1D scatter plot colored by density

Question

I was wondering whether it was possible to use mpl-scatter-density or datashader to create a 1D scatter plot colored by density as it is shown here for the 2D case.

It can be done with gaussian_kde, but it is quite slow when the number of points that I want to represent are above 10k.

Also, is it there a way to do the mpl-scatter-density approach without defining axis as fig.add_subplot(1, 1, 1, projection='scatter_density') and by just creating them with plt.subplots?

I have tried to do so by using the ScatterDensityArtist from mpl_scatter_density without success.

Here is some sample code of the 1D scatter plot colored by density using gaussian_kde.

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

# Generate fake data
data_x = np.broadcast_to(np.array([[1], [2], [3]]), (2, 3, 1000))
data_y = data_x*np.random.normal(size=(2,3,1000))

# Create subplots
nrows = 1
ncols = 2
size = 5
fig, ax_array = plt.subplots(
    nrows,
    ncols,
    figsize=(16/9*ncols*size,nrows*size),
    squeeze=False
)
for i,ax_row in enumerate(ax_array):
    for j,axes in enumerate(ax_row):
        
        index = nrows*i+j
        
        x = data_x[index,:,:]
        y = data_y[index,:,:]
        
        for x_values,y_values in zip(x,y):
            z_values = gaussian_kde(y_values)(y_values)
            idx = z_values.argsort()

            x_values, y_values, z_values = x_values[idx], y_values[idx], z_values[idx]

            axes.scatter(
                x_values,y_values,
                c=z_values, s=10,
                cmap=plt.cm.get_cmap('Reds')
            )
plt.show()