 | Summaries and Customer Reviews are supplied by Amazon.com | We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts talks about turning many types of small data sources into useful visual data. And you will learn Python as part of the bargain. What you’ll learn - Write ten lines of code and present visual information instead of data soup.
- Set up an open source environment ready for data visualization.
- Forget Excel: use Python.
- Learn numerical and textual processing.
- Draw graphs and plots based on textual and numerical data.
- Learn how to deal with images.
Who is this book for? IT personnel, programmers, engineers, and hobbyists interested in acquiring and displaying data from the Web, sensors, economic trends, and even astronomical sources. About the Apress Beginning Series The Beginning series from Apress is the right choice to get the information you need to land that crucial entry–level job. These books will teach you a standard and important technology from the ground up because they are explicitly designed to take you from “novice to professional.” You’ll start your journey by seeing what you need to know—but without needless theory and filler. You’ll build your skill set by learning how to put together real–world projects step by step. So whether your goal is your next career challenge or a new learning opportunity, the Beginning series from Apress will take you there—it is your trusted guide through unfamiliar territory! |
Average Customer Rating:   Almost what I wanted it to be. This book really has hit a gap in the market. A lot of my use for Python requires visualizing the output and this was the first Python text I bought. Shai Vaingast has a novel approach in writing this - he starts with an example of reading data from a handheld GPS. This book teaches Python by stealth, using useful examples and offering both shortcuts and 'the proper way' to do things. It has chapters presenting the NumPy module and the SciPy module, as well as a brief introduction to the plotting library MatPlotLib.
The examples are all useful, and I've only found one that doesn't work as written in the book (because the web is fluent and the url for retrieving NASDAQ data has presumably changed). Given that the title is 'Beginning Python Visualization', I'd expected more on actually visualizing my data. Hopefully the next edition will have more of this, but the book is still really useful for someone new to Python. It's not a technical reference: this book is about learning.
Beginning Python Visualization is a great text for those new to data analysis and visualization in Python. I was left wanting more from this book though. What more do you need? Python is a fantastic scripting language with a massive set of libraries and contributed software. It is especially prolific in scientific and engineering fields. However, the very size of this domain can be daunting, and an experienced guide is virtually essential. The author is the guide that you want and this book is obviously the distillation of a tremendous amount of experience and thought on the subject.
Though I have using Python, numpy, SciPy and matplotlib (collectively, pylab) for some years this book has nevertheless been a revelation, with many great practical ideas and suggestions that I am already implementing in my work. The book is well organised, with good examples, the writing is clear and flowing - a real pleasure to read. A fun way to learn Python I'm not sure you'll know that much about visualization when you're done reading this book, but you'll have lots of fun and probably know enough Python to be dangerous. This reminds me of the fun books that came out about ten years ago written by authors that taught various languages - C, C++, Haskell, Java - by programming multimedia. You don't see very many of those around anymore, even though that's a great way to teach someone a new programming language - by letting them feel like they've accomplished something when they actually "see" the results of their code. That's what this book does. You're not really learning that much about the theory of visualization. You're learning how to graph filter plots, display images, and put up some interesting but basic graphics with Python and its libraries.
Note that to use this book you have to use some open source Python libraries such as scipy - Scientific Python - for example. If you want to learn visualization I recommend the books of Edward Tufte. If you are the no-nonsense type who wants to go from journeyman to expert Python programmer I suggest Learning Python. However, if you are just someone who needs to get something simple in the way of an interface going, and you'd like to try out Python as a language and tool, this might be the way to go. An Excellent Book Sometimes a picture is worth a thousand words. "Beginning Python Visualization: Creating Visual Transformation Scripts", published in February 2009 by Apress, shows how Python and its related tools can be used to easily and effectively turn raw data into visual representations that communicate effectively. The author is Shai Vaingast, a professional engineer and engineering manager who needed to train scientists and engineers to do this kind of programming work. He was looking for a tutorial and reference work, and unable to find a suitable text, wound up writing his first book. He wrote in the easy and clear style of someone comfortable and engaged with the subject matter.
The book uses several very specific examples that illustrate general principles.
The first example is using GPS data. By using Python one can extract data from GPS receivers and enter it into the computer and manipulate it to do what one wants including creating graphs and charts. In this section he shows how to use CSV, comma separated values, as a most useful file format. He shows show to extract data from real world GPS devices and import it via serial ports and the PySerial module. It would be easy for the reader to duplicate and extend this project.
The heart of the book is coverage of useful examples utilizing MatPlotLib, NumPy and SciPy. These related tools are easy to use and fully integrated with Python. MatPlotLib is for plotting data and graphs, including interactive graphs and image files. NumPy is a powerful math library comparable to commercial tools like MatLab, and SciPy extends NumPy to for the sciences. Examples are numerous and include signal analysis using Fourier transforms.
There is also a section on Image Processing using PIL, the Python Imaging Library. This is used for relatively simple image cropping and sizing and also for bit by bit image processing. Interpolation and curve fitting are also well covered. For anyone wanting an introduction to graphical analysis of statistical data, this would be an excellent resource.
The author is obviously a professional in this field. He has a knack for good organizational style and a pragmatic approach to the work. In the book he says "Most of the time, research is organized chaos. The emphasis, however, should be on organized, not chaos." A real value I got from the book is a better understanding of data files, format, and organization as well as methods and guidelines for selecting file formats and storing and organizing data to enable fast and efficient data processing. It is obvious that this book was written by a practicing engineer.
The theme of the book is that Python can be an all purpose environment for data manipulation and visualization, using nothing but free and open source tools that are easily integrated and scriptable without using multiple programming languages. The book should be an invaluable tool for scientists and engineers but it is also easily accessible to anyone interested in math and data analysis. There is no need for an advanced math background. While, as a matter of full disclosure, I have undergraduate degrees in Math and Physics, I feel the book should be easily accessible to anyone with a solid high school math background who is seriously interested in the subject. The book contains a short introductory tutorial on the basics of Python so anyone familiar with programming in any language should be fine.
The book is an easy read from front to back, and I am sure it will also be a good reference resource for the future. The writing style is very clear and unforced and I found surprisingly few errors. While the Python world has a surplus of introductory and general books, books covering this kind of specific domain are especially welcome, and we could use more on other topics by competent authors.
At 363 pages the book is a surprisingly fast read. Its methodology is to use specific, short code examples to make all the key points. Most of the code samples are well selected, short and written in clear, concise Python. This is not the kind of book that overwhelms you with massive amounts of code. Either the book was well edited or else it was written by an exceptionally lucid thinker, or both.
So, if you want to learn how to process, organize, and visualize data from various sources using the Python language, I recommend this book to you.
I also have posted a podcast of an interview I did with the author at
www.awaretek.com/python/index.html Very usefull book to learn python I started to read this book and installed python on my machine to practice python coding along the way. Indeed, this is the best way to learn.
This book is very well written and strongly suggested to anyone who wants to learn python. Clearly, the author have made an effort to organize this book to be fun and reader friendly.
Besides being educating, this book also suggests a methodology to organize large amounts of data. Some people I know might find this very useful.
Have fun to read this. I did. |  |
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