Plotting 2D Kernel Density Estimation with Python

Question

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-axis and x-axis non-transparent. Also, how to show the values of the density on the contour? I would be very appreciated if someone could help me out. Below please see my code and graph.

import numpy as np import seaborn as sns import matplotlib.pyplot as pl  Y = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100) ax = sns.kdeplot(Y, shade = True, cmap = "PuBu") ax.patch.set_facecolor('white') ax.collections[0].set_alpha(0) ax.set_xlabel('$Y_1$', fontsize = 15) ax.set_ylabel('$Y_0$', fontsize = 15) pl.xlim(-3, 3) pl.ylim(-3, 3) pl.plot([-3, 3], [-3, 3], color = "black", linewidth = 1) pl.show() 

Answer 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 ?