4. More Plots and Customizations
by Giuseppe Vettigli, Dimitry Foures, Igor Milovanović
Python Data Visualization Cookbook - Second Edition
- Python Data Visualization Cookbook Second Edition
- Table of Contents
- Python Data Visualization Cookbook Second Edition
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Preface
- 1. 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
- 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
- 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
- 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
- 6. Plotting Charts with Images and Maps
- 7. Using the Right Plots to Understand Data
- 8. More on matplotlib Gems
- 9. Visualizations on the Clouds with Plot.ly
- Index
In this chapter we will learn about:
- 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
In this chapter, we will explore more advanced properties of the matplotlib library. We are going to introduce more options and will look at how to achieve certain visually pleasing results.
During this chapter, we will seek the solutions to some non-trivial problems with representing data when simple charts are not enough. We will try to use more than one type of graph or create hybrid graphs to cover advanced data structures and the representation required.
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