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.

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Python Data Visualization Cookbook (Second Edition)
Nov, 2015 | 302 pages
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Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Python Data Visualization Cookbook Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Preparing Your Working Environment
Preparing Your Working Environment
Introduction
Installing matplotlib, NumPy, and SciPy
Installing virtualenv and virtualenvwrapper
Installing matplotlib on Mac OS X
Installing matplotlib on Windows
Installing Python Imaging Library (PIL) for image processing
Installing a requests module
Customizing matplotlib's parameters in code
Customizing matplotlib's parameters per project
2
Knowing Your Data
Knowing Your Data
Introduction
Importing data from CSV
Importing data from Microsoft Excel files
Importing data from fixed-width data files
Importing data from tab-delimited files
Importing data from a JSON resource
Exporting data to JSON, CSV, and Excel
Importing and manipulating data with Pandas
Importing data from a database
Cleaning up data from outliers
Reading files in chunks
Reading streaming data sources
Importing image data into NumPy arrays
Generating controlled random datasets
Smoothing the noise in real-world data
3
Drawing Your First Plots and Customizing Them
Drawing Your First Plots and Customizing Them
Introduction
Defining plot types – bar, line, and stacked charts
Drawing simple sine and cosine plots
Defining axis lengths and limits
Defining plot line styles, properties, and format strings
Setting ticks, labels, and grids
Adding legends and annotations
Moving spines to the center
Making histograms
Making bar charts with error bars
Making pie charts count
Plotting with filled areas
Making stacked plots
Drawing scatter plots with colored markers
4
More Plots and Customizations
More Plots and Customizations
Introduction
Setting the transparency and size of axis labels
Adding a shadow to the chart line
Adding a data table to the figure
Using subplots
Customizing grids
Creating contour plots
Filling an under-plot area
Drawing polar plots
Visualizing the filesystem tree using a polar bar
Customizing matplotlib with style
5
Making 3D Visualizations
Making 3D Visualizations
Introduction
Creating 3D bars
Creating 3D histograms
Animating in matplotlib
Animating with OpenGL
6
Plotting Charts with Images and Maps
Plotting Charts with Images and Maps
Introduction
Processing images with PIL
Plotting with images
Displaying images with other plots in the figure
Plotting data on a map using Basemap
Plotting data on a map using the Google Map API
Generating CAPTCHA images
7
Using the Right Plots to Understand Data
Using the Right Plots to Understand Data
Introduction
Understanding logarithmic plots
Understanding spectrograms
Creating stem plot
Drawing streamlines of vector flow
Using colormaps
Using scatter plots and histograms
Plotting the cross correlation between two variables
Importance of autocorrelation
8
More on matplotlib Gems
More on matplotlib Gems
Introduction
Drawing barbs
Making a box-and-whisker plot
Making Gantt charts
Making error bars
Making use of text and font properties
Rendering text with LaTeX
Understanding the difference between pyplot and OO API
9
Visualizations on the Clouds with Plot.ly
Visualizations on the Clouds with Plot.ly
Introduction
Creating line charts
Creating bar charts
Plotting a 3D trefoil knot
Visualizing maps and bubbles
Index
Index

Creating line charts

In this recipe, we will see how to create a line chart. We have already introduced this kind of chart in Chapter 3, Drawing Your First Plots and Customizing Them, and we have seen how to make the plots with matplotlib. This time we'll focus on how to create and share them with Plot.ly.

Getting ready

Before starting, you need to set up your credentials for the Plot.ly platform in the programming environment:

$ python -c "import plotly; plotly.tools.set_credentials_file(username='DemoAccount', api_key='mykey')"

Replace Demo Account and mykey with your Plotly username and API key.

How to do it...

The following code example demonstrates how to plot two curves. In particular, we will:

  1. Generate the data to plot (a sine and a cosine wave).

  2. Organize the data in the format required by Plot.ly.

  3. Send a request to the server.

  4. Receive a URL that points to our chart.

  5. Run the following code:

    import plotly.plotly as py
from plotly.graph_objs import Scatter
import numpy as np

x = np.linspace...
Previous Section
End of Section 3
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