Unlock deeper insights into visualization in form of 2D and 3D graphs using Matplotlib 2.x
About This Book
- Create and customize live graphs, by adding style, color, font to make appealing graphs.
- A complete guide with insightful use cases and examples to perform data visualizations with Matplotlib's extensive toolkits.
- Create timestamp data visualizations on 2D and 3D graphs in form of plots, histogram, bar charts, scatterplots and more.
Who This Book Is For
This book is for anyone interested in data visualization, to get insights from big data with Python and Matplotlib 2.x. With this book you will be able to extend your knowledge and learn how to use python code in order to visualize your data with Matplotlib. Basic knowledge of Python is expected.
What You Will Learn
- Familiarize with the latest features in Matplotlib 2.x
- Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots and many more.
- Make clear and appealing figures for scientific publications.
- Create interactive charts and animation.
- Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn.
- Design intuitive infographics for effective storytelling.
Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization.
Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples.
It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts.
By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Style and approach
Step by step comprehensive guide filled with real world examples.
Table of contents
Hello Plotting World!
- What is Matplotlib?
What's new in Matplotlib 2.0?
- Changes to the default style
- Improved functionality or performance
- Changes in settings
Setting up the plotting environment
- Setting up Python
- Installing the Matplotlib dependencies
- Setting up Jupyter notebook
- Using Jupyter notebook
- Viewing Matplotlib plots
- Saving the notebook project
- All set to go!
Plotting our first graph
- Loading data for plotting
- Importing the Matplotlib pyplot module
- Plotting a curve
- Viewing the figure
- Saving the figure
- Hello Matplotlib!
- Basic structure of a Matplotlib figure
- Setting colors in Matplotlib
- Adjusting text formats
- Customizing lines and markers
Customizing grids, ticks, and axes
- Adjusting tick spacing
- Customizing tick formats
- Setting label sizes
- Trying out the ticker locator and formatter
- Rotating tick labels
- Using style sheets
- Title and legend
- Test your skills
Figure Layout and Annotations
Adjusting the layout
- Adjusting the size of the figure
- Adjusting spines
- Adding subplots
- Adjusting margins
- Stacking subplots of different dimensions with subplot2grid
- Drawing inset plots
- Adjusting the layout
Visualizing Online Data
- Typical API data formats
- Introducing pandas
- Visualizing the trend of data
- Introducing Seaborn
- Visualizing univariate distribution
- Visualizing a bivariate distribution
- Visualizing categorical data
- Controlling Seaborn figure aesthetics
Visualizing Multivariate Data
- Getting End-of-Day (EOD) stock data from Quandl
- Two-dimensional faceted plots
- Other two-dimensional multivariate plots
- Three-dimensional (3D) plots
- Adding Interactivity and Animating Plots
A Practical Guide to Scientific Plotting
General rules of effective visualization
- Planning your figure
Crafting your graph
- The science of visual perception
- Getting organized
- Giving emphasis and avoiding clutter
- Styling plots for slideshows, posters, and journal articles
- Visualizing statistical data more intuitively
- General rules of effective visualization
- Exploratory Data Analytics and Infographics
- Title: Matplotlib 2.x By Example
- Release date: August 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788295260
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