💡 What is a 2D density chart?
There are several chart types allowing to visualize the distribution of a combination of 2 numeric variables. They always have a variable represented on the X axis, the other on the Y axis, like for a scatterplot (left).
Then the number of observations within a particular area of the 2D space is counted and represented with a color gradient. The shape can vary: hexagones result in a
hexbin chart, squares in a
2d histogram. A kernel density estimate can be used to get a
2d density plots or a
Confusing? Visit data-to-viz to clarify..
Contour plot with
The contour plot can be easily built thanks to the
kdeplot() function of the Seaborn library.
2D histogram with
Build a 2d histogram thanks to the
hist2d() function of the
Seaborn library. Do not forget to play with the
bins argument to find the value representing the best your data.
Hexbin chart with
Split the graph area in hexagones and you get a hexbin density chart. This time, it is
matplotlib that gets you covered thanks to its
2d density chart with
2D densities are computed thanks to the
gaussian_kde() function and plotted thanks with the
pcolormesh() function of
2d density and marginal plots
2D densities often combined with marginal distributions. It helps to highlight the distribution of both variables individually. It is pretty straightforward to add thanks to the
jointplot() function of the