Download PDF Numerical Python: A Practical Techniques Approach for Industry
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Numerical Python: A Practical Techniques Approach for Industry
Download PDF Numerical Python: A Practical Techniques Approach for Industry
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Leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, Matplotlib, Pandas, 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 Numerical Python, 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.
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.
This practical book is for those practicing industry coders, data scientists, engineers, financial engineers, scientists, business managers and more who use or plan to use numerical Python techniques and methods.
2. Vectors, matrices and multidimensional arrays
3. Symbolic computing
4. Plotting and visualization
5. Equation solving
6. Optimization
7. Interpolation
8. Integration
9. Ordinary differential equations
10. Sparse matrices and graphs
11. Partial differential equations
12. Data processing and analysis
13. Statistics
14. Statistical modeling
15. Machine learning
16. Bayesian statistics
17. Signal processing
18. Data input and output
19. Code optimization
20. Appendix: Installation
Product details
Paperback: 512 pages
Publisher: Apress; 1 edition (October 2, 2015)
Language: English
ISBN-10: 1484205545
ISBN-13: 978-1484205549
Product Dimensions:
7 x 1.2 x 10 inches
Shipping Weight: 2.3 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#660,686 in Books (See Top 100 in Books)
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.
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.
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.
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.
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.
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.
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