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|a Johansson, Robert.
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| 245 |
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|a Numerical Python
|h [electronic resource] :
|b A Practical Techniques Approach for Industry /
|c by Robert Johansson.
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| 250 |
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|a 1st ed. 2015.
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| 264 |
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2015.
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|a XXII, 487 p. 54 illus. in color.
|b online resource.
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|a Springer eBook Collection
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|a 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 -- 20. Appendix: Installation.-.
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| 520 |
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|a 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 computat ional 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 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.
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