Getting Started with Seaborn Library

Seaborn is a Python data visualization library built on top of the Matplotlib library. It provides a better user experience by being easy to use as well as offering various options to adjust the figure styling and aesthetics as needed. In addition, it’s the most common library used for exploratory data analysis due to it being so easy to use and its support for various data formats.

In the figure below, we’ve analyzed the tips dataset that’s available in the seaborn library. We can use the seaborn library to create many of these kinds of visualizations for in-depth data analysis.

We can use and access the seaborn library on any Python-supported platform, such as Google Colab and Jupyter Notebook. We’ll use the in-lesson Python code widgets to explore and understand the seaborn library.

The installation of the seaborn library can be done with the following command: pip install seaborn.

After the successful installation of the seaborn library, we can import it and use it for visualization purposes, as shown below:

The code snippet above shows us how to import the seaborn library. Here, as sns means that whenever we need to access the library, we’ll refer to it as sns, which is the default alias used for the seaborn library.

There are 17 built-in datasets in the seaborn library. This means we can easily use seaborn’s preloaded datasets to practice data visualizations without going to the trouble of finding datasets and downloading them ourselves.

Let’s take a look at the datasets first. We can view the names of all the datasets within the seaborn library using the sns.get_dataset_names() function, as shown below:

These datasets are pretty rich and are an excellent start for data visualization and analysis. Moreover, we can use these datasets for different plots in seaborn, such as scatter plots, pair plots, line plots, box plots, swarm plots, distribution plots, and so on.

The seaborn library is built on top of the Matplotlib library. Matplotlib is excellent for creating data visualizations. However, it can be a bit hard to comprehend. In contrast, the seaborn library provides a high-level interface to use Matplotlib and other added features. Seaborn is popular because of the styling and aesthetic functionalities it offers.

To import any dataset from the seaborn library, we use the sns.load_dataset() function by passing the name of the dataset in the function call. The sns.load_dataset() function returns a DataFrame object that we store in the variable, as shown in the code widget below:

As shown in the code widget above, we’ve passed the exercise value to the sns.load_dataset() function and stored the resultant DataFrame in the ex_df variable. We’ve viewed the first five records of the dataset using the pandas library head() method. The dataset has columns such as id, diet, pulse, time, and kind. We can further analyze and clean the data using the pandas library to make it ready for visualization. We can access any of the 17 datasets, such as tips, penguins, taxis, and so on, from the seaborn library by simply specifying its name in the sns.load_dataset() function.

Notice that we’ve imported the pandas library on the top. We use it to modify and view our data, as shown in the code widget above. The seaborn library is often used together with the pandas library for data cleaning to make it ready for data visualization. Therefore, it’s essential to import the required libraries at the beginning of our code before referencing them anywhere else in our code.

We can customize the theme for our plots in seaborn using the sns.set_theme() function, as shown below:

Seaborn’s default theme is activated when we call the sns.set_theme() function without any arguments. Seaborn has five built-in themes: whitegrid, darkgrid,dark, white, and ticks. Seaborn uses the darkgrid theme by default when we call the sns.set_theme() function without any arguments. However, we can customize the theme according to our requirements by passing the desired argument in the sns.set_theme() function.