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010 _a 2019763095
020 _a9781484242469
024 7 _a10.1007/978-1-4842-4246-9
_2doi
035 _a(DE-He213)978-1-4842-4246-9
040 _aDLC
_beng
_epn
_erda
_cDLC
072 7 _aCOM051360
_2bisacsh
072 7 _aUMX
_2bicssc
072 7 _aUMX
_2thema
082 0 4 _a005.133
_223
_bJOH
100 1 _aJohansson, Robert,
100 1 _eauthor.
245 1 0 _aNumerical Python :
_bScientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib /
_cby Robert Johansson.
250 _a2nd ed. 2019.
260 _bApress,
_c2023.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2019.
300 _axxiii, 700 pages :
_b168 illustrations, 63 illustrations,
_c24 cm.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _t1. Introduction to Computing with Python --
_t2. Vectors, Matrices and Multidimensional Arrays --
_t3. Symbolic Computing --
_t4. Plotting and Visualization --
_t5. Equation Solving --
_t6. Optimization --
_t7. Interpolation --
_t8. Integration --
_t9. Ordinary Differential Equations --
_t10. Sparse Matrices and Graphs --
_t11. Partial Differential Equations --
_t12. Data Processing and Analysis --
_t13. Statistics --
_t14. Statistical Modeling --
_t15. Machine Learning --
_t16. Bayesian Statistics --
_t17. Signal and Image Processing --
_t18. Data Input and Output --
_t19. Code Optimization.
520 _aLeverage 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.
588 _aDescription based on publisher-supplied MARC data.
650 0 _aArtificial intelligence.
650 0 _aBig data.
650 0 _aComputer software.
650 0 _aPython (Computer program language).
650 1 4 _aPython.
650 2 4 _aArtificial Intelligence.
650 2 4 _aBig Data.
650 2 4 _aMathematical Software.
650 1 4 _0https://scigraph.springernature.com/ontologies/product-market-codes/I29080
650 2 4 _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _0https://scigraph.springernature.com/ontologies/product-market-codes/I29120
650 2 4 _0https://scigraph.springernature.com/ontologies/product-market-codes/M14042
776 0 8 _iPrint version:
_tNumerical Python
_z9781484242452
_w(DLC) 2018966798
776 0 8 _iPrinted edition:
_z9781484242452
776 0 8 _iPrinted edition:
_z9781484242476
776 0 8 _iPrinted edition:
_z9781484246481
906 _a0
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