Thanks to mwaskon for suggesting the mayavi library. I recreated the density scatter plot in mayavi as follows: import numpy as np from scipy import stats from mayavi import mlab mu, sigma = 0, 0.1 x = 10*np.random.normal(mu, sigma, 5000) y = 10*np.random.normal(mu, sigma, 5000) z = 10*np.random.normal(mu, sigma, 5000) xyz = np.vstack([x,y,z]) kde = … Read more
Write code yourself. Then it fits your problem best! Boilerplate: Never assume code you download from the net to be correct or optimal… make sure to fully understand it before using it. %matplotlib inline from numpy import array, linspace from sklearn.neighbors import KernelDensity from matplotlib.pyplot import plot a = array([10,11,9,23,21,11,45,20,11,12]).reshape(-1, 1) kde = KernelDensity(kernel=”gaussian”, bandwidth=3).fit(a) … Read more
In addition to hist2d or hexbin as @askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses. If you want to do that: import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde # Generate fake data x = np.random.normal(size=1000) y = x * … Read more
Five years later, when I Google “how to create a kernel density plot using python”, this thread still shows up at the top! Today, a much easier way to do this is to use seaborn, a package that provides many convenient plotting functions and good style management. import numpy as np import seaborn as sns … Read more
In addition to hist2d or hexbin as @askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses. If you want to do that: import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde # Generate fake data x = np.random.normal(size=1000) y = x * … Read more