About the Reviewer
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
Kostiantyn Kucher was born in Odessa, Ukraine. He received his master's degree in computer science from Odessa National Polytechnic University in 2012, and he has used Python as well as matplotlib and PIL for machine learning and image recognition purposes.
Since 2013, Kostiantyn has been a PhD student in computer science specializing in information visualization. He conducts his research under the supervision of Prof. Dr. Andreas Kerren with the ISOVIS group at the Computer Science department of Linnaeus University (Växjö, Sweden).
Kostiantyn was a technical reviewer for the first edition of this book.
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