Big data analytics drives innovation in scientific research, digital marketing, policy making, and much more. With the increasing amount of data from sensors, user activities, to APIs and databases, there is a need to visualize data effectively in order to communicate the insights to the target audience.
Matplotlib offers a simple but a powerful plotting library that helps to resolve the complexity in big data visualization, and turns overwhelming data into useful information. The library offers versatile plot types and robust customizations to transform data into persuasive and actionable figures. With the recent introduction of version 2, Matplotlib has further established its pivotal role in Python visualization.
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 with 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 and data.gov. By extending the power of Matplotlib using toolkits such as GeoPandas, Lifelines, Mplot3d, NumPy, Pandas, Plot.ly, Scikit-learn, SciPy, and Seaborn, you will learn how to visualize geographical data on maps, implement interactive charts, and craft professional scientific visualizations from complex datasets. By the end of this book, you will become well-versed with Matplotlib in your day-to-day work and be able to create advanced data visualizations.
In the first part of this book, you will learn the basics of creating a Matplotlib plot:
- Chapter 1, Hello Plotting World!, covers the basic constituents of a Matplotlib figure, as well as the latest features of Matplotlib version 2
- Chapter 2, Figure Aesthetics, explains how to in customize the style of components in a Matplotlib figure
- Chapter 3, Figure Layout and Annotations, explains how to add annotations and subplots, which allow more comprehensive representation of the data
Once we have a solid foundation of the basics of Matplotlib, in part two of this book, you will learn how to mix and match different techniques to create increasingly complex visualizations:
- Chapter 4, Visualizing Online Data, teaches you how to design intuitive infographics for effective storytelling through the use of real-world datasets.
- Chapter 5, Visualizing Multivariate Data, gives you an overview of the plot types that are suitable for visualizing datasets with multiple features or dimensions.
- Chapter 6, Adding Interactivity and Animating Plots, shows you that Matplotlib is not limited to creating static plots. You will learn how to create interactive charts and animations.
Finally, in part three of this book, you will learn some practical considerations and data analysis routines that are relevant to scientific plotting:
- Chapter 7, A Practical Guide to Scientific Plotting, explains that data visualization is an art that's closely coupled with statistics. As a data scientist, you will learn how to create visualizations that are not only understandable by yourself, but legible to your target audiences.
- Chapter 8, Exploratory Data Analytics and Infographics, guides you through more advanced topics in geographical infographics and exploratory data analytics.
These are the prerequisites for this book:
- Basic Python knowledge is expected. Interested readers can refer to Learning Python by Fabrizio Romano if they are relatively new to Python programming.
- A working installation of Python 3.4 or later is required. The default Python distribution can be obtained from https://www.python.org/download/. Readers may also explore other Python distributions, such as Anaconda (https://www.continuum.io/downloads), which provides better package dependency management.
- A Windows 7+, macOS 10.10+, or Linux-based computer with 4 GB RAM or above is recommended.
- The code examples are based on Matplotlib 2.x, Seaborn 0.8.0, Pandas 0.20.3, Numpy 1.13.1, SciPy 0.19.1, pycountry 17.5.14, stockstats 0.2.0, BeautifulSoup4 4.6.0, requests 2.18.4, plotly 2.0.14, scikit-learn 0.19.0, GeoPandas 0.2.1, PIL 1.1.6, and lifelines 0.11.1. Brief instructions for installing these packages are included in the chapters, but readers can refer to the official documentation pages for more details.
This book aims to help anyone interested in data visualization to get insights from big data with Python and Matplotlib 2.x. Well-visualized data aids analysis and communication regardless of the field. This book will guide Python novices to quickly pick up Matplotlib plotting skills through step-by-step tutorials. Data scientists will learn to prepare high-quality figures for publications. News editors and copywriters will learn how to create intuitive infographics to make their message crisply understandable.
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