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  • Book Overview & Buying Python Data Visualization Cookbook (Second Edition)
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Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

By : Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli
4 (6)
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Python Data Visualization Cookbook (Second Edition)

Python Data Visualization Cookbook (Second Edition)

4 (6)
By: Igor Milovanovic, Dimitry Foures, Giuseppe Vettigli

Overview of this book

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 will 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 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. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (11 chapters)
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10
Index

Using scatter plots and histograms


Scatter plots are very often encountered around, as they are the most common plot to visualize the relation between two variables. If we want to take a quick look at the data and see if there is any relation between those (that is, correlation), we would draw a quick scatter plot. For a scatter plot to exist, we must have one variable that can be systematically changed by, for example, experimenter, so we can inspect the possibilities of influencing another variable.

That's why, in this recipe, you will learn how to understand the scatter plots.

Getting ready

We want to see, for example, how two events are affected by each other or if they are affected at all. This visualization is especially useful on large sets of data, where we cannot make any conclusions by looking at the data in the native form—when it is just numbers.

Correlation between values, if there is any, can be positive and negative. Positive correlation is when, for increasing X values, the Y...

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