# Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, 3rd Edition

### eBook Details:

**Paperback:**512 pages**Publisher:**WOW! eBook (September 28, 2024)**Language:**English**ISBN-10:**8868804120**ISBN-13:**978-8868804120

### eBook Description:

### Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, 3rd Edition

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This Numerical Python, 3rd Edition book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library’s latest version, demonstrates Python’s power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis.

What You’ll Learn

- Work with vectors and matrices using NumPy
- Review Symbolic computing with SymPy
- Plot and visualize data with Matplotlib
- Perform data analysis tasks with Pandas and SciPy
- Understand statistical modeling and machine learning with statsmodels and scikit-learn
- Optimize Python code using Numba and Cython

After reading this Numerical Python, 3rd Edition 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.