Seaborn for Data Visualization in Python

Visualizing Data with Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

One of the main features of Seaborn is its ability to style plots to look attractive and professional. It also has several functions for visualizing statistical relationships between variables.

Here is an example of how to use Seaborn to create a scatter plot:

import seaborn as sns
 
# Load the tips dataset
tips = sns.load_dataset("tips")
 
# Create a scatter plot of total bill versus tip
sns.scatterplot(x="total_bill", y="tip", data=tips)
 

This will create a scatter plot with the total_bill on the x-axis and the tip on the y-axis, using the tips dataset.

Seaborn also has several functions for visualizing the distribution of a dataset. For example, you can use the displot function to create a histogram or kernel density estimate (KDE) plot:

import seaborn as sns
 
# Load the tips dataset
tips = sns.load_dataset("tips")
 
# Create a histogram of the tip amount
sns.displot(tips["tip"], kde=False)
 
# Create a KDE plot of the tip amount
sns.displot(tips["tip"], kde=True)

Seaborn also has functions for creating categorical plots, such as box plots, violin plots, and bar plots. For example:

import seaborn as sns
 
# Load the tips dataset
tips = sns.load_dataset("tips")
 
# Create a box plot of the tip amount by day
sns.boxplot(x="day", y="tip", data=tips)
 
# Create a violin plot of the tip amount by day
sns.violinplot(x="day", y="tip", data=tips)
 
# Create a bar plot of the average tip amount by day
sns.barplot(x="day", y="tip", data=tips)