Seaborn

Learning outcomes

At the end of this sessions, learners …

  • understand why Seaborn is important

  • have run Python code that uses Seaborn

Loading Seaborn

HPC cluster

How to load Seaborn

Alvis

module load Seaborn/0.13.2-gfbf-2024a

Kebnekaise

module load GCC/13.2.0 Seaborn/0.13.2

COSMOS

module load GCC/13.2.0 Seaborn/0.13.2

Pelle

module load Seaborn/0.13.2-gfbf-2024a

Tetralith

module load buildtool-easybuild/4.8.0-hpce082752a2 GCC/13.2.0 Python/3.11.5 SciPy-bundle/2023.11; pip install seaborn

Dardel

module load cray-python/3.11.7 PDCOLD/23.12 matplotlib/3.8.2-cpeGNU-23.12

Exercises

import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np
import pandas as pd

# Create some data
rng = np.random.RandomState(0)
x = np.linspace(0, 10, 500)
y = np.cumsum(rng.randn(500, 6), 0)

# Plot the data with Matplotlib defaults
plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left');


import seaborn as sns
sns.set()


# same plotting code as above!
plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left');

data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)
data = pd.DataFrame(data, columns=['x', 'y'])

# Two overlaid density plots
for col in 'xy':
    sns.kdeplot(data[col], shade=True)

# Density plot
sns.kdeplot(data);