How To Add Counts Of Points As A Label In A Sparse Scatter Plot

I have sparse scatter plot to visualize the comparison of predicted vs actual values. The range of the values are 1-4 and there are no decimal points. I have tried plotly so far wi

Solution 1:

This answer uses matplotlib.

To answer the initial question first: You need to find out how often the data produces a point at a given coordinate to be able to annotate the points. If all values are integers this can easily be done using a 2d histogram. Out of the hstogram one would then select only those bins where the count value is nonzero and annotate the respective values in a loop:

x = [3, 0, 1, 2, 2, 0, 1, 3, 3, 3, 4, 1, 4, 3, 0]
y = [1, 0, 4, 3, 2, 1, 4, 0, 3, 0, 4, 2, 3, 3, 1]

import matplotlib.pyplot as plt
import numpy as np

x = np.array(x)
y = np.array(y)

hist, xbins,ybins = np.histogram2d(y,x, bins=range(6))
X,Y = np.meshgrid(xbins[:-1], ybins[:-1])
X = X[hist != 0]; Y = Y[hist != 0]
Z   = hist[hist != 0]


fig, ax = plt.subplots()
ax.scatter(x,y, s=49, alpha=0.4)

for i in range(len(Z)):
    ax.annotate(str(int(Z[i])), xy=(X[i],Y[i]), xytext=(4,0), 
                textcoords="offset points" )

plt.show()

You may then decide not to plot all points but the result from the histogramming which offers the chance to change the color and size of the scatter points,

ax.scatter(X,Y, s=(Z*20)**1.4, c = Z/Z.max(), cmap="winter_r", alpha=0.4)

Since all values are integers, you may also opt for an image plot,

fig, ax = plt.subplots()
ax.imshow(hist, cmap="PuRd")

for i in range(len(Z)):
    ax.annotate(str(int(Z[i])), xy=(X[i],Y[i]), xytext=(0,0), color="w",
                ha="center", va="center", textcoords="offset points" )

Without the necesity to calculate the number of occurances, another option is to use a hexbin plot. This gives slightly inaccurate positions of the dots, du to the hexagonal binning, but I still wanted to mention this option.

import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np

x = np.array(x)
y = np.array(y)

fig, ax = plt.subplots()

cmap = plt.cm.PuRd
cmaplist = [cmap(i) for i in range(cmap.N)]
cmaplist[0] = (1.0,1.0,1.0,1.0)
cmap = matplotlib.colors.LinearSegmentedColormap.from_list('mcm',cmaplist, cmap.N)

ax.hexbin(x,y, gridsize=20, cmap=cmap, linewidth=0 )

plt.show()