python核密度图_用Python绘制2D内核密度估计

这是一个使用scipy和matplotlib的解决方案：

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()

以前的代码给出以下结果：

其具有不透明的x轴,不透明的y轴和轮廓上的密度值.这是预期的结果吗？