Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

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 (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Drawing scatter plots with colored markers


If you have two variables and want to spot the correlation between those, a scatter plot may be the solution to spot patterns.

This type of plot is also very usable as a start for more advanced visualization of multidimensional data (for example, to plot a scatter plot matrix).

Getting ready

Scatter plots display values for two sets of data. The data visualization is done as a collection of points not connected by lines. Each of them has its coordinates determined by the value of the variables. One variable is controlled (independent variable), while the other variable is measured (dependent variable) and is often plotted on the y axis.

How to do it...

Here's a code sample that plots two plots: one with uncorrelated data and the other with strong positive correlation:

import matplotlib.pyplot as plt
import numpy as np

# generate x values
x = np.random.randn(1000)

# random measurements, no correlation
y1 = np.random.randn(len(x))

# strong correlation...