Numerical Python
Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
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14 900 Ft
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Product details:
- Edition number 3
- Publisher Apress
- Date of Publication 28 September 2024
- Number of Volumes 1 pieces, Book
- ISBN 9798868804120
- Binding Paperback
- No. of pages492 pages
- Size 254x178 mm
- Language English
- Illustrations XX, 492 p. 165 illus., 155 illus. in color. Illustrations, black & white 827
Categories
Long description:
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
Who This Book Is For
Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
MoreTable 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.- Appendix.
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