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Top (max 10) reviews: Numerical Python: A Practical Techniques Approach for Industry.

4.3 out of 5.0    4 total reviews.

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5.0 out of 5.0 -

by SeferTech on Sept. 1, 2016

This 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.

5.0 out of 5.0 -

by Llewelyn on Feb. 18, 2016

In 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.

5.0 out of 5.0 -

by Amazon Customer on Dec. 9, 2016

Great 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.

3.0 out of 5.0 -

by calvinnme on Oct. 8, 2017

I 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.