Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However if youre looking for statistical plots that are easy to build and visually appealing Seaborn is the obvious choice. Youll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation or KDE to visualize the probability distribution of your data. Youll then work with bivariate histograms and KDE curves. Next youll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. Youll work with boxen plots which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally youll use Violin plots to represent the entire probability density function obtained via a KDE estimation for your data.