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Python Data Visualization Cookbook [Kindle Edition]

Igor Milovanović
5.0 out of 5 stars  See all reviews (1 customer review)

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Product Description

Product Description

In Detail

Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries.

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts.

Python Data Visualization Cookbook starts by showing you how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.

This book will help those who already know how to program in Python to explore a new field – one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.


This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.

Who this book is for

Python Data Visualization Cookbook is for developers that already know about Python programming in general. If you have heard about data visualization but you don't know where to start, then this book will guide you from the start and help you understand data, data formats, data visualization, and how to use Python to visualize data.

You will need to know some general programming concepts, and any kind of programming experience will be helpful, but the code in this book is explained almost line by line. You don't need maths for this book, every concept that is introduced is thoroughly explained in plain English, and references are available for further interest in the topic.

About the Author

Igor Milovanovic

Igor Milovanovic is an experienced developer with a strong background in Linux system knowledge and software engineering. He is skilled at building scalable, data-driven, distributed-software-rich systems.

He is an Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies. He is always persistent on advocating methodologies that promote high-quality software, such as test-driven development, one-step builds, and continuous integration.

He also possesses a solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa.

Product details

  • Format: Kindle Edition
  • File Size: 7041 KB
  • Print Length: 282 pages
  • Page Numbers Source ISBN: 1782163360
  • Publisher: Packt Publishing (25 Nov. 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00GX9HM76
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #332,873 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
3 of 3 people found the following review helpful
5.0 out of 5 stars Exceedingly Good Cookbook & More! 8 Jan. 2014
By dmc
When I see words like "cookbook" and "recipes" my expectation doesn't extend beyond a collection of practical tips, possibly repackaged and augmented to provide some additional insight into the techniques being used. I also expect the bulk of such books to be sections that you dip into according to your current needs. This book works fine in this respect but sells itself short because it is so much more. The author has clearly put serious thought into the contents and included additional topics with enough breadth and depth that the book also serves as an introductory reference book, i.e., the sort you read from cover-to-cover. I realised this after reading the first couple of chapters and dipping into several others. There are so many useful tips that I decided to start again and read the entire book.

I especially like that the book draws on many other packages and modules (what people do in the real world) rather than restrict itself to simplified examples that then become redundant when your demands get more sophisticated. This starts almost immediately in chapter 1 and throughout the book with IPython and VirtualEnv. In fact, practically the only criticism I have of the entire book is that the installation of VirtualEnv should come first and it's virtues emphasised more strongly. In addition to these two, the choice of additional packages/modules leads to some very interesting and unexpected topics, including image processing, generating CAPTCHA images, geo-spatial mapping, 3D animation (+OpenGL) and many more. I have used matplotlib frequently but almost as an aside and only for simple charting. This book has introduced so many related topics that I will be re-visiting it regularly for some time yet.

Although recently published the book makes no mention of PANDAS.
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Most Helpful Customer Reviews on (beta) 5.0 out of 5 stars  6 reviews
9 of 9 people found the following review helpful
5.0 out of 5 stars What software documentation should be 29 Jan. 2014
By Amazon Customer - Published on
Format:Kindle Edition
The majority of software documentation is similar to a remark made by the developer of a well-known, difficult language; "Maybe you are not smart enough?". In contrast, this book has made sure that nothing is implied without being oversimplified. The book covers; installing and customizing libraries, reading in data, extensive information on 2D and 3D plots, using images and maps, determining the right plots for specified data types, and additional information for matplotlib. References are provided to other sources throughout the book.
7 of 7 people found the following review helpful
5.0 out of 5 stars Some great things about data visualization 21 Jan. 2014
By Yury - Published on
I'd like to recommend this book, for people, who makes their first steps in data visualization. Good compilation of free Python stuff for data analysis at one place. From the enviroment creating to the complicated plots.
There no any comments about Python as a language, so be sure that you know it quite good. No need to be a senior developer, but strong junior, would be nice.
4 of 4 people found the following review helpful
5.0 out of 5 stars good book to learn data visualization 19 Mar. 2014
By carl - Published on
I am an intermedium python developer. My past python experience is on system admin, DevOps, deployment and web management. Data visualization is a fairly new area to me. So this book is a perfect fit for me.

Author uses lots of examples to demonstrate different visualization terminology, which really helps people to understand the abstract image processing technology. This book also shows you how to setup the virtual env to isolate development environment. Although the main purpose of this book is to teach how to visualize data, many of the example programs also show the best python development practice. Majority of the code is runnable without touch-up. Some typos are pretty easy to be spotted. I would recommend it to people who already have python experience and would like to extend their experience to data visualization area.
4 of 5 people found the following review helpful
5.0 out of 5 stars Good introduction for Scientists to learn more about Data Visualization 25 Feb. 2014
By Jack Golding - Published on
Format:Kindle Edition
Python Data Visualization Cookbook introduces the process of doing data visualisation with the Python programming language. The book uses the Scipy stack for data visualisation (however was published before the new Bokeh package was released) and introduces how to install the libraries in multiple operating systems which can be a task in itself for those unfamiliar with Python. The book covers the basics of data visualization and touches on exploratory data analysis, mostly in a scientific context. Given the size of the field of data visualization, it is unrealistic to expect that a book can introduce the semantics of a programming language as well as all of its applications. In conclusion this book is recommended to professionals who are interested in scientific data visualisation with a novice level understanding of both mathematics and programming.
1 of 1 people found the following review helpful
5.0 out of 5 stars Gets you started right away 29 Nov. 2014
By Bernie Ongewe - Published on
Format:Kindle Edition
This is a nice tour of modules and techniques for importing and scrubbing data from various sources (CSV, databases, Excel, etc), manipulating said data and presenting it in an intuitive manner. The author is generous with examples, which allows you to start right away.

While this is not a rigorous tutorial, the author goes into exactly the right depth to allow you to make a decision on methodology and begin implementing right away.

If, rather than becoming a NumPy scholar, you expect to have to deliver results from varied species of data, having this in your back pocket will help you accomplish that.
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