One approach is to plot the data as a scatter plot with a low alpha, so you can see the individual points as well as a rough measure of density. (The downside to this is that the approach has a limited range of overlap it can show — i.e., a maximum density of about 1/alpha.)
Here’s an example:
As you can imagine, because of the limited range of overlaps that can be expressed, there’s a tradeoff between visibility of the individual points and the expression of amount of overlap (and the size of the marker, plot, etc).
import numpy as np
import matplotlib.pyplot as plt
N = 10000
mean = [0, 0]
cov = [[2, 2], [0, 2]]
x,y = np.random.multivariate_normal(mean, cov, N).T
plt.scatter(x, y, s=70, alpha=0.03)
plt.ylim((-5, 5))
plt.xlim((-5, 5))
plt.show()
(I’m assuming here you meant 30e3 points, not 30e6. For 30e6, I think some type of averaged density plot would be necessary.)