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Mastering Python scientific computing : a complete guide for Python programmers to master scientific computing using Python APIs and tools /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mehta, Hemant Kumar (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Mehta, Hemant Kumar,  |e author. 
245 1 0 |a Mastering Python scientific computing :  |b a complete guide for Python programmers to master scientific computing using Python APIs and tools /  |c Hemant Kumar Mehta. 
246 3 0 |a Complete guide for Python programmers to master scientific computing using Python APIs and tools 
264 1 |a Birmingham, UK :  |b Packt Publishing,  |c 2015. 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Community experience distilled 
588 0 |a Online resource; title from cover page (Safari, viewed October 12, 2015). 
500 |a Includes index. 
505 0 |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Landscape of Scientific Computing -- and Why Python?; Definition of scientific computing; A simple flow of the scientific computation process; Examples from scientific/engineering domains; A strategy for solving complex problems; Approximation, errors, and associated concepts and terms; Error analysis; Conditioning, stability, and accuracy; Backward and forward error analysis; Is it okay to ignore these errors?; Computer arithmetic and floating-point numbers 
505 8 |a The background of the Python programming languageThe guiding principles of the Python language; Why Python for scientific computing?; Compact and readable code; Holistic language design; Free and open source; Language interoperability; Portable and extensible; Hierarchical module system; Graphical user interface packages; Data structures; Python's testing framework; Available libraries; The downsides of Python; Summary; Chapter 2: A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes; Mathematical components of scientific computations 
505 8 |a A system of linear equationsA system of nonlinear equations; Optimization; Interpolation; Extrapolation; Numerical integration; Numerical differentiation; Differential equations; The initial value problem; The boundary value problem; Random number generator; Python scientific computing; Introduction to NumPy; The SciPy library; The SciPy Subpackage; Data analysis using pandas; A brief idea of interactive programming using IPython; IPython parallel computing; IPython Notebook; Symbolic computing Using SymPy; The features of SymPy; Why SymPy?; The plotting library; Summary 
505 8 |a Chapter 3: Efficiently Fabricating and Managing Scientific DataThe basic concepts of data; Data storage software and toolkits; Files; Structured files; Unstructured files; Database; Possible operations on data; Scientific data format; Ready-to-use standard datasets; Data generation; Synthetic data generation (fabrication); Using Python's built-in functions for random number generation; Bookkeeping functions; Functions for integer random number generation; Functions for sequences; Statistical-distribution-based functions; Nondeterministic random number generator 
505 8 |a Designing and implementing random number generators based on statistical distributionsA program with simple logic to generate five-digit random numbers; A brief note about large-scale datasets; Summary; Chapter 4: Scientific Computing APIs for Python; Numerical scientific computing in Python; The NumPy package; The ndarrays data structure; File handling; Some sample NumPy programs; The SciPy package; The optimization package; The interpolation package; Integration and differential equations in SciPy; The stats module; Clustering package and spatial algorithms in SciPy 
546 |a English. 
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650 0 |a Python (Computer program language) 
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650 0 |a Electronic data processing. 
650 6 |a Python (Langage de programmation) 
650 6 |a Informatique. 
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830 0 |a Community experience distilled. 
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