How to change marker size/scale in legend when marker is set to pixel

Question

I am scatter ploting data points with a very small marker (see screengrab below). When I use the very small marker ',' the legend is very hard to read (example code taken from here).
(Python 3, Jupyter lab)

How can I increase the size of the marker in the legend. The two versions shown on the above mentioned site do not work:

legend = ax.legend(frameon=True)  
for legend_handle in legend.legendHandles:  
    legend_handle._legmarker.set_markersize(9)

and

ax.legend(markerscale=6)

The two solutions do however work when the marker is set to '.'.
How can I show bigger makers in the legend?

Sample Code from intoli.com:

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

for i in range(5):
    mean = [np.random.random()*10, np.random.random()*10]
    covariance = [ [1 + np.random.random(), np.random.random() - 1],  [0, 1 + np.random.random()], ]
    covariance[1][0] = covariance[0][1]  # must be symmetric
    x, y = np.random.multivariate_normal(mean, covariance, 3000).T
    plt.plot(x, y, ',', label=f'Cluster {i + 1}')

ax.legend(markerscale=12)

fig.tight_layout()
plt.show()

Answer 1

You can get 1 pixel sized markers for a plot by setting the markersize to 1 pixel. This would look like

plt.plot(x, y, marker='s', markersize=72./fig.dpi, mec="None", ls="None")

What the above does is set the marker to a square, set the markersize to the ppi (points per inch) divided by dpi (dots per inch) == dots == pixels, and removes lines and edges.

Then the solution you tried using markerscale in the legend works nicely.

Complete example:

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

for i in range(5):
    mean = [np.random.random()*10, np.random.random()*10]
    covariance = [ [1 + np.random.random(), np.random.random() - 1],  [0, 1 + np.random.random()], ]
    covariance[1][0] = covariance[0][1]  # must be symmetric
    x, y = np.random.multivariate_normal(mean, covariance, 3000).T
    plt.plot(x, y, marker='s', markersize=72./fig.dpi, mec="None", ls="None", 
             label=f'Cluster {i + 1}')

ax.legend(markerscale=12)

fig.tight_layout()
plt.show()