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Numerical Python: A Practical Techniques Approach for Industry 1st ed. Edition
There is a newer edition of this item:
Numerical Pythonby 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 get some takeaway 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.
- ISBN-101484205545
- ISBN-13978-1484205549
- Edition1st ed.
- Publication dateOctober 2, 2015
- LanguageEnglish
- Dimensions7 x 1.16 x 10 inches
- Print length512 pages
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Editorial Reviews
Review
“Python’s numerical and mathematical modules aren’t just appreciated by coders working in the sciences … . It is for these fields that Johansson has written this detailed guide. … Johansson helps you brush up on problem solving, mathematics, algorithms, data, and even serialisation. … The book is a valuable reference across many fields.” (The MagPi, Issue 43, March, 2016)
From the Back Cover
- 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 handle file I/O using HDF5 and other common file formats for numerical data
- How to optimize Python code using Numba and Cython
About the Author
Product details
- Publisher : Apress; 1st ed. edition (October 2, 2015)
- Language : English
- Paperback : 512 pages
- ISBN-10 : 1484205545
- ISBN-13 : 978-1484205549
- Item Weight : 1.96 pounds
- Dimensions : 7 x 1.16 x 10 inches
- Best Sellers Rank: #5,126,464 in Books (See Top 100 in Books)
- #779 in Software Programming Compilers
- #1,409 in Mathematical & Statistical Software
- #5,277 in Python Programming
- Customer Reviews:
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- Reviewed in the United States on February 19, 2016In the last 50 years there are two things that have emerged in a technological world. First, applied mathematics has moved much more into numerical methods than in trying to solve problems analytically. The second thing that has emerged is that computing has both led and followed the numerical computing revolution. Python, amongst languages, is arguably a language with links to optimized code (such as C or Fortran) plus a language capable of a plethora of tasks, including scientific calculation, statistical modelling, network analysis, machine learning, language processing, and so forth. Johansson's book fits beautifully into a niche where serious science or other endeavour requires both some cookbook code and explanation of some basics. This book steps beautifully through from setting up to topics that will help a person with intermediate mathematical understanding and basic Python programming skill implement practical and useful code. There is a coding consistency that allows the user to add and modularise code blocks, if required. There is the support of code online. As a fairly critical consumer of literature purporting to be of practical industry use, my sense is that this book exceeds expectations.
- Reviewed in the United States on February 27, 2018Great book; I chose it because I wanted to go deeper into Python for mathematical calculations. The book will walk you through the packages you need to perform several calculations in scientific computing with Python. It will tell you how to install the packages, how to launch them, and how to use them. Check the table of contents to confirm the topics you're looking for are covered.
- Reviewed in the United States on September 1, 2016This is a true gem! If you are looking for a single book to get you up to speed on numerical and scientific computing in Python this is it. The book is full of useful code snippets and the all the code is available through github. What is unique about this book is the breadth of numerical methods applications it covers including from non-linear equation solving to ode's and pde's and everything in between. It even features chapters on statistics and machine learning. The last chapter deals with code optimization including a discussion of Cython. There is also a very nice short (100 page) summary of the book available from the authors github account (google it) which contains even material not in the book on parallel computing via MPI, OpenMP (via Cython), and GPU (using pyopencl). I highly recommend it.
- Reviewed in the United States on June 11, 2018Wonderful book, by far the best I have found about SymPy. Goes through a large selection of topics and will get you ready for math in Python.
- Reviewed in the United States on December 9, 2016Great introductions to Python mathematics/science packages presented in a much friendlier format than typical on-line documentation. Important methods are emphasized and coverage is extensive. Provides a general orientation to standard practices, what can be accomplished, and where to go for further details. This is a good place to start before digging into on-line docs.
- Reviewed in the United States on October 8, 2017I was very frustrated that every single line of code included in the book was typed on an interactive tool. This is NOT how things are done in industry. The author should have shown the algorithms in terms of .py files and how you call python files from other programs. So I download the code from GitHub hoping I'll find the answer there. Yep, there are the .py files. However, the author comments every line as "IN[1]", OUT[1], etc. It is just a comment so that is OK, but still, I wish that the code had been shown as .py files in the book.
Top reviews from other countries
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PL76Reviewed in Germany on March 2, 2016
5.0 out of 5 stars Sehr gutes Kochbuch für Python-Anwender
Dieses Buch ist ein sehr gutes Kochbuch für Python-Anwender, die häufig mit den wohlbekannten (open source) Python-Libraries NumPy, SciPy, SimPy, Matplotlib usw. arbeiten.
Das Buch ist nicht geeignet für Python-Einsteiger - es werden hier keine Grundlagen der Programmierung und insbesondere, um Python zu lernen, gehandelt. Dafür eignen sich andere Bücher, m.E. am Besten zu empfehlen ist "Beginning Python: From Novice to Professional" (2nd Edition) von Magnus Lie Hetland.
Für diejenigen, die stattdessen schon einigermaßen fit in Programmieren sind und Python gut kennen, kann die Nutzung der o.g. Bibliotheken, die nicht zur Python Standard Library gehören, in Frage kommen. Besonders, wenn Python als Tool für technische/wissenschaftliche Berechnungen dienen soll. Leider gibt es viel zu viel über diese Bibliotheken in Internet-Foren sowie Tausende von Seiten auf den offiziellen Webseiten.
Um nicht zu viel Zeit in Stöbern und Lesen zu verlieren und schnell einsteigen zu können, hilft dieses Buch wirklich sehr. Hier wird die Verwendung von NumPy, SimPy und Matplotlib in den ersten Kapiteln gezeigt, um eine Vielzahl an möglichen Aufgaben zu erledigen. Dies wird dann die Basis sein, um sowohl rechnerisch als auch graphisch weitere Probleme anzugehen. Die Lösung von Gleichungssystemen sowie gewöhnlichen und partiellen Differentialgleichungen (ganz interessant der Teil über die Finite Elementenmethode), die Integralrechnung, Statistik, Machine Learning werden gut und praktisch erklärt - auf die passende Bibliothek für jedes von diesen Themen wird auch eingegangen.
Last but not least: das letzte Kapitel beschäftigt sich mit der Codeoptimierung bzw. mit der Steigerung der Rechenschnelligkeit durch just-in-time Kompilation (via Numba) und ahead-of-time Kompilation (Cython). Leider handelt es sich lediglich um Vorgeschmack, denn es gibt weiterführende Bücher zu den Themen (für Cython sehr zu empfehlen ist "Cython - A Guide for Python Programmers" von Kurt W. Smith), die wesentlich ausführlicher sind.
Insgesamt aber ein Buch, das mir sehr gut gefallen hat.
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Amazon KundeReviewed in Germany on February 28, 2017
5.0 out of 5 stars Der standard für numpy
Das Buch führt alle Aspekte von numpy aus, und stellt z.B. das ndarray mit allen möglichen Filtern dar. Hat mir beim Verständnis des slicing sehr geholfen.