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

by Robert Johansson

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.

Genres: Programming, Mathematics, Computers

Kindle Edition

First published November 1, 2015

Reviews

[Sam Macharia · Rating 4 out of 5]

A great book. A good way to approach numerical problems in Python. However, you need to have prior knowledge of Python programming and even then, you might have to Google for clarification on various techniques here and there. Be that as it may, it pays off to go through this process.

[Igor Kuvychko · Rating 4 out of 5]

Excellent introduction to many popular numerical Python packages. The chapters strike the right balance of brevity, have great code examples, and present the information clearly. The reader must have some knowledge of Python (~intermediate level) as this is definitely not an intro to Python book. If you are looking to expand your numerical Python toolbox this is a great resource, but be forewarned that you get what amounts to a brief summary of capabilities for each package. For example, Bayesian statistics gets one chapter where the reader is introduced to pymc package. If you need to dig deeper, "Further Reading" sections at the end of each chapter contain great references (e.g., "Probabilistic Programming and Bayesian Methods for Hackers" by Davidson-Pilon).