Seaborn is a Python data visualization library based on matplotlib.
It provides a high-level interface for drawing attractive and informative statistical graphics.
boxplots and violinplots are used to shown the distribution of categorical data. A box plot (or box-and-whisker plot) shows the distribution of quantitative data in a way that facilitates comparisons between variables or across levels of a categorical variable. The box shows the quartiles of the dataset while the whiskers extend to show the rest of the distribution, except for points that are determined to be “outliers” using a method that is a function of the inter-quartile range.
The stripplot will draw a scatterplot where one variable is categorical. A strip plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.
The swarmplot is similar to stripplot(), but the points are adjusted (only along the categorical axis) so that they don’t overlap. This gives a better representation of the distribution of values, although it does not scale as well to large numbers of observations (both in terms of the ability to show all the points and in terms of the computation needed to arrange them).
factorplot is the most general form of a categorical plot. It can take in a kind parameter to adjust the plot type:
Matrix plots allow you to plot data as color-encoded matrices and can also be used to indicate clusters within the data.
In order for a heatmap to work properly, your data should already be in a matrix form, the sns.heatmap function basically just colors it in for you.
The clustermap uses hierarchal clustering to produce a clustered version of the heatmap. For example: