python 画正态分布图 python怎么画正态分布

场景：已知mean和variance，绘制正态分布曲线。

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math

#正态分布的概率密度函数。可以理解成 x 是 mu（均值）和 sigma（标准差）的函数
def normfun(x,mu,sigma):
    pdf = np.exp(-((x - mu)**2)/(2*sigma**2)) / (sigma * np.sqrt(2*np.pi))
    return pdf

mu = 32.86
sigma =1.93
# Python实现正态分布
# 绘制正态分布概率密度函数
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 50)
y_sig = np.exp(-(x - mu) ** 2 /(2* sigma **2))/(math.sqrt(2*math.pi)*sigma)
plt.plot(x, y_sig, "r-", linewidth=2)
plt.vlines(mu, 0, np.exp(-(mu - mu) ** 2 /(2* sigma **2))/(math.sqrt(2*math.pi)*sigma), colors = "c", linestyles = "dashed")
plt.vlines(mu+sigma, 0, np.exp(-(mu+sigma - mu) ** 2 /(2* sigma **2))/(math.sqrt(2*math.pi)*sigma), colors = "k", linestyles = "dotted")
plt.vlines(mu-sigma, 0, np.exp(-(mu-sigma - mu) ** 2 /(2* sigma **2))/(math.sqrt(2*math.pi)*sigma), colors = "k", linestyles = "dotted")
plt.xticks ([mu-sigma,mu,mu+sigma],['μ-σ','μ','μ+σ'])
plt.xlabel('Frequecy')
plt.ylabel('Latent Trait')
plt.title('Normal Distribution: $\mu = %.2f, $sigma=%.2f'%(mu,sigma))
plt.grid(True)
plt.show()