Let’s consider that you want to study the relationship between 2 numerical variables with a lot of points. Then you can consider the number of points on each part of the plotting area and thus calculate a 2D kernel density estimate. It is like a smoothed histogram. Instead of a point falling into a particular bin, it adds a weight to surrounding bins. This plot is inspired from this stack overflow question. See this page to custom the color palette.

# libraries
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import kde
 
# create data
x = np.random.normal(size=500)
y = x * 3 + np.random.normal(size=500)
 
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
nbins=300
k = kde.gaussian_kde([x,y])
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
 
# Make the plot
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto')
plt.show()
 
# Change color palette
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.Greens_r)
plt.show()

You can add a color bar easily using colorbar() function.

# Add color bar
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.Greens_r)
plt.colorbar()
plt.show()

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