Learn Visualizing Categorical Scatter Plots

In statistics, a categorical variable is a variable that can take on one of a limited number of possible values (e.g., "Day of Week", "Gender", "Country").

A stripplot is essentially a scatter plot where one of the axes represents a categorical variable. It visualizes the distribution of many individual one-dimensional values.

Why Use a Stripplot?

Unlike a histogram or a density plot which aggregates data, a stripplot shows every single observation as a dot. This is perfect for smaller datasets where you want to see the exact spread and identify outliers.

Key Parameters for Customization

Since dots can overlap (a problem called "overplotting"), stripplot offers several ways to make them distinct:

alpha: controls transparency (0 to 1). Setting this to a low value (e.g., 0.25) helps visualize density — darker areas mean more points;
size: changes the radius of the dots;
marker: changes the shape of the points (e.g., 'D' for diamonds, 's' for squares);
jitter: adds a small amount of random noise to the position of dots so they don't sit exactly on top of each other (enabled by default).

Live Example

Here is how to create a stripplot that uses transparency to handle overlapping data.


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import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
df = sns.load_dataset('tips')

# Create a customized stripplot
sns.stripplot(
    data=df,
    x='day',            # Categorical axis
    y='total_bill',     # Numerical axis
    alpha=0.5,          # Make points semi-transparent
    size=10,            # Make points larger
    jitter=True         # Spread points out slightly
)

plt.show()

Task

Swipe to start coding

Visualize the distribution of bills per day with a customized style.

Set the style to 'whitegrid'. Pass a dictionary to set the background color ('axes.facecolor') to 'aliceblue'.
Create a stripplot using the tips dataset (df):
- Map 'day' to the x axis and 'total_bill' to the y axis.
- Color the points based on the 'smoker' status using hue.
- Set the point size to 20.
- Use the 'crest' palette.
- Change the marker shape to diamonds using marker='D'.
- Set the transparency alpha to 0.25.
Display the plot.

Solution

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Section 1. Chapter 9

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