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Python Data Visualization Cookbook
Python Data Visualization Cookbook

Python Data Visualization Cookbook: As a developer with knowledge of Python you are already in a great position to start using data visualization. This superb cookbook shows you how in plain language and practical recipes, culminating with 3D animations.

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Python Data Visualization Cookbook

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Key benefits

  • 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

Description

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.

Who is this book 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.

What you will learn

  • 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 25, 2013
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781782163374
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Product Details

Publication date : Nov 25, 2013
Length: 280 pages
Edition : 1st
Language : English
ISBN-13 : 9781782163374
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Languages :
Tools :

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Building Machine Learning Systems with Python
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Python Data Visualization Cookbook
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Table of Contents

8 Chapters
Preparing Your Working Environment Chevron down icon Chevron up icon
Knowing Your Data Chevron down icon Chevron up icon
Drawing Your First Plots and Customizing Them Chevron down icon Chevron up icon
More Plots and Customizations Chevron down icon Chevron up icon
Making 3D Visualizations Chevron down icon Chevron up icon
Plotting Charts with Images and Maps Chevron down icon Chevron up icon
Using Right Plots to Understand Data Chevron down icon Chevron up icon
More on matplotlib Gems Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(9 Ratings)
5 star 66.7%
4 star 11.1%
3 star 0%
2 star 0%
1 star 22.2%
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Amazon Customer Jan 29, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I use this book to better understand the mathplotlib which in my case helped me to replace math labIt was well documented and very nice written .A must have for python mid and advance programmers
Amazon Verified review Amazon
carl Mar 19, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
David Jensen Jan 29, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Jack Golding Feb 25, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Bernie Ongewe Nov 29, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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