Plotting 2D Kernel Density Estimation With Python

I would like to plot a 2D kernel density estimation. I find the seaborn package very useful here. However, after searching for a long time, I couldn't figure out how to make the y-

Solution 1:

Here is a solution using scipy and matplotlib only :

import numpy as np
import matplotlib.pyplot as pl
import scipy.stats as st

data = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
x = data[:, 0]
y = data[:, 1]
xmin, xmax = -3, 3
ymin, ymax = -3, 3

# Peform the kernel density estimate
xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([xx.ravel(), yy.ravel()])
values = np.vstack([x, y])
kernel = st.gaussian_kde(values)
f = np.reshape(kernel(positions).T, xx.shape)

fig = pl.figure()
ax = fig.gca()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
# Contourf plot
cfset = ax.contourf(xx, yy, f, cmap='Blues')
## Or kernel density estimate plot instead of the contourf plot
#ax.imshow(np.rot90(f), cmap='Blues', extent=[xmin, xmax, ymin, ymax])
# Contour plot
cset = ax.contour(xx, yy, f, colors='k')
# Label plot
ax.clabel(cset, inline=1, fontsize=10)
ax.set_xlabel('Y1')
ax.set_ylabel('Y0')

pl.show()

The previous code gives the following result :

which has a non-transparent x-axis, a non-transparent y-axis and values of the density on the contour. Is this the expected result ?

Solution 2:

Did you check these examples?

http://matplotlib.org/examples/pylab_examples/contour_demo.html

http://matplotlib.org/examples/pylab_examples/contourf_demo.html

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