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Mastering Python Data Visualization, by Kirthi Raman
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Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences.
This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis.
By the end of this book, you will be able to effectively solve a broad set of data analysis problems.
- Sales Rank: #1354189 in eBooks
- Published on: 2015-11-04
- Released on: 2015-11-04
- Format: Kindle eBook
About the Author
Kirthi Raman Kirthi Raman is currently working as a lead data engineer with Neustar Inc, based in Mclean, Virginia USA. Kirthi has worked on data visualization, with a focus on JavaScript, Python, R, and Java, and is a distinguished engineer. Previously, he worked as a principle architect, data analyst, and information retrieval specialist at Quotient, Inc. Kirthi has also worked as a technical lead and manager for a start-up. He has taught discrete mathematics and computer science for several years. Kirthi has a graduate degree in mathematics and computer science from IIT Delhi and an MS in computer science from the University of Maryland. He has written several white papers on data analysis and big data.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Dissapointing
By Luis Miguel Soares
I am giving 2 stars to this review because I assume that the author spent a lot of time writing it and there somewhere buried in the book there are some good ideas. However I doubt that this book was professionally edited and having Packt charge more than 50 dollars for this is an outrage. The book is a big mess, there is no flow in the subjects presented, even inside the same chapter there are conceptual leaps that make the reading feel like a roller coaster with basic notions interspersed with advanced algorithms. Take the chapter on Bioinformatics most of the material is about graphs and analysis of social networks, I agree that social networks are a good introduction to graphs but I doubt they can be classified as bioinformatics (in the bioinformatics there is something else that bothers me, they use code from another book's examples file without properly citing the other work, I assume they actually asked permission to the authors but I still think is a little bit shady). Even if you try to focus on the book, it becomes impossible since it is riddled with misplaced code, random phrases that are also misplaced, page filler of installation logs that contribute nothing and errors (on page 285 does that graph correspond to the code? is it even a valid graph?). Overall I kept putting the book down in frustration from all the hurdles. I am sorry for the author but I cannot recommend this version of the book.
0 of 0 people found the following review helpful.
Liked a lot, it's inspiring, discipline-agnostic, but a bit hard and lacking specifics and some order.
By yoalieh
I liked this book, though it's not easy to be loved.
I'd liked the introduction a lot, as the author talked about data visualization as a discipline, and gave some tips and ideas of diferent kind of visualizations (There's is a lot more than graph bars and scatterplots it seems, ;) ). It tries to be discipline-agnostic by using many real life examples from many disciplines. I think this can bring inspiration when in need of a way to present information hard to explain.
After that, when talking about Python, it gives an overview about Python versions and libraries which can simplify the process of creating good visualizations. Finally, almost all examples are based in Conda, but still other things are used. This can cause a bit of confussion, but I see it as one of the potential of this book, as it can be used as reference to create good visualizations in different workflows, and serves as a reference about which libraries can be used for a special kind of visualization if it's not covered by one of them.
The examples in further chapters are very good, and I loved when it talks about Numpy, simulation, or advanced data structures, all of which can be used to create better visualization, or even the part talking about drawing graphs.
Don't expect this book to be a cookbook, it's more like a big notebook of a professional in charge of creating a LOT of visualizations for different fields. I think it lacks a bit of more explaining on some specfic examples or libraries, but that would require a lot more books to fit them. Also, a very good level of Python understanding, and documentation for each library in use is not only recommended, but a must.
0 of 0 people found the following review helpful.
Great ideas but the path to application isn't always clear
By Amazon Customer
[Disclaimer: Packt Publishing asked me to review the book in light of my Github public profile. I was given complete editorial freedom and NOT compensated in anyway for the review however]
Overall, I enjoyed this book, although I suspect it's real value will become apparent when I return to it over the next few years when faced with visualising tricky datasets. Broadly, Kirthi Raman covers three areas: Introducing visualisation as an activity itself (he considers it a form of story telling), several Python tools for visualisation and analytic techniques that can drive the visualisation/modelling process. I particularly like that a plethora of approaches are encouraged, so that if you find one isn’t suited to what you’re doing, there are always plenty other to consider. As someone who uses Python on a daily basis to both model and visualise a variety of data sources, Raman's book is an important addition to my professional library.
Where I find the book lacking is in providing a clear path to applying the array of techniques and packages suggested. To be clear, there are good code examples for almost every visualisation/analytic technique (the financial models are particularly well explained), but I would have liked more explanation/worked examples of going from a raw dataset to a professional visualisation.
Another minor criticism is that it is quite ambitious in its scope (there are whole journals devoted to some of the modelling techniques covered in a few pages), but by making the reader aware of these approaches, the reader can always read further.
To end on a practical note, I like that the publisher makes the book available in multiple formats, including Kindle and DRM-free PDF. This is very practical for reading (and using) the book over multiple devices. I would recommend a colour display though, so as to enjoy the full effect of the many visualisation examples.
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