Data Visualization#
Data visualization is the practice of representing data graphically to better understand trends, patterns, and insights. The University of South Carolina Libraries offer a variety of data visualization resources, including guides on visualization tools and techniques, and help with selecting the most effective methods for specific needs. Students, faculty, and staff can access consultations, attend workshops, and use the DataLab space on the 5th floor of Thomas Cooper Library. These resources support learning and application of tools such as Excel, Tableau, and R for effective data analysis and presentation.
Matplotlib#
This guide introduces several types of data visualizations—bar charts, line charts, scatter plots, and maps—and explains how to create them using Python libraries. Bar charts and line charts are ideal for comparing categories or showing trends over time, while scatter plots help reveal relationships between two variables. Maps, particularly choropleth and proportional dot maps, are used to visualize geographic or spatial data. The tutorial primarily uses Matplotlib for charting due to its simplicity. Numpy and Pandas are used to efficiently manage and manipulate data, while Geopandas is introduced for working with spatial data formats like Shapefiles and GeoJSON. Installation guidance is provided for users working outside of Anaconda.
Seaborn#
This section introduces Seaborn, a Python library for creating statistical visualizations. Built on matplotlib and integrated with pandas, Seaborn makes it easy to explore and visualize relationships in data with minimal code. It provides smart defaults, customizable features, and is well-suited for both quick exploration and presentation-ready plots.