How to Make Grouped Boxplot with Seaborn Catplot?

Creating Grouped Boxplots with Seaborn's Catplot

Grouped boxplots are invaluable for comparing distributions across multiple categories and subcategories. Seaborn's catplot() function simplifies this process by combining categorical plots with facets, allowing for clear visualization of data variations.

Understanding Grouped Boxplots

A grouped boxplot displays the distribution of a numerical variable across different categories, with further subdivisions based on a secondary categorical variable. This structure helps in comparing distributions within and between groups, providing insights into data patterns and variations.

Using the Titanic Dataset

For illustration, we'll utilize Seaborn's built-in Titanic dataset, which contains information about passengers, including their class, fare, and survival status. This dataset is ideal for demonstrating grouped boxplots, as it offers multiple categorical variables for grouping.

Plotting a Grouped Boxplot

To create a grouped boxplot, we specify the following parameters in catplot():

• x: The primary categorical variable (e.g., 'class').
• y: The numerical variable to be plotted (e.g., 'fare').
• hue: The secondary categorical variable for grouping (e.g., 'sex').
• kind: The type of plot ('box' for boxplot).
• data: The dataset to be used.
• height and aspect: Control the size and aspect ratio of the plot.

Example:

import seaborn as sns
import matplotlib.pyplot as plt

# Load Titanic dataset
titanic = sns.load_dataset("titanic")

# Create grouped boxplot
sns.catplot(x="class", y="fare", hue="sex", kind="box", data=titanic, height=6, aspect=1.5)
plt.title("Fare Distribution by Class and Gender")
plt.show()

Customizing the Plot

Seaborn offers several options to enhance the appearance and functionality of the plot:

• Palette: Use palette to apply different color schemes (e.g., 'Set3', 'coolwarm').
• Orientation: Swap axes by changing x and y parameters for horizontal or vertical plots.
• Grid Lines: Add grid lines for better readability using plt.grid() from Matplotlib.
• Axis Labels: Customize axis labels and titles using Matplotlib's plt.xlabel(), plt.ylabel(), and plt.title().

Conclusion

Seaborn's catplot() function provides a powerful and flexible way to create grouped boxplots, facilitating the comparison of distributions across multiple categories and subcategories. By leveraging its various parameters and customization options, you can generate insightful visualizations that enhance data analysis and interpretation.