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Python Data Visualization Cookbook
Over 60 recipes that will enable you to learn how to create attractive visualizations using Python's most popular libraries
Overview
Learn how to set up an optimal Python environment for data visualization
Understand the topics such as importing data for visualization and formatting data for visualization
Understand the underlying data and how to use the right visualizations
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.
What you will learn from this book
Install and use iPython
Use Python's virtual environments
Install and customize NumPy and matplotlib
Draw common and advanced plots
Visualize data using maps
Create 3D animated data visualizations
Import data from various formats
Export data from various formats
Approach
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.
Overview
Learn how to set up an optimal Python environment for data visualization
Understand the topics such as importing data for visualization and formatting data for visualization
Understand the underlying data and how to use the right visualizations
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.
What you will learn from this book
Install and use iPython
Use Python's virtual environments
Install and customize NumPy and matplotlib
Draw common and advanced plots
Visualize data using maps
Create 3D animated data visualizations
Import data from various formats
Export data from various formats
Approach
This book is written in a Cookbook style targeted towards an advanced audience. It covers the advanced topics of data visualization in Python.
263 pages, Paperback
First published January 1, 2013
6 people are currently reading
65 people want to read
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Displaying 1 - 9 of 9 reviews
February 3, 2014
Having just started my career as a web developer two years ago, my weapon of choice was PHP. Naturally, not being a mature enough developer to fully understand the power of PHP, I quickly found caveats, even though the community was great, and the language and tools that came with it were oh so simple to understand and use. I quickly started to look into Python about a year ago, by starting to use the Django framework. I was quickly hooked by all that was Python, and found myself using it more and more, both casually and professionally.
Wanting to get better at Python, I looked around for a good book for me to read. I had gathered so much information about Python, but it had no structure, no fundamentals inside my head. I found Tarek Ziadé's "Expert Python Programming" (http://www.packtpub.com/expert-python...) from Packt Publishing and my mind was blown. There was so much of the professional way of doing things with Python that I didn't even start to think about, or thought about it but was scared that it's not the time yet, I would not even be able to comprehend it. I was wrong. I still use it as a reference in my everyday job, and kindly, fullheartedly recommend it to anyone.
Having my mind set out to explore big data and the more statistical part of the web, I then searched for another book that could teach me something regarding the new direction I wanted to head to. Being satisfied by the customer support and general website UI/UX, I naturally began my search on Packt's web application. I found Igor Milovanović's Python Data Visualization Cookbook, (http://www.packtpub.com/python-data-v...), and skeptically downloaded it. I say 'skeptically', because cookbooks are not usually my go-to option when it comes to expanding my programming knowledge in general -- I find them too short and technical, with few to null of those much needed explanations that I require in order to understand the subject I'm interested in.
I was, again, surprised. Not only the book has thorough explanations of the recipes, but it also comes with great tips and tricks for Python programming in general. The author made sure you first and foremost grasp the concept of data visualization, the need for it, and application in real life scenarios, before starting the teaching process.
I discovered a whole array of tools that I wasn't aware of, that can be used not only for data visualization, but for my every day Python programming. After setting up my environment, by installing the somewhat dependence heavy libraries needed for going through the cookbook, I started to get frightened by my lack of knowledge in the field of big data manipulation in general. With math not being my forte, and no formal training in statistics, I surprisingly was not caught off-guard by the recipes. There are not advanced math requirements or statistical foundation required in order to understand the cookbook, and the author makes the recipes relatively easy to understand. When we encounter strange or unfamiliar terms, we get explanations or links towards wikipedia or web-standard pages that explain the terminology.
With a broad spectrum of real-life applications, and sharing crucial data manipulation techniques, like noise reduction or handling various data formats, the author makes sure you're prepared for most of the scenarios you might bump into, be you a researcher, software developer (CRM's, ERP's, products that usually require data visualization) or active in academia. But it doesn't stop here. By expanding the already thick information layer, the book takes us, with baby steps, through key concepts in 3D data visualization, OpenGL, working with the Google Maps API for plotting statistical information based on geographical location, but also addresses more discrete subjects as choosing the right colour for your presentation depending on your target demographic, or understanding what data visualization technique can best supply the audience with valuable info, regarding the nature of the information you want to emphasize.
As all cookbooks, it won't make you a field expert by reading it, but it will surely carve an image of what Python can do regarding data visualization, and that's the most important thing, based on my personal experience. Learning what a tool can do means that whenever you may need it, the information is only a click or a page away. I'd recommend it for both professionals that already work in the subject, and beginner-intermediate curiosity-driven enthusiasts as myself.
Wanting to get better at Python, I looked around for a good book for me to read. I had gathered so much information about Python, but it had no structure, no fundamentals inside my head. I found Tarek Ziadé's "Expert Python Programming" (http://www.packtpub.com/expert-python...) from Packt Publishing and my mind was blown. There was so much of the professional way of doing things with Python that I didn't even start to think about, or thought about it but was scared that it's not the time yet, I would not even be able to comprehend it. I was wrong. I still use it as a reference in my everyday job, and kindly, fullheartedly recommend it to anyone.
Having my mind set out to explore big data and the more statistical part of the web, I then searched for another book that could teach me something regarding the new direction I wanted to head to. Being satisfied by the customer support and general website UI/UX, I naturally began my search on Packt's web application. I found Igor Milovanović's Python Data Visualization Cookbook, (http://www.packtpub.com/python-data-v...), and skeptically downloaded it. I say 'skeptically', because cookbooks are not usually my go-to option when it comes to expanding my programming knowledge in general -- I find them too short and technical, with few to null of those much needed explanations that I require in order to understand the subject I'm interested in.
I was, again, surprised. Not only the book has thorough explanations of the recipes, but it also comes with great tips and tricks for Python programming in general. The author made sure you first and foremost grasp the concept of data visualization, the need for it, and application in real life scenarios, before starting the teaching process.
I discovered a whole array of tools that I wasn't aware of, that can be used not only for data visualization, but for my every day Python programming. After setting up my environment, by installing the somewhat dependence heavy libraries needed for going through the cookbook, I started to get frightened by my lack of knowledge in the field of big data manipulation in general. With math not being my forte, and no formal training in statistics, I surprisingly was not caught off-guard by the recipes. There are not advanced math requirements or statistical foundation required in order to understand the cookbook, and the author makes the recipes relatively easy to understand. When we encounter strange or unfamiliar terms, we get explanations or links towards wikipedia or web-standard pages that explain the terminology.
With a broad spectrum of real-life applications, and sharing crucial data manipulation techniques, like noise reduction or handling various data formats, the author makes sure you're prepared for most of the scenarios you might bump into, be you a researcher, software developer (CRM's, ERP's, products that usually require data visualization) or active in academia. But it doesn't stop here. By expanding the already thick information layer, the book takes us, with baby steps, through key concepts in 3D data visualization, OpenGL, working with the Google Maps API for plotting statistical information based on geographical location, but also addresses more discrete subjects as choosing the right colour for your presentation depending on your target demographic, or understanding what data visualization technique can best supply the audience with valuable info, regarding the nature of the information you want to emphasize.
As all cookbooks, it won't make you a field expert by reading it, but it will surely carve an image of what Python can do regarding data visualization, and that's the most important thing, based on my personal experience. Learning what a tool can do means that whenever you may need it, the information is only a click or a page away. I'd recommend it for both professionals that already work in the subject, and beginner-intermediate curiosity-driven enthusiasts as myself.
December 24, 2020
A lil dated, but useful.
January 22, 2014
http://goo.gl/d1msu7
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. However, Packt has an upcoming book dedicated entirely to it, which is probably a good thing since filtering and reshaping data is an important and broad enough topic to warrant a whole book and PANDAS is quickly becoming the de-facto tool for that job.
Finally, I've seen some criticism of Packt books but seen no evidence of poor quality in the dozen or so books I've bought from them so far. The quality of this particular book surpasses many books I've bought in the past and for a lot more money!
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. However, Packt has an upcoming book dedicated entirely to it, which is probably a good thing since filtering and reshaping data is an important and broad enough topic to warrant a whole book and PANDAS is quickly becoming the de-facto tool for that job.
Finally, I've seen some criticism of Packt books but seen no evidence of poor quality in the dozen or so books I've bought from them so far. The quality of this particular book surpasses many books I've bought in the past and for a lot more money!
January 25, 2014
http://bit.ly/1aT7qeS
I was quite sceptical before reading the book as I suspected it will not bring anything new to me but I was pleasantly mistaken. The book provides decent introduction and recipes for handling different data sets, contains information on how to prepare, structure and apply different data for different kinds of plots. It also gives hints on using the right plot to understand raw data. I also liked a chapter on plotting data on a map using Google Map API.
I tried to find not only pros and found three missing points (at least for me).
* most of the examples in the book NumPy & Matplotlib, I was quite surprised that I did not see PANDAS library being mentioned which becomes a standard for handling data in Python nowadays.
* there's one recipe on how to access a typical sql database from python and extract data using sql. I missed another example for recently popular nosql databases like MongoDB.
* I missed a chapter on d3py. D3PY is a Python frontend for D3.js - Data Driven documents for the web.
Still I would like to recommend reeading this book. It is beneficial for intermediate+ Python developers, students or scientists that already know Python basics and is a good collection of recipes. If one needs to dive in quickly to present data on a chart, then this book is for you. For absolute beginners a Python introduction is a must.
I was quite sceptical before reading the book as I suspected it will not bring anything new to me but I was pleasantly mistaken. The book provides decent introduction and recipes for handling different data sets, contains information on how to prepare, structure and apply different data for different kinds of plots. It also gives hints on using the right plot to understand raw data. I also liked a chapter on plotting data on a map using Google Map API.
I tried to find not only pros and found three missing points (at least for me).
* most of the examples in the book NumPy & Matplotlib, I was quite surprised that I did not see PANDAS library being mentioned which becomes a standard for handling data in Python nowadays.
* there's one recipe on how to access a typical sql database from python and extract data using sql. I missed another example for recently popular nosql databases like MongoDB.
* I missed a chapter on d3py. D3PY is a Python frontend for D3.js - Data Driven documents for the web.
Still I would like to recommend reeading this book. It is beneficial for intermediate+ Python developers, students or scientists that already know Python basics and is a good collection of recipes. If one needs to dive in quickly to present data on a chart, then this book is for you. For absolute beginners a Python introduction is a must.
February 25, 2014
Python Data Visualization Cookbook introduces the process of doing data visualization with the Python programming language. The book uses the Scipy stack for data visualization (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 visualization with a novice level understanding of both mathematics and programming.
September 13, 2016
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.
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.
November 29, 2014
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.
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.
January 30, 2014
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.
September 29, 2019
The book is a little bit old with python 2.7, But it is a book, and illustrate a lot of useful ideas, good job!
Displaying 1 - 9 of 9 reviews