TY  - BOOK
AU  - Johansson,Robert
TI  - Numerical python: Scientific computing and data science applications with numpy, SciPy and matplotlib 
SN  - 9781484242452 (print)
AV  - QA76.73.P98 
U1  - 005.1372  2
PY  - 2022///
CY  - New York
PB  - Apress
KW  - Symbolic computing
KW  - optimization
KW  - Data processing and analysis
KW  - Electronic books
KW  - local
N1  - 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; Requires an SPL library card
N2  - Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for 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
UR  - https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484242469/?ar
ER  -