Horizontal Boxplots with Points using Seaborn in Python

Boxplots are one of the most popular tools for data visualization, mainly created using Seaborn, which provides a simple and powerful way to create both horizontal and vertical boxplots and other types of visualizations.

In this article, we will focus on how to create a horizontal boxplot with points using Seaborn in Python.

What is a Boxplot?

A boxplot is a graphical representation of a dataset that shows the distribution of data using quartiles, median, and outliers. The box in the middle represents the interquartile range (IQR), with whiskers extending to the minimum and maximum values within a certain distance from the median. Outliers are displayed as individual points outside the whiskers.

Creating Horizontal Boxplots with Points

We'll use the "tips" dataset from Seaborn, which contains information about restaurant bills and the day of the week ?

import seaborn as sns
import matplotlib.pyplot as plt

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

   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

Basic Horizontal Boxplot with Points

To create a horizontal boxplot with individual data points, we combine sns.boxplot() and sns.swarmplot() ?

import seaborn as sns
import matplotlib.pyplot as plt

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

# Create horizontal boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(x="total_bill", y="day", data=tips, orient="h")
sns.swarmplot(x="total_bill", y="day", data=tips, color="black", size=3, orient="h")

# Add labels and title
plt.title("Total Bill by Day")
plt.xlabel("Total Bill ($)")
plt.ylabel("Day of Week")

plt.show()

Customized Horizontal Boxplot

We can enhance the plot by customizing colors, styles, and parameters ?

import seaborn as sns
import matplotlib.pyplot as plt

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

# Set style and color palette
sns.set_style("whitegrid")
sns.set_palette("husl")

# Create customized horizontal boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(x="total_bill", y="day", data=tips, whis=[0, 100], width=0.6, 
            fliersize=5, orient="h")
sns.swarmplot(x="total_bill", y="day", data=tips, color="black", size=4, orient="h")

# Customize appearance
plt.title("Total Bill Distribution by Day", fontsize=14, fontweight="bold")
plt.xlabel("Total Bill ($)", fontsize=12)
plt.ylabel("Day of Week", fontsize=12)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)

plt.tight_layout()
plt.show()

Key Parameters

Alternative Approach Using stripplot()

Instead of swarmplot(), you can use stripplot() for a different point distribution ?

import seaborn as sns
import matplotlib.pyplot as plt

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

# Create horizontal boxplot with stripplot
plt.figure(figsize=(10, 6))
sns.boxplot(x="total_bill", y="day", data=tips, orient="h")
sns.stripplot(x="total_bill", y="day", data=tips, color="red", size=3, 
              orient="h", jitter=True)

plt.title("Total Bill by Day (with stripplot)")
plt.xlabel("Total Bill ($)")
plt.ylabel("Day of Week")

plt.tight_layout()
plt.show()

Conclusion

Horizontal boxplots with points using Seaborn effectively visualize data distributions across categories. Combine boxplot() with swarmplot() or stripplot() to show both summary statistics and individual data points for comprehensive data exploration.