核密度估计图（Kernel Density Estimation, KDE）

核密度估计图（Kernel Density Estimation, KDE）

KDE

KDE是一种用来估计概率密度函数的非参数方法

seaborn

from numpy.random import randn
import matplotlib as mpl
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_palette("hls")
mpl.rc("figure", figsize=(10, 6))
data = randn(250) # generate random data
plt.title("KDE Demonstration using Seaborn and Matplotlib", fontsize=20)
sns.distplot(data, color="#ff8000")

SciPy和NumPy

from scipy.stats.kde import gaussian_kde
from scipy.stats import norm
from numpy import linspace, hstack
from pylab import plot, show, hist

sample1 = norm.rvs(loc=-1.0, scale=1, size=320) #norm.rvs通过loc和scale参数可以指定随机变量的偏移和缩放参数，这里对应的是正态分布的期望和标准差。size得到随机数数组的形状参数
sample2 = norm.rvs(loc=2.0, scale=0.6, size=320)
sample = hstack([sample1, sample2])

probDensityFun = gaussian_kde(sample)
plt.title("KDE Demonstration using Scipy and Numpy", fontsize=20)
x = linspace(-5, 5, 200)
plot(x, probDensityFun(x), 'r')
hist(sample, normed=1, alpha=0.45, color='purple')
show()

[1] Kirthi Raman. Mastering Python Data Visualization