How to Color Scatter Markers Based on a Third Variable in Matplotlib?

Coloring Scatter Markers Based on a Third Variable

Scatterplots are an effective way to visualize the relationship between two or more variables. When you have a third variable that you want to represent, you can use it to color the markers in your scatterplot. Here's how to achieve grayscale coloring in Matplotlib:

To color your markers in greyscale, you can specify a grayscale colormap to the scatter function. A colormap defines the range of colors that will be used to shade the markers. Here's an example:

import numpy as np
import matplotlib.pyplot as plt

# Generate sample data
w = np.random.rand(10)
M = np.random.rand(10)
p = np.random.rand(10)

plt.scatter(w, M, c=p, s=500, cmap='gray')  # s is the marker size
plt.show()

In this example:

• We import numpy for data manipulation and matplotlib.pyplot for plotting.
• We generate sample data for w, M, and p.
• We use plt.scatter to plot the data points, specifying c=p to use the values in p to determine the color of each marker.
• We set s=500 to adjust the size of the markers.
• Crucially, we specify the cmap='gray' argument to use the grayscale colormap. This will shade the markers in shades of gray according to the values in p.

Alternatively, if you prefer a wider selection of grayscale colormaps, you can specify the cmap parameter directly. There are numerous pre-made grayscale colormaps available, such as gray, gist_yarg, and binary. To use the reversed version of any colormap, append "_r." For instance, gray_r instead of gray. Here's an example using the gray colormap:

plt.scatter(w, M, c=p, s=500, cmap='gray')

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