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Numerical Python
Numerical Python by Robert Johansson shows you how to leverage the numerical
and mathematical modules in Python and its Standard Library as well as popular
open source numerical Python packages like NumPy, FiPy, matplotlib and more to
numerically compute solutions and mathematically model applications in a number
of areas like big data, cloud computing, financial engineering, business management
and more.
After reading and using this book, you’ll have some case study examples of
applications that can be found in areas like business management, big data/cloud
computing, financial engineering (i.e., options trading investment alternatives), and
even games.
Up until very recently, Python was mostly regarded as just a web scripting
language. Well, computational scientists and engineers have recently discovered
the flexibility and power of Python to do more. Big data analytics and cloud
computing programmers are seeing Python’s immense use. Financial engineers are
also now employing Python in their work. Python seems to be evolving as a language
that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical
computations.
• How to plot and graph with matplotlib
• How to construct vectors and matrices with NumPy
• How to solve linear/non-linear equations with NumPy
• How to solve equations with finite element methods using FiPy
• How to handle image processing with Pillow, matplotlib and OpenCV
• How to do numerical computations, interpolations and optimizations using SciPy
• How to handle file I/O, data encoding/compression, and text/date/time processing
9781484 205549
55999
ISBN 978-1-4842-0554-9
Numerical Python: A Practical Techniques Approach for Industry
Robert Johansson
Urayasu, Chiba, Japan
ISBN-13 (pbk): 978-1-4842-0554-9 ISBN-13 (electronic): 978-1-4842-0553-2
DOI 10.1007/978-1-4842-0553-2
Library of Congress Control Number: 2015952828
Copyright © 2015 by Robert Johansson
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material
is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting,
reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval,
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developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly
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for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only
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The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified
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While the advice and information in this book are believed to be true and accurate at the date of publication, neither
the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may
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Printed on acid-free paper
v
Contents at a Glance
About the Author ���������������������������������������������������������������������������������������������������xvii
About the Technical Reviewer ��������������������������������������������������������������������������������xix
Introduction ������������������������������������������������������������������������������������������������������������xxi
■Chapter 1: Introduction to Computing with Python ����������������������������������������������� 1
■Chapter 2: Vectors, Matrices, and Multidimensional Arrays ������������������������������� 25
■Chapter 3: Symbolic Computing �������������������������������������������������������������������������� 63
■Chapter 4: Plotting and Visualization ������������������������������������������������������������������ 89
■Chapter 5: Equation Solving ������������������������������������������������������������������������������ 125
■Chapter 6: Optimization ������������������������������������������������������������������������������������� 147
■Chapter 7: Interpolation ������������������������������������������������������������������������������������ 169
■Chapter 8: Integration ��������������������������������������������������������������������������������������� 187
■Chapter 9: Ordinary Differential Equations �������������������������������������������������������� 207
■Chapter 10: Sparse Matrices and Graphs ���������������������������������������������������������� 235
■Chapter 11: Partial Differential Equations ��������������������������������������������������������� 255
■Chapter 12: Data Processing and Analysis �������������������������������������������������������� 285
■Chapter 13: Statistics ���������������������������������������������������������������������������������������� 313
■Chapter 14: Statistical Modeling ����������������������������������������������������������������������� 333
■Chapter 15: Machine Learning �������������������������������������������������������������������������� 363
■Chapter 16: Bayesian Statistics������������������������������������������������������������������������� 383
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