2D and 3D Scatter Histograms from arrays in Python

Here it follows two functions: hist2d_bubble and hist3d_bubble; that may fit for your purpose:

import numpy as npimport matplotlib.pyplot as pyplotfrom mpl_toolkits.mplot3d import Axes3Ddef hist2d_bubble(x_data, y_data, bins=10):    ax = np.histogram2d(x_data, y_data, bins=bins)    xs = ax[1]    ys = ax[2]    points = []    for (i, j), v in np.ndenumerate(ax[0]):        points.append((xs[i], ys[j], v))    points = np.array(points)    fig = pyplot.figure()    sub = pyplot.scatter(points[:, 0],points[:, 1],                         color='black', marker='o', s=128*points[:, 2])    sub.axes.set_xticks(xs)    sub.axes.set_yticks(ys)    pyplot.ion()    pyplot.grid()    pyplot.show()    return points, subdef hist3d_bubble(x_data, y_data, z_data, bins=10):    ax1 = np.histogram2d(x_data, y_data, bins=bins)    ax2 = np.histogram2d(x_data, z_data, bins=bins)    ax3 = np.histogram2d(z_data, y_data, bins=bins)    xs, ys, zs = ax1[1], ax1[2], ax3[1]    smart = np.zeros((bins, bins, bins),dtype=int)    for (i1, j1), v1 in np.ndenumerate(ax1[0]):        if v1 == 0:            continue        for k2, v2 in enumerate(ax2[0][i1]):            v3 = ax3[0][k2][j1]            if v1 == 0 or v2 == 0 or v3 == 0:                continue            num = min(v1, v2, v3)            smart[i1, j1, k2] += num            v1 -= num            v2 -= num            v3 -= num    points = []    for (i, j, k), v in np.ndenumerate(smart):        points.append((xs[i], ys[j], zs[k], v))    points = np.array(points)    fig = pyplot.figure()    sub = fig.add_subplot(111, projection='3d')    sub.scatter(points[:, 0], points[:, 1], points[:, 2],                color='black', marker='o', s=128*points[:, 3])    sub.axes.set_xticks(xs)    sub.axes.set_yticks(ys)    sub.axes.set_zticks(zs)    pyplot.ion()    pyplot.grid()    pyplot.show()    return points, sub

The two figures above were created using:

temperature = [4,   3,   1,   4,   6,   7,   8,   3,   1]radius      = [0,   2,   3,   4,   0,   1,   2,  10,   7]density     = [1,  10,   2,  24,   7,  10,  21, 102, 203]import matplotlibmatplotlib.rcParams.update({'font.size':14})points, sub = hist2d_bubble(radius, density, bins=4)sub.axes.set_xlabel('radius')sub.axes.set_ylabel('density')points, sub = hist3d_bubble(temperature, density, radius, bins=4)sub.axes.set_xlabel('temperature')sub.axes.set_ylabel('density')sub.axes.set_zlabel('radius')

Related:

Howto bin series of float values into histogram in Python?

How to correctly generate a 3d histogram using numpy or matplotlib built in functions in python?

2D histogram with Python

here's a bare-bones 2D version of Castro's code above. It simply plots the mean value at each x,y coordinate. This could be plotted using imshow but Castro's approach makes for a much neater scatter plot.

from matplotlib import pyplot as pltimport numpy as np# make some x,y points and z data that needs to be averaged and plottedx = [1,1,1,2,2,2,2,3,4,4,4,4]y = [1,1,1,2,2,2,2,3,4,4,4,4]z = [1,1,1,2,2,3,3,4,4,4,5,5]xbins, ybins = int(max(x)), int(max(y))rng = [[1, xbins+1], [1, ybins+1]]bins = [xbins,ybins]# get the sum of weights and sum of occurrences (their division gives the mean) H, xs, ys =np.histogram2d(x, y, weights=z, bins=bins, range=rng) count, _, _ =np.histogram2d(x, y, bins=bins, range=rng) # get the mean value of each x,y pointcount = np.ma.masked_where(count==0,count)H = np.ma.masked_where(H==0,H)H/=count# separate the H matrix into x,y,z arrays (and discard zero values)points = []for (i, j),v in np.ndenumerate(H):    if v: points.append((xs[i], ys[j], v))points = np.array(points)# plot the datafig = plt.figure()cm = plt.cm.get_cmap('hot')p = plt.scatter(points[:, 0], points[:, 1], c=points[:, 2], cmap=cm)plt.colorbar(p).set_label('avg. z value')plt.grid()plt.show()

All the duplicated x,y points are now reduced to a unique set and their z values have been averaged: