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024 7 _a10.1007/979-8-8688-0413-7
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072 7 _aCOM051360
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100 1 _aJohansson, Robert.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985221
245 1 0 _aNumerical Python
_h[electronic resource] :
_bScientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib /
_cby Robert Johansson.
250 _a3rd ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXX, 492 p. 165 illus., 155 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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347 _atext file
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505 0 _a1. 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.
520 _aLearn 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.
650 0 _aPython (Computer program language).
_964608
650 0 _aComputer software.
_972330
650 0 _aBig data.
_957216
650 0 _aArtificial intelligence.
_958441
650 1 4 _aPython.
_947553
650 2 4 _aMathematical Software.
_972331
650 2 4 _aBig Data.
_957216
650 2 4 _aArtificial Intelligence.
_958441
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868804120
776 0 8 _iPrinted edition:
_z9798868804144
776 0 8 _iPrinted edition:
_z9798868812385
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0413-7
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
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