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How can one create a heatmap from a 2D scatterplot data in Python, where for each (x,y) point in the scatterplot one has a z value associated to it? The z value will be the value used to color the heatmap.

For example, in R, I can use :

# This example is from http://knowledge-forlife.com/r-creating-heatmap-scatterplot-data/ #I'm just setting the seed so you can see the same example on your computer set.seed(1) #Our X data x <- runif(150) #Our Y data y <- runif(150) #Our Z data z <- c(rnorm(mean=1,100),rnorm(mean=20,50)) #Store the length of our data N <- length(x) # View the scatterplot plot(x, y) #Here is the interpolation to give the heatmap effect. #Use xo and yo to set the output grid you want to use. #xo and yo are used to change the resolution of the interpolation #Here, I have included a somewhat standard protocol for these parameters s <- interp(x,y,z,xo=seq(min(x),max(x),length=N), yo=seq(min(x),max(x),length=N),duplicate="mean") #Here's where the fun happens #Note you can add your typical plotting paramaters here, such as xlab or ylab image.plot(s,xlim=c(0,1),ylim=c(0,1),zlim=c(-2,25))

Scatterplot (each (x,y) point in this scatterplot one has a z value associated to it; the z values aren't visible in the scatterplot):

enter image description here

Corresponding heatmap (the color represents the z values):

enter image description here

Note that this question is different from Generate a heatmap in MatPlotLib using a scatter data set, where the color in the heatmap represents the density of the (x,y) points).

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2 Answers 2

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I went ahead with Gerges Dib's suggestion. Here is the code, sampling (x,y,z) points from 3D Gaussian distribution:

import numpy as np import scipy.interpolate from scipy.stats import multivariate_normal import matplotlib.pyplot as plt import seaborn as sns sns.set() # Sample from 3D Gaussian distribution np.random.seed(0) number_of_samples = 20 x = np.random.rand(number_of_samples) y = np.random.rand(number_of_samples) xy = np.column_stack([x.flat, y.flat]) # Create a (N, 2) array of (x, y) pairs. mu = np.array([0.0, 0.0]) sigma = np.array([.95, 2.5]) covariance = np.diag(sigma**2) z = multivariate_normal.pdf(xy, mean=mu, cov=covariance) plt.scatter(x, y) plt.savefig('scatterplot.png', dpi=300) plt.tricontourf(x, y, z) plt.savefig('tricontourf.png', dpi=300) # Interpolate and generate heatmap: grid_x, grid_y = np.mgrid[x.min():x.max():1000j, y.min():y.max():1000j] for method in ['nearest','linear','cubic'] : plt.figure() grid_z = scipy.interpolate.griddata(xy,z,(grid_x, grid_y), method=method) # [pcolormesh with missing values?](https://stackoverflow.com/a/31687006/395857) import numpy.ma as ma plt.pcolormesh(grid_x, grid_y, ma.masked_invalid(grid_z), cmap='RdBu', vmin=np.nanmin(grid_z), vmax=np.nanmax(grid_z)) plt.title('{0} interpolation'.format(method)) plt.colorbar() plt.savefig('heatmap_interpolation_{0}.png'.format(method), dpi=300) plt.clf() plt.close()

scatterplot.png:

enter image description here

tricontourf.png:

enter image description here

heatmap_interpolation_nearest.png

enter image description here

heatmap_interpolation_linear.png:

enter image description here

heatmap_interpolation_cubic.png:

enter image description here

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Here is a translation of your code into python using numpy for vector operations and matplotlib for plotting:

import numpy as np from matplotlib import pyplot x = np.random.uniform(size=150) y = np.random.uniform(size=150) z = np.concatenate([np.random.randn(100)+1, np.random.randn(50)+20]) pyplot.plot(x, y, 'ok') pyplot.tricontourf(x, y, z) pyplot.show()

One difference here is that I did not use interpolation to put x and y on a grid, but rather used matplotlib's tricontourf which uses triangular tessellation. If you need to put the data onto a rectangular grid, you can use scipy.interpolate.griddata which works very similar to the interp function you have in R. Then, for plotting a regular grid, you can use pyplot.pcolormesh.

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