I don't know of any matplotlib implementations but it's not hard to do. Here I let hexbin
do the counting, and then go through each cell and add the appropriate number of petals:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
np.random.seed(0)
n = 2000
x = np.random.standard_normal(n)
y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
cmap = colors.ListedColormap(['white', 'yellow', 'orange'])
hb = plt.hexbin(x,y, bins='log', cmap=cmap, gridsize=20, edgecolor='gray')
plt.axis([-2, 2, -12, 12])
plt.title("sunflower plot")
counts = hb.get_array()
coords = hb.get_offsets()
for i, count in enumerate(counts):
x, y = coords[i,:]
count = int(10**count)
if count>3 and count<=12:
n = count // 1
if n>1:
plt.plot([x], [y], 'k.')
plt.plot([x], [y], marker=(n, 2), color='k', markersize=18)
if count>12:
n = count // 5
if n>1:
plt.plot([x], [y], 'k.')
plt.plot([x], [y], marker=(n, 2), color='k', markersize=18)
plt.show()
Here yellow is 1 petal = 1, and orange 1 petal = 5.
One obvious place for improvement here is working with the colormap. For example, do you want to preset the colors boundaries or calculate them from the data, etc? Here I just kludged it a bit: I used bins='log'
just to get a reasonable ratio between yellow and orange cells for the particular sample I used; and also I hard coded the borders between white, yellow, and orange cells (3 and 12).
Being able to use a tuple to specify the marker characteristics in matplotlib makes it really easy to draw all the different petal numbers.