Seaborn Cheatsheet

June 5, 2021

Plotting data was always for me a weak point: it always took me a lot of time to make the plots and graphs, reading the documentation, googling how to do one particular tweak and things like that.

So I challenge myself: could I build a cheatsheet about plotting?

Yes, yes I could.

Seaborn is a very simple but powerful plotting library on top of matplotlib designed for statistical analysis.

Quick links: PDF (no bg), PDF (bg), SVG (no bg), SVG (bg) .

Instructions

Each rectangle list the parameters that take the plot functions. The bold labels inside, like hist and violin, are the names of the plots.

The names are followed by a list of data parameter, also in bold. These are used to tell the function from where take the data. Some functions may take more parameters additionally, so they are in between square brakets.

The rest of the parameters listed are optional. Sometimes the cheatsheet shows the type of the parameter, sometimes the range and in others just a few possible values.

From all of that you can write Python code like the following that plots a histogram for the univariate "age" in the given Pandas data frame. The extra kde=True plots a kernel density estimation on top.

>>> import seaborn as sns
>>> sns.histplot(data=df, x="age", kde=True)

Nested rectangles means that the outer rectangle includes the parameters listed in the inner rectangle but not the other way around.

The dashed rectangles means the union of: the parameters are shared across.

For example bar and point accepts the parameters of box and line.

Some parameters are followed by a comment in italics for a quick explanation of the parameters.

The cheatsheet has a lot of small plots as visual examples. Some of them are linked with a line with a plot function (rectangle) and some of them are linked with a dashed line to a particular setting.

For example in the cheatsheet there are 4 examples of hist showing the different results based on element='step', multiple='stack' and multiple='dodge'.

The fourth example is linked to the rectangle for the hist function (bivariate) and not linked to a particular setting.

The colors also have a meaning: plot functions of the same color belongs to the same Seaborn module.

Seaborn has –broadly speaking– 2 groups of functions:

• the ones that work at the axes level.
• the ones that creates their own figure and axes, the figure level (or module level)

The former are the core of Seaborn. Call them on the same axes to do a combination (an overlay) of different plots.

The latter are however simpler. They just call the axes level function based on the kind= parameter.

The advantage of the figure level functions is that they can create a matrix of plots with the row= and col= parameters.

The colors represent these: red for uni/bivariate distribution plots (displot), blue for relationship plots (relplot) and green for distribution within categories plots (catplot).

The violet group is for regressions and does not follow exactly the pattern above.

Related tags: seaborn, matplotlib, pandas, plotting, cheatsheet