Python seaborn.load_dataset() method allows users to quickly load sample datasets provided by Seaborn for practicing and experimenting with data visualization techniques. In this article, we will understand about Python seaborn.load_dataset() method.

Python seaborn.load_dataset() Method Syntax

Below is the syntax of Python seaborn.load_dataset() Method.

Syntax:

seaborn.load_dataset(name, cache=True, data_home=None, **kws)

Parameter:

• name: This parameter specifies the name of the dataset to load. Seaborn provides several built-in datasets such as ‘iris’, ‘tips’, ‘titanic’, etc.
• cache: A boolean parameter (default is True) that determines whether to cache downloaded datasets locally for future use.
• data_home: The directory to save cached datasets. If not specified, the default is ~/.seaborn/data.
• kws: Additional keyword arguments that are passed to the underlying Pandas read_csv() function for loading the dataset.

Return Type: Pandas DataFrame containing the loaded dataset.

Python seaborn.load_dataset() Method Examples

Below are some of the examples by which we can understand about Seaborn load_dataset() Method in Python:

Visualizing Iris Dataset

In this example, we load the famous Iris dataset using seaborn.load_dataset() and then create a pairplot to visualize relationships between different features while differentiating species by color.

import seaborn as sns

# Load Iris dataset
iris_df = sns.load_dataset('iris')

# Visualize using pairplot
sns.pairplot(iris_df, hue='species')

Output:

<seaborn.axisgrid.PairGrid at 0x7d6463483790>

Analyzing Titanic Dataset

Here, we load the Titanic dataset and use seaborn.load_dataset() to fetch the data. Then, we create a barplot to analyze the survival rate based on passenger class.

import seaborn as sns

# Load Titanic dataset
titanic_df = sns.load_dataset('titanic')

# Visualize survival rate by class
sns.barplot(x='class', y='survived', data=titanic_df)

Output:

<Axes: xlabel='class', ylabel='survived'>

Exploring Tips Dataset

In this example, we load the Tips dataset and employ seaborn.load_dataset() to load it. Then, we create a violin plot to explore the distribution of tips across different days and times.

import seaborn as sns

# Load Tips dataset
tips_df = sns.load_dataset('tips')

# Visualize tip distribution by day and time
sns.violinplot(x='day', y='tip', hue='time', data=tips_df, split=True)

Output:

<Axes: xlabel='day', ylabel='tip'>