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Data Visualization and Analysis With Seaborn Library

Visualization with Count Plots

Explore how to visualize categorical data with count plots using Seaborn. Learn to create basic and grouped count plots, customize colors and labels, adjust plot orientation, and refine plot styling. Understand how to organize category orders and manipulate plot saturation to enhance clarity and visual appeal.

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Overview

Count plots are used to count the number of instances each category has. We use it for categorical data to represent the number of instances of each unique category in the form of bars.

Plotting count plots

Let’s start by importing the required libraries and the tips dataset from seaborn library using the sns.load_dataset() function. Next, we can view the first five data records to get an overview of the data using the pandas head() function.

Python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
tips_df = sns.load_dataset('tips')
print(tips_df.head())

Let’s check the ratio of male and female customers in the tips dataset by passing x='sex' to the sns.countplot() function. The sns.countplot() function returns a bar container with all “artists” describing the bars in a count plot. Next, we call the bar_label() function to add labels to the bar. The function takes a container as input to add labels to the bars of the count plot.

Python
cplot = sns.countplot( data = tips_df , x ='sex')   #returns bar container

plt.bar_label(cplot.containers[0])

plt.savefig('output/graph.png')    # save figure

In the example above, we saw that different ...