Why is Matplotlib\'s Scatter Plot Marker Size Defined as \'s: size in points^2\'?

Pyplot Scatter Plot Marker Size: Understanding Points^2

In the pyplot documentation for scatter plots, the marker size is defined as "s: size in points^2." This notation can be confusing, so let's explore what it truly means.

Points^2: Defining the Marker Area

"Points^2" refers to the area of the marker in square points. A point is an arbitrary unit used in typography and graphics to measure font size and spacing. In the context of scatter plots, points^2 determines the size of the marker symbol itself, not its pixels or points on the screen.

Impact on Marker Size

To double the width (or height) of a marker, you need to increase s by a factor of 4 because the area is proportional to the square of the dimension. This means that doubling the linear dimension of a marker increases its apparent size more than linearly. As a result, defining the size as area rather than linear dimension ensures a more intuitive visual representation.

In Practice

When specifying the marker size, you can adjust it until it looks visually appropriate. Different marker sizes can be used to distinguish different data points or highlight specific trends.

Example

Consider the following code:

import matplotlib.pyplot as plt

x = [0, 2, 4, 6, 8, 10]
y = [0] * len(x)
s = [20 * 4**n for n in range(len(x))]

plt.scatter(x, y, s=s)
plt.show()

This code creates a scatter plot with markers of increasing size. Each successive marker is four times the previous one in area, demonstrating the exponential relationship between s and the marker size.

Conclusion

Understanding the concept of points^2 helps you accurately control the size of markers in scatter plots. By adjusting the area, you can create visually appealing and informative graphs.

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