EDUCBA Logo

EDUCBA

MENUMENU
  • Explore
    • EDUCBA Pro
    • PRO Bundles
    • Featured Skills
    • New & Trending
    • Fresh Entries
    • Finance
    • Data Science
    • Programming and Dev
    • Excel
    • Marketing
    • HR
    • PDP
    • VFX and Design
    • Project Management
    • Exam Prep
    • All Courses
  • Blog
  • Enterprise
  • Free Courses
  • Log in
  • Sign Up
Home Data Science Data Science Tutorials Seaborn Tutorial Seaborn Datasets
 

Seaborn Datasets

Updated March 17, 2023

Introduction to Seaborn Datasets

The Seaborn datasets function provides quick access to small datasets, which is very useful while documenting the seaborn and reproducing the bug reports. The seaborn dataset is not necessary if used in a typical scenario. Some seaborn datasets contain a small amount of preprocessing, defining proper ordering for categorical variables data. It is essential and valuable in python.

 

 

Seaborn Datasets

Watch our Demo Courses and Videos

Valuation, Hadoop, Excel, Mobile Apps, Web Development & many more.

Key Takeaways

  • The Seaborn dataset provides the data to plot a graph for testing purposes. The load dataset function is used to load the data from specified datasets.
  • After loading the datasets, we can view that data for the specified dataset using a head function in seaborn python.

What are Seaborn Datasets?

The seaborn dataset repository exists to provide the targets for the load dataset function, which was used to download the sample datasets. When browsing the seaborn data repo, we get the list of available data sets from python using the function name get_datasets_names.

Below example shows to retrieve the number of datasets from the seaborn library in python as follows:

Code:

import seaborn as sn
sn.get_dataset_names ()

Output:

retrieve the number of datasets from the seaborn library in python

The above example shows multiple datasets displayed; we can use the same when creating code with seaborn. Seaborn has proven it is a viral and valuable tool for visualization. We know that seaborn provides the API on top of matplotlib, offering multiple choices for plotting styles and loading the dataset that python provided.

The Seaborn provides the high-level functions for the plot types, which were statistical; the seaborn is integrated with the functionality provided by the panda’s data frame. We are loading the dataset in seaborn by using the load dataset function. We are loading the default dataset by using the load_dataset function in seaborn. We can also load our dataset or csv file by using the function. We can also make our dataset and load it when creating code into the seaborn.

How to Use Seaborn Datasets?

When using it, we need to follow the steps below.

The below steps show how we can use the seaborn datasets. While using the seaborn dataset, we need to install the matplotlib and seaborn library in our system.

1. In the first step, while using the seaborn dataset, we install the library of seaborn in our system as follows. The example below shows that we are installing the same by using the pip command.

Code:

pip install seaborn

Output:

install the library of seaborn using pip command

2. While installing the library package, we must import the seaborn and matplotlib library when loading the default datasets in our code. Without importing that library, we cannot load the dataset.

Code:

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

Output:

import the seaborn and matplotlib library

3. In the above step, we can see that we have imported the seaborn, matplotlib, and pandas library. Now in this step, we are loading the dataset name as tips by using the load_dataset function as follows. We can load any default dataset by using this function.

Code:

plot = sns.load_dataset ('tips')

Output:

dataset name as tips by using the load_dataset function

4. In the above step, we can see that we have loaded the tips dataset; while loading the dataset, we have defined plot variables. Now this variable we have used to see that dataset data is as follows

Code:

plot.head ()

Output:

plot variables

5. After loading the dataset, we can plot any graph by using data present in the loaded dataset. We can plot multiple graphs by using a dataset in python.

Code:

seaborn = sns.catplot (x = 'time', y = 'time', data = plot)
plt.show ()

Output:

plot multiple graphs by using a dataset in python

Seaborn Datasets and DataFrames

When working with it, we must import the panda’s library with matplotlib and seaborn. We are importing the required modules by using the import keyword. We are importing the pandas, seaborn, numpy, and matplotlib modules in the example below.

Code:

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

Output:

import the panda's library with matplotlib and seaborn

Seaborn comes with multiple datasets contained in the seaborn library. When the seaborn is installed, the datasets will automatically be downloaded. We do not need to download the same later. We can use any of the datasets as per the requirement. In the below example, we are loading the mpg datasets as follows.

Code:

plot = sns.load_dataset ('mpg')
plot.head ()

Output:

loading the mpg datasets

There are multiple datasets available in seaborn. Data frame stores the data in rectangular grids from which we can view the data easily. Every row from the rectangular grid contains the value of the instance, and every column from the grid holds the data from a specific variable. This means the data frame rows do not have the same type of value, which can be logical or numerical. Data frame into the seaborn comes with the panda’s library, defining the label structure, which is two-dimensional.

Examples of Seaborn Datasets

Different examples are mentioned below:

Example #1

In the below example, we are loading the exercise dataset and plotting the catplot as follows.

Code:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
plot = sns.load_dataset("exercise")
plot.head ()
seaborn = sns.catplot(x="time", y="pulse", data=plot)
plt.show()

Output:

loading the exercise dataset and plotting the catplot

Example #2

In the below example, we are loading the mpg dataset and plotting the catplot as follows.

Code:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
plot = sns.load_dataset("mpg")
plot.head ()
seaborn = sns.catplot(x="mpg", y="model_year", data=plot)
plt.show()

Output:

loading the mpg dataset and plotting the catplot

Example #3

In the below example, we are loading the tips dataset and plotting the catplot as follows.

Code:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
plot = sns.load_dataset("tips")
plot.head ()
seaborn = sns.catplot(x="tip", y="total_bill", data=plot)
plt.show()

Output:

loading the tips dataset and plotting the catplot

FAQ

Other FAQs are mentioned below:

Q1. What is the use of seaborn datasets in python?

Answer:

The datasets are used to load the data from datasets. We are loading the dataset’s data by using the function name load_dataset. There are multiple default datasets available.

Q2. Which libraries do we need to use when loading seaborn datasets?

Answer:

When using it, we must load the seaborn, numpy, pandas, and matplotlib libraries.

Q3. What is the use of DataFrames in the seaborn dataset?

Answer:

The DataFrames in seaborn is used to store the data in the rectangular grid from which we can view data easily.

Conclusion

The seaborn dataset repository exists to provide the targets for the load dataset function, which was used to download the sample datasets. The function offers quick access to small datasets, which is helpful while documenting the seaborn and reproducing the bug reports.

Recommended Articles

This is a guide to Seaborn Datasets. Here we discuss the introduction and how to use a seaborn dataset with examples and an FAQ. You may also have a look at the following articles to learn more –

  1. Seaborn barplot
  2. Seaborn heatmap
  3. Seaborn Scatter Plot
  4. Seaborn Histogram

Primary Sidebar

Footer

Follow us!
  • EDUCBA FacebookEDUCBA TwitterEDUCBA LinkedINEDUCBA Instagram
  • EDUCBA YoutubeEDUCBA CourseraEDUCBA Udemy
APPS
EDUCBA Android AppEDUCBA iOS App
Blog
  • Blog
  • Free Tutorials
  • About us
  • Contact us
  • Log in
Courses
  • Enterprise Solutions
  • Free Courses
  • Explore Programs
  • All Courses
  • All in One Bundles
  • Sign up
Email
  • [email protected]

ISO 10004:2018 & ISO 9001:2015 Certified

© 2025 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

By continuing above step, you agree to our Terms of Use and Privacy Policy.
*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

EDUCBA Login

Forgot Password?

🚀 Limited Time Offer! - 🎁 ENROLL NOW