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Synopsis
Leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, Matplotlib, Pandas, and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more.
After reading and using Numerical Python, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.
Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work.
What you’ll learn
- How to work with vectors and matrices using NumPy
- How to work with symbolic computing using SymPy
- How to plot and visualize data with Matplotlib
- How to solve linear and nonlinear equations with SymPy and SciPy
- How to solve solve optimization, interpolation, and integration problems using SciPy
- How to solve ordinary and partial differential equations with SciPy and FEniCS
- How to perform data analysis tasks and solve statistical problems with Pandas and SciPy
- How to work with statistical modeling and machine learning with statsmodels and scikit-learn
- How to handle file I/O using HDF5 and other common file formats for numerical data
- How to optimize Python code using Numba and Cython
Who this book is for
This practical book is for those practicing industry coders, data scientists, engineers, financial engineers, scientists, business managers and more who use or plan to use numerical Python techniques and methods.
Table of Contents
1. Introduction to computing with Python2. 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 processing
18. Data input and output
19. Code optimization
20. Appendix: Installation
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From the Back Cover
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include:
- How to work with vectors and matrices using NumPy
About the Author
Robert Johansson is an experienced Python programmer and computational scientist, with a PhD in Theoretical Physics from Chalmers University of Technology, Sweden. He has worked with scientific computing in academia and industry for over ten years and he has participated in both open source development and proprietary research 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, and about teaching and communicating best practices for bringing these fields together with optimal outcome: novel, reproducible, and extensible computational results. Robert’s background includes five years of post-doctorial research in theoretical and computational physics, and more recently he has taken on a role as data scientist in the IT industry.
"About this title" may belong to another edition of this title.
- PublisherApress
- Publication date2015
- ISBN 10 1484205545
- ISBN 13 9781484205549
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages512
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