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 -- Appendix.

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 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, 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. 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 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.