1: WHAT IS SEABORN?

Seaborn is a Python library for statistical data visualisation that provides high-level functions to create attractive and informative charts with minimal code [1].

It’s built on top of Matplotlib and integrates closely with pandas - making it easier to explore patterns and trends in tabular data.

2: USAGE OF SEABORN

Seaborn is commonly used to [2]:

• Visualise distributions (histograms, joint plots) to understand how numerical data is spread.
• Show relationships between variables with scatter plots, regression plots, etc.
• Work with categorical data using bar plots, box plots, and other statistical summaries.

a) Set a Theme (Style)

import seaborn as sns
sns.set_theme(style = “darkgrid”)

After importing the Seaborn library, this sets a consistent background style (in this case, a dark grid) for all your Seaborn plots in the script or notebook.

b) Plot a Numerical Distribution (Histogram)

import seaborn as sns

tips = sns.load_dataset(“tips”)
sns.histplot(data = tips, x = “total_bill”)

This draws a histogram of the total_bill column from the tips dataset so you can see how the bill amounts are distributed.

c) Scatter Plot with Colour by Category

import seaborn as sns

tips = sns.load_dataset(“tips”)
sns.scatterplot(data = tips, x = “total_bill”, y = “tip”, hue = “smoker”)

This creates a scatter plot of total_bill vs tip with the tips dataset, using different colours (hue = “smoker”) to distinguish between smoker and non-smoker customers.