Challenge 2: Exploring Categorical Data

Visualizing categorical data is crucial for gaining insights into how different categories relate to other variables. Categorical data, unlike continuous data, falls into discrete categories or labels. Seaborn, with its suite of powerful tools, provides efficient ways to visualize and interpret such data.

Visualizing categorical variables with Seaborn allows you to:

Compare the distribution of a numerical variable across different categories.
Visualize the relationships between two categorical variables.
Highlight how categorical variables relate to one or more numerical variables.

By leveraging Seaborn's functionalities, one can dive deep into the intricacies of categorical data, enabling a holistic understanding of its nuances.

Task

Swipe to start coding

Using Seaborn, dive into the world of categorical data visualization:

Create a box plot to display the distribution of a numerical variable across different categories.
Display the distribution of a numerical variable across different categories using a swarm plot.
Visualize the count of observations in each category using a count plot.

Solution

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

data = sns.load_dataset("tips")

days = data['day']

total_bill = data['total_bill']

time = data['time']

# 1. Creating a box plot

sns.boxplot(x=days, y=total_bill)

plt.title('Box plot of Total Bill across Days')

plt.show()

# 2. Displaying distribution using a swarm plot

sns.swarmplot(x=days, y=total_bill)

plt.title('Swarm plot of Total Bill across Days')

plt.show()

# 3. Count of observations using a bar plot

sns.countplot(x=days)

plt.title('Bar plot of Days')

plt.show()

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Section 5. Chapter 2

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

data = sns.load_dataset("tips")

days = data['day']

total_bill = data['total_bill']

time = data['time']

# 1. Creating a box plot

sns.___(___=days, y=total_bill)

plt.title('Box plot of Total Bill across Days')

plt.show()

# 2. Displaying distribution using a swarm plot

sns.___(x=days, ___=total_bill)

plt.title('Swarm plot of Total Bill across Days')

plt.show()

# 3. Count of observations using a bar plot

sns.___(___=days)

plt.title('Bar plot of Days')

plt.show()

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