How To Make Counts Appear In Swarm Plot For Seaborn?

Swarm plots, a type of dot plot, effectively visualize data distribution within categories. Unlike standard dot plots, swarm plots avoid overlapping points, making it easier to see individual values. However, including the count of data points in each category can further enhance the plot's clarity. This article will guide you through the process of adding counts to a swarm plot using Seaborn.

Understanding the Swarm Plot

Before we dive into how to display counts, let's first understand what a swarm plot is and how it works. A swarm plot is a type of categorical scatterplot that is used to visualize the distribution of a categorical variable. It is similar to a strip plot, but with the points adjusted to be non-overlapping, making it easier to visualize the distribution of the data.

The Problem: Counts are Not Displayed by Default

By default, the swarm plot in seaborn does not display the counts of observations in each category. This can make it difficult to interpret the plot, especially when working with large datasets. For example, consider the following code:

import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")

# Create a swarm plot
sns.swarmplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

This code will create a swarm plot showing the distribution of the total_bill variable by day. However, the plot does not display the counts of observations in each day.

Steps for Adding Counts to Swarm Plot

Adding counts to a swarm plot involves a few key steps:

1. Create the Swarm Plot: Utilize Seaborn's swarmplot() function to generate the initial swarm plot.
2. Calculate Data Point Counts: Determine the number of data points within each category using methods like value_counts().
3. Annotate the Plot: Employ plt.text() to add count annotations at appropriate locations on the plot.

Import necessary libraries and load the dataset

import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")

Step 1: Create the Swarm Plot

Create the swarm plot using sns.swarmplot()

sns.set(style="whitegrid")
ax = sns.swarmplot(x="day", y="total_bill", data=tips)

Step 2: Calculate Data Point Counts

Calculate the counts of data points for each category.

# Calculate counts for each day
counts = tips['day'].value_counts().sort_index()

Step 3: Annotate the Plot with Counts

Annotate the plot with the calculated counts.

# Add counts as annotations
for i, count in enumerate(counts):
    ax.text(i, tips['total_bill'].max() + 2, f"Count: {count}",
            ha='center', size='medium', color='black', weight='semibold')

Display the Plot

plt.title('Swarm Plot of Total Bill by Day')
plt.xlabel('Day')
plt.ylabel('Total Bill')
plt.show()

Output:

Full Implementation Code- Make Counts Appear In Swarm Plot For Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

# Load the tips dataset
tips = sns.load_dataset("tips")

# Create swarm plot
sns.set(style="whitegrid")
ax = sns.swarmplot(x="day", y="total_bill", data=tips)

# Calculate counts for each day
counts = tips['day'].value_counts().sort_index()

# Add counts as annotations
for i, count in enumerate(counts):
    ax.text(i, tips['total_bill'].max() + 2, f"Count: {count}",
            ha='center', size='medium', color='black', weight='semibold')

# Set plot title and labels
plt.title('Swarm Plot of Total Bill by Day')
plt.xlabel('Day')
plt.ylabel('Total Bill')

# Show plot
plt.show()

Putting number marks on groups of dots helps make sense of your chart. By doing what we say here, you can add number marks to your dot groups easy, giving a better view of how your data spreads out.