4.5.5 Joint plot

Seaborn’s jointplot displays a relationship between 2 variables (bivariate) as well as 1D profiles (univariate) in the margins. The multivariate normal distribution is a nice tool to demonstrate this type of plot as it is sampling from a multidimensional Gaussian and there is natural clustering.

Draw a plot of two variables with bivariate and univariate graphs.

1. Scatter Histogram Plot

sns.jointplot(x='S1', y='S2', data=df,  color = 'dodgerblue')

2. Density Histogram Plot

It is also possible to use the kernel density estimation procedure described above to visualize a bivariate distribution.

sns.jointplot('S1', 'S2', data = df, kind='kde', color = 'dodgerblue')

3. Hex Histogram Plot

A bivariate analog of a histogram is known as a “Hexbin” plot because it shows the counts of observations that fall within hexagonal bins. This plot works best with relatively large datasets.

sns.jointplot('S1', 'S2', data = df, kind='hex', color = 'dodgerblue')