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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.

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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:

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:

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:

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:

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:

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 ()

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:

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:

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:

Example #2

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:

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:

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.