Mastering Python Data Visualization: Generate effective results in a variety of visually appealing charts using the plotting packages in Python

Kirthi Raman

Mex$1128.99

4.5 (4 Ratings)

Paperback Oct 2015 372 pages 1st Edition

eBook

Mex$631.99 ~~Mex$902.99~~

Kirthi Raman

Mex$1128.99

4.5 (4 Ratings)

Paperback Oct 2015 372 pages 1st Edition

eBook

Mex$631.99 ~~Mex$902.99~~

Mex$631.99 ~~Mex$902.99~~

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Description

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.

What you will learn

Gather, cleanse, access, and map data to a visual framework
Recognize which visualization method is applicable and learn best practices for data visualization
Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception
Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it
Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics
Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning
Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js
Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment

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Frequently bought together

Mex$1128.99

Python Data Visualization Cookbook (Second Edition)

Mex$1004.99

Mex$1004.99

Total Mex$ 3,138.97

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Math Review Nov 17, 2015

I have not completely read the book yet, I liked the examples in sports and Monte Carlo Simulations.

Amazon Verified review

Oleg Okun Dec 06, 2015

As its title says, this book is about exploration of data visualization in Python. The author approaches to this task by not only featuring the available Python functionality to visualize data, but by putting it into the context of "visualization of information for knowledge inference". In his words, this means not the visualization per se, but knowledge discovery aimed visualization which is the integral part of data science related projects. With this goal in mind, the author conveys readers through a number of real-world stories (taken from finance, sports, bioinformatics, natural language processing) accompanied by plots of various kinds.As a Python distribution, Anaconda is chosen as it includes many pre-installed packages. As any knowledge discovery assumes data processing and analysis, the discussion is also on numpy, scipy, matplotlib, scikit-learn, NetworkX, bokeh, IPython, plotly and a few others less commonly known packages as well as on their application to the book main topic.The book presumes some background knowledge of Python from readers and therefore it is best suitable for those who have exposure to Python programming but wants to acquire data visualization skills, meaning the typical job titles of a data analyst or a data engineer.

yoalieh Dec 09, 2015

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.

Amazon Customer Dec 12, 2015

[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.

Mastering Python Data Visualization: Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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