Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Language: English
Published by Apress, 2024
- Softcover
- Used
Seller: Marlton Books, Bridgeton, NJ, U.S.A.Marlton Books
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Readable, but has significant damage / tears. Has a remainder mark. paperback Used - Acceptable 2024.
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- Title
- Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
- Author
- Johansson, Robert
- Publisher
- Apress
- Publication year
- 2024
- Condition
- Acceptable
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798868804120
- Edition
- 3rd Edition
- Item weight
- 33.72 ounces
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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