Numerical Python: A Practical Techniques Approach for Industry
Product ID: 66126410
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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) Read more From the Back Cover Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work.            After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include: How to work with vectors and matrices using NumPyHow to work with symbolic computing using SymPyHow to plot and visualize data with MatplotlibHow to solve linear and nonlinear equations with SymPy and SciPyHow to solve solve optimization, interpolation, and integration problems using SciPyHow to solve ordinary and partial differential equations with SciPy and FEniCSHow to perform data analysis tasks and solve statistical problems with Pandas and SciPyHow to work with statistical modeling and machine learning with statsmodels and scikit-learnHow to handle file I/O using HDF5 and other common file formats for numerical dataHow to optimize Python code using Numba and Cython Read more About the Author Robert Johansson is an experienced Python programmer and computational scientist, with a PhD in Theoretical Physics from Chalmers University of Technology, Sweden. He has worked with scientific computing in academia and industry for over ten years and he has participated in both open source development and proprietary research projects. His open source contributions include work on QuTiP, a popular Python framework for simulating the dynamics of quantum systems, and he has also contributed to several other popular Python libraries in the scientific computing landscape. Robert is passionate about scientific computing and software development, and about teaching and communicating best practices for bringing these fields together with optimal outcome: novel, reproducible, and extensible computational results. Robert’s background includes five years of post-doctorial research in theoretical and computational physics, and more recently he has taken on a role as data scientist in the IT industry. Read more
L**N
Practical indeed, very usable, and an interesting read beyond one's own domain.
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.
M**L
Great book
Great 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.
S**H
A tasty meaty treat of python and real numerical methods...will get you coding fast.
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.
A**R
Start Here First
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.
A**S
Best current SymPy book
Wonderful 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.
C**E
I was very frustrated that every single line of code ...
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.
Trustpilot
1 week ago
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