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Mastering Python Scientific Computing, by Hemant Kumar Mehta
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A complete guide for Python programmers to master scientific computing using Python APIs and tools
About This Book- The basics of scientific computing to advanced concepts involving parallel and large scale computation are all covered.
- Most of the Python APIs and tools used in scientific computing are discussed in detail
- The concepts are discussed with suitable example programs
If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming.
What You Will Learn- Fundamentals and components of scientific computing
- Scientific computing data management
- Performing numerical computing using NumPy and SciPy
- Concepts and programming for symbolic computing using SymPy
- Using the plotting library matplotlib for data visualization
- Data analysis and visualization using Pandas, matplotlib, and IPython
- Performing parallel and high performance computing
- Real-life case studies and best practices of scientific computing
In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing.
At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python.
The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs.
Style and approachThis book follows a hands-on approach to explain the complex concepts related to scientific computing. It details various APIs using appropriate examples.
- Sales Rank: #545210 in Books
- Published on: 2015-10-01
- Released on: 2015-09-23
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .68" w x 7.50" l, 1.14 pounds
- Binding: Paperback
- 345 pages
About the Author
Hemant Kumar Mehta
Hemant Kumar Mehta is a distributed and scientific computing enthusiast. He has more than 13 years of experience of teaching, research, and software development. He received his BSc (in computer science) Hons., master of computer applications degree, and PhD in computer science from Devi Ahilya University, Indore, India in 1998, 2001, and 2011, respectively. He has experience of working in diverse international environments as a software developer in MNCs. He is a post-doctorate fellow at an international university of high reputation. Hemant has published more than 20 highly cited research papers in reputed national and international conferences and journals sponsored by ACM, IEEE, and Springer. He is the author of Getting Started with Oracle Public Cloud, Packt Publishing. He is also the coauthor of a book named Internet and Web Technology, published by Kaushal Prakashan Mandir, Indore. He earned his PhD in the field of cloud computing and big data. Hemant is a member of ACM (Special Interest Group on High-performance Computing Education: SIGHPC-Edu), senior member of IEEE (the computer society, STC on cloud computing, and the big data technical committee), and a senior member of IACSIT, IAENG, and MIR Labs.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Some useful content but more depth required
By PJG
Rather than 'mastering', this book is really a fairly advanced introduction to core Python scientific libraries. A number of topics are covered, in addition to the de facto standard of NumPy, SciPy, iPython and Matplotlib. There is no mention of SciKit-Learn and Pandas is introduced in just 14
pages. However, there are a few chapters with some really interesting content, and here is where the book could shine. Chapter 6 introduces SymPy applications for mathematical computing, and these could almost warrant a stand-alone book if the material was expanded; as it is, the content is disappointing. SymPy is introduced in less than ten pages, but the scientific examples are literally just a page of code with no discussion or comments other than a terse introduction. If it was assumed, as it seems to be, that the reader is very familiar with the second quantisation of the 1-D quantum harmonic oscillator, then surely there is little point in presenting a block of code when a search for documentation would be easier? Chapter 8, on 'Parallel and Large-Scale Scientific Computing' is much better and richer in useful content.
Overall I wanted to like the book but the content is rather patchy: lacking in some places and rather undeveloped. Really, the book could do with much more detail in some sections as they honestly feel lightweight, and this is a shame as there are some topics here which are not covered in many other books. More depth would be needed to fully recommend this title.
0 of 0 people found the following review helpful.
Highly Recommend
By Bill Jones
This book has huge potential, and like a few of the fellow reviewers I also agree that it could have used a lot more discussion in a few of the advanced areas. However, since the bulk of this book delivers on Scientific Computing I was really satisfied overall with the book. The real-life case studies and best practices were what sealed the deal for me. The previous 8 chapters are great, but Chapter 10 made me feel like I accomplished something, while also ending with friendly reminders or gotcha's. Security is something that you don't find covered in most programming books, but this book did indeed offer some guidance on it in Chapter 10. I guess I really enjoyed the final chapter a lot, but this book is something I'll have to read over a few dozen times to allow the deep technical aspects sink into my cranium a lot more.
1 of 1 people found the following review helpful.
Not much depth unfortunately and no mastery
By Tim Crothers
I had high hopes for this book as there aren't a lot of solid resources for using Python for scientific computing. Unfortunately in the end the author tried to cover too much and as a result the information is very surface level with insufficient depth to be useful in most cases, no "mastery" will be gained from it. There are a few gems (hence the 3 stars) and the book does an okay job of covering the topics germane to scientific computing so is useful for those looking to understand the options at a high level. I find that more specific books on topics like NumPy, pandas, and SciPy are far more useful for mastering than the simple treatment they are given here.
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