview

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
  

Who This Book Is For

Developers who want to understand how to use Python and its related ecosystem for numerical computing. 

Product Details

ISBN-13:	9781484242469
Publisher:	Apress
Publication date:	12/24/2018
Sold by:	Barnes & Noble
Format:	eBook
File size:	41 MB
Note:	This product may take a few minutes to download.

About the Author

Robert Johansson is an experienced Python programmer and computational scientist with a Ph.D. in Theoretical Physics from Chalmers University of Technology, Sweden. He has worked with scientific computing in academia and industry for over 15 years and participated in open source and proprietary research and development projects. His open-source contributions include work on QuTiP, a popular Python framework for simulating the dynamics of quantum systems, and he has also contributed to several other popular Python libraries in the scientific computing landscape. Robert is passionate about scientific computing and software development, teaching and communicating best practices for combining these fields with optimal outcomes: novel, reproducible, extensible, and impactful computational results.

Table of Contents

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. Signal and Image Processing.- 18. Data Input and Output.- 19. Code Optimization.

Editorial Reviews

I would recommend the textbook to those interested in learning the Python ecosystem for numerical and scientific work. I enjoyed reading the style of examples where a few lines of code are explained at a time. This style feels like I'm getting a personalized lecture from Johansson while reading the book. It will be a very nice resource on the desk of any graduate student working with Python.” (Charles Jekel, SIAM Review, Vol. 62 (2), 2020)

From the Publisher