14  - Visualization with Seaborn
Channel: Aaron J Newman
Date: 09/10/2021

Seaborn is a great plotting package for Data Science in Python. It allows you to easily generate common kinds of plots that help you visualize the distribution of data, and look for possible differences between conditions, or other patterns.

In this lesson, we learn to plot relational, distributional, and categorical plots with Seaborn, using simulated reaction time data from a lexical decision task.

Summary
- Seaborn makes it easy to generate plots useful for EDA
- relplot() is used for visualizing relationships between two continuous variables
- displot() is used for visualizing distributions of data, as histograms or kernel density estimates (KDEs)
- catplot() is used for making comparisons between different levels of categorical data, such as different experimental conditions or groups
- Different kinds of catplot() plots vary in how much they communicate information about the distributions of data in each category, versus on differences between the means of the categories
- In EDA, it's often useful to visualize the data many ways to really understand it. Even still, it's important to have an understanding of the strengths and limitations of different kinds of plots
- Seaborn makes it relatively easy to generate plots and adjust properties such as colour and font size, and as well as making plots accessible to the widest range of viewers

From the course, NESC 3505 Neural Data Science, by Aaron J Newman, Dalhousie University, Halifax, NS, Canada. 

All course videos are here:  http://neuraldatascience.io/videos

Textbook is available here: http://neuraldatascience.io