Accessible Scatter and Regression Plots in seaborn with py-maidr

Make seaborn scatter plots and regression (regplot) plots accessible with py-maidr: points read in x order, fitted line as a separate layer.

A scatter plot is read point by point in x order: the Left and Right arrows move across the points, each stop announces its x and y (and its hue group when one is set), and the pitch follows y. A regression plot adds a fitted line, which py-maidr exposes as a second layer: Page Up and Page Down switch between the points and the smooth curve, so the trend can be heard on its own and then checked against the scatter.

SCATTER and SMOOTH are in the stable set: see Plot Type Stability.

Setup

Every example on this page needs only the import below. The plot cells repeat it so each one can be copied on its own.

import matplotlib.pyplot as plt
import seaborn as sns

# Just import maidr package: plt.show() now renders accessible output 
import maidr  

# The scatter plot below reuses this dataset.
iris = sns.load_dataset("iris")

Scatter Plot

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 

# Create a scatter plot
fig, ax = plt.subplots()
scatter_plot = sns.scatterplot( 
    data=iris, x="sepal_length", y="sepal_width", hue="species", ax=ax
)

# Adding title and labels
ax.set_title("Iris Sepal Length vs Sepal Width")
ax.set_xlabel("Sepal Length (cm)")
ax.set_ylabel("Sepal Width (cm)")

# Add number formatters for better screen reader output
ax.xaxis.set_major_formatter("{x:.1f}")
ax.yaxis.set_major_formatter("{x:.1f}")

# Show the plot
plt.show() 

Regression Plot

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

import maidr 

# Generate sample data
np.random.seed(42)
x = np.linspace(0, 10, 50)
y = 2 * x + 1 + np.random.normal(0, 2, 50)

# Create a regression plot
fig, ax = plt.subplots(figsize=(6, 6))
reg_plot = sns.regplot(
    x=x,
    y=y,
    scatter_kws={"s": 50, "alpha": 0.7},
    line_kws={"color": "red", "lw": 2},
    ax=ax,
) 

ax.set_title("Regression Plot with Fitted Line")
ax.set_xlabel("X values")
ax.set_ylabel("Y values")

# Add number formatters for better screen reader output
ax.xaxis.set_major_formatter("{x:.1f}")
ax.yaxis.set_major_formatter("{x:.2f}")

plt.show()