seaborn

The seaborn logo

Learning outcomes

At the end of this sessions, learners …

• have practiced using the documentation of favorite HPC cluster

• understand what seaborn is

• understand why seaborn is important

• have created a plot with seaborn

• (optional) have created a plot with seaborn from a pandas table

For teachers

Prior:

• What is a plotting library?

• Why would there be multiple plotting libraries?

What is seaborn?

seaborn allows you to create figures:

import seaborn as sns
y = [0, 1, 4, 9, 16]
sns.lineplot(x = range(len(y)), y = y).figure.show()

Which shows:

Why is seaborn imported as sns?

From the seaborn FAQ:

This is an obscure reference to the namesake of the library, but you can also think of it as “seaborn name space”.

Why does this plot look identical to the matplotlib plot?

Because it is!

This plot is identical to the matplotlib plot in the session about matplotlib, because seaborn is built on top of matplotlib. Hence, seaborn uses matplotlib for plotting.

Why seaborn is important

seaborn is one of the most popular Python plotting libraries. It can be used to create publication-quality figures and the seaborn plot gallery shows that most plot types are present.

How popular is seaborn?

seaborn is not popular enough to be in the PyPI top 20.

However, at the seaborn PyPI statistics page we see that it has around 20 million downloads per month. As the number 20 package has around 800 million downloads per month, we can infer that it is not all too unpopular.

Exercises

Want to see the answers as a video?

HPC cluster	YouTube video
Alvis	YouTube video
COSMOS	YouTube video
Dardel	YouTube video
Kebnekaise	YouTube video
Pelle	YouTube video
Tetralith	YouTube video

Exercise 1: a minimal seaborn program

Use the documentation of the HPC cluster you work on.

Answer: where is your documentation?

Sorted by HPC cluster:

HPC center	HPC cluster	HPC cluster-specific documentation
C3SE	Alvis	Documentation
UPPMAX	Bianca	Documentation
LUNARC	COSMOS	Documentation
PDC	Dardel	Documentation
HPC2N	Kebnekaise	Documentation
UPPMAX	Pelle	Documentation
NSC	Tetralith	Documentation

In that documentation, find the software module to load the seaborn Python package.

Answer: where is the seaborn documentation?

HPC cluster	HPC cluster-specific seaborn documentation
Alvis	Has no documentation on how to load seaborn
Bianca	seaborn documentation
COSMOS	Partially relevant seaborn documentation: it only shows the module name
Dardel	Has no documentation on how to load seaborn
Kebnekaise	seaborn documentation
Pelle	seaborn documentation
Tetralith	Has no documentation on how to load seaborn

In a terminal (on your HPC cluster), load the software module to use seaborn.

Answer: how to load the seaborn software module

HPC cluster	How to load Seaborn
Alvis	module load Seaborn/0.13.2-gfbf-2024a
COSMOS	module load GCC/13.2.0 Seaborn/0.13.2
Dardel	module load python/3.12.3 ; pip3 install seaborn
Kebnekaise	module load GCC/12.3.0 Python/3.11.3 SciPy-bundle/2023.07 matplotlib/3.7.2 Tkinter/3.11.3 Seaborn/0.13.2
Pelle	module load Seaborn/0.13.2-gfbf-2024a
Tetralith	module load Python/3.10.4-env-hpc1-gcc-2022a-eb ; pip install seaborn

On your HPC cluster, create a script called seaborn_exercise_1.py with the following code:

import seaborn
print(seaborn.__version__)

Run the script.

Answer: how to run the script

HPC cluster	How to run the script
Alvis	python seaborn_exercise_1.py
COSMOS	python seaborn_exercise_1.py
Dardel	python3 seaborn_exercise_1.py
Kebnekaise	python seaborn_exercise_1.py
Pelle	python seaborn_exercise_1.py
Tetralith	python seaborn_exercise_1.py

What do you see?

Answer: how does that look like?

The output looks similar to this:

0.13.2

Even though the code shows nothing directly useful, why is this a useful exercise anyways?

Answer

This is a useful exercise, because it proves that you have successfully loaded/installed seaborn.

Exercise 2: a minimal plot

On your HPC cluster, create a script called seaborn_exercise_2.py, with the following content:

import seaborn as sns
y = [0, 1, 4, 9, 16]
sns.lineplot(x = range(len(y)), y = y).figure.savefig("seaborn_exercise_2.png")

Run the script.

Answer: how to run the script

HPC cluster	How to run the script
Alvis	python seaborn_exercise_2.py
COSMOS	python seaborn_exercise_2.py
Dardel	python3 seaborn_exercise_2.py
Kebnekaise	python seaborn_exercise_2.py
Pelle	python seaborn_exercise_2.py
Tetralith	python seaborn_exercise_2.py

Check that the figure is created.

Answer: how to check that the figure is created

There are many ways to do so:

• Download the file to your local computer: You can download the file to your local computer. Then use your favorite way to view this image.

• View in a remote desktop environment: Your favorite HPC cluster has a remote desktop environment, which is a visual/graphical environment that is intuitive to use. There, for example, use the file explorer to find the file, then double-click it to do display it

• View from a console environment that has X-forwarding enabled: Use the same procedure as on the ‘HPC Python’ course Day 1: view a plot.

(optional) Exercise 3: displaying a pandas table

In this exercise, we will again use the ‘diamonds’ dataset (as a comma-separated file), click to download: a dataset about diamonds.

This dataset contains information about more than fifty thousand diamonds. Two such features are the weight (in carats) and the price (in USD). Here we want to use an image to display the relationship between these two.

On your HPC cluster, create a script called seaborn_exercise_3.py. In that script:

• Use pandas to read the dataset

• Use seaborn to create a scatter plot from that data. Put the diamond weight on the x-axis and the diamond price on the y-axis. Use the seaborn documentation, a search engine or an AI chatbot for the answer.

• save the plot as seaborn_exercise_3.png Use the matplotlib documentation, a search engine or an AI chatbot for the answer.

Answer

Here is a simple solution (simplified from this script):

import pandas as pd
import seaborn as sns
table = pd.read_csv("diamonds.csv")

scatter_plot = sns.relplot(
    data = table, x = "carat", y = "price"
)
scatter_plot.savefig("seaborn_exercise.png") 

This will look like this:

(optional) Exercise 4: making the plot pretty

Use the seaborn documentation to improve the plot, for example:

• Add a title

• Add titles to the axes

• Add a linear trendline

• Whatever you like

External links

• Python Data Science Handbook