Layering plots is a technique used to overlay multiple plots on top of each other to create a more complex visualization. In Python, you can layer plots using various libraries such as Matplotlib, Seaborn, and Plotly.

Here's an example of layering a line plot and a scatter plot using Matplotlib:

import matplotlib.pyplot as plt
import numpy as np
 
# Generate some random data
x = np.linspace(0, 10, 100)
y = np.sin(x)
 
# Create a line plot
plt.plot(x, y, color='blue')
 
# Generate some random points
x_points = np.random.choice(x, size=20)
y_points = np.sin(x_points)
 
# Create a scatter plot
plt.scatter(x_points, y_points, color='red')
 
# Set the title and axis labels
plt.title('Layered Plot Example')
plt.xlabel('x')
plt.ylabel('y')
 
# Show the plot
plt.show()

In this example, we first generate 100 data points from the sine function using NumPy and create a line plot using plt.plot(). We then generate 20 random points from the same x-axis range and create a scatter plot using plt.scatter(). We set the color of the line plot to blue and the color of the scatter plot to red. Finally, we set the title and axis labels using plt.title(), plt.xlabel(), and plt.ylabel(), and display the plot using plt.show().

You can layer as many plots as you want in the same figure. Just remember to use the appropriate plotting function and customize the appearance of each plot as needed.