Seaborn Tutorial for Data Visualization

Seaborn is one of the best Python libraries for Data Visualization. It is built on top of Matplotlib. Unlike Matplotlib, Seaborn cannot be the only Data Visualization library in your skillset, but some visualizations look better and more understandable with Seaborn. So if you want to learn how to use Seaborn for Data Visualization, this article is for you. In this article, I will take you through a tutorial on the Seaborn library for Data Visualization.

Seaborn Tutorial for Data Visualization

I will start this task by importing the necessary Python libraries and the dataset. Here I will be using the popular tips data. So let’s import the libraries and the dataset to get started:

import seaborn as sns
import pandas as pd
data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/tips.csv")
print(data.head())
   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

The pair plot method in seaborn helps analyze all the numerical features with respect to categorical features. Let’s analyze all the numerical features based on the time of the meal using a pair plot:

sns.set_theme(style="ticks")
sns.pairplot(data, hue="time")
pairplot Seaborn

Seaborn is also very useful for visualizing box plots. Let’s create a box plot to analyze the total bill for every day of the week:

sns.catplot(x="day", y="total_bill", kind="box", data=data)
box plot: tips data

Let’s analyze the gender of the bill payer by using a violin plot:

sns.catplot(x="day", y="total_bill", hue="sex",
            kind="violin", split=True, data=data)
Violin plot: tips data

We can also analyze relationships between two features by using a regression plot. Here’s how we can look at the relationships between the tips and the total bill:

# linear regression with marginal distribution
g = sns.jointplot(x="total_bill", y="tip", data=data,
                  kind="reg", truncate=False,
                  xlim=(0, 60), ylim=(0, 12),
                  color="b", height=7)
Seaborn Tutorial: regression plot

So these were some of the most important visualizations that you can visualize using the Seaborn library in Python. You can learn about more data visualization graphs from here.

Summary

So this is how you can use the Seaborn library for data visualization using Python. Unlike Matplotlib, Seaborn cannot be the only Data Visualization library in your skillset, but some visualizations look better and more understandable with Seaborn. I hope you liked this article on a tutorial on the Seaborn library for data visualization. Feel free to ask valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder

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