15 Data Visualisation with matplotlib and seaborn

Learning Objectives

Be able to visualise biological data using Python
Create a plots using ‘matplotlib’ and ‘seaborn’
Customize the plots with titles, labels, and legends

15.1 Using matplotlib and seaborn

matplotlib is a widely used plotting library in Python, providing a wide range of capabilities

seaborn is built on top of matplotlib and offers a high-level interface. It simplifies the process of creating plots and enhances their aesthetics.

Import Required Libraries

!pip install seaborn

import matplotlib.pyplot as plt 
import seaborn as sns 
import numpy as np
import pandas as pd

Example Dataset

For the example:

np.random.seed(42)  
data = {  
       'height': np.random.normal(160, 10, 100),  # Normal distribution (mean=160, std=10)  
       'weight': np.random.normal(65, 15, 100)    # Normal distribution (mean=65, std=15)  
       } 

df = pd.DataFrame(data)

Scatter Plot
Create a scatter plot to visualize the relationship between height and weight.

plt.figure(figsize=(8, 5))  
plt.scatter(df['height'], df['weight'], color='blue', alpha=0.6) 
plt.title('Scatter Plot of Height vs. Weight')  
plt.xlabel('Height (cm)')  
plt.ylabel('Weight (kg)')  
plt.grid()  
plt.show()

Histogram
Create a histogram to show the distribution of weights.

plt.figure(figsize=(8, 5)) 
plt.hist(df['weight'], bins=15, color='green', edgecolor='black', alpha=0.7)  
plt.title('Histogram of Weights')  
plt.xlabel('Weight (kg)')  
plt.ylabel('Frequency')  
plt.grid()  
plt.show()

Boxplot with Seaborn
Use Seaborn to create a boxplot to visualize the distribution of heights.

plt.figure(figsize=(8, 5))  
sns.boxplot(y=df['height'], color='orange')  
plt.title('Boxplot of Heights')  
plt.ylabel('Height (cm)')  
plt.grid() 
plt.show()

Exercise 1 - Visualising humanmine data

Level:

Use ‘matplotlib’ and ‘seaborn’ to plot the humanmine data. Decide what analysis and plots you would like to use.

Additional exercise is to plot the memory usage of different datatypes as they increase in size exponentially. Similarly plot the speed of retrieving variables against the size of composite data types. What do you notice?

15.2 Documentation

See the documentation for ‘seaborn’ here:

https://seaborn.pydata.org/

Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, https://doi.org/10.21105/joss.03021.

J. D. Hunter, “Matplotlib: A 2D Graphics Environment”, Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.

See the documentation for ‘matplotlib’ here:

https://matplotlib.org/stable/index.html

15.3 Summary

Now you have all the tools you need!

Tomorrow we will use all you have learnt so far

Key Points

Plotting and visualisation is key to understanding and working with data
‘matplotlib’ and ‘seaborn’ can be used to plot your data in Python