# -*- coding: utf-8 -*-import numpy as npimport matplotlib.mlab as mlabimport matplotlib.pyplot as pltfrom scipy import optimizeplt.rcParams['font.sans-serif']=['SimHei']  #用来正常显示中文标签plt.rcParams['axes.unicode_minus'] = False  #用来正常显示负号ax = plt.gca()#去掉边框ax.spines['top'].set_color('none')ax.spines['right'].set_color('none')#移位置 设为原点相交ax.xaxis.set_ticks_position('bottom')ax.spines['bottom'].set_position(('data',0))ax.yaxis.set_ticks_position('left')ax.spines['left'].set_position(('data',0))# 数据mu = 100 # mean of distributionsigma = 15 # standard deviation of distributionx = mu + sigma * np.random.randn(10000)percentage = 0.95num_bins = 20cnt = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5, cumulative=True)x = []y = []for index in range(len(cnt[1])):    if index != 0:        x.append(cnt[1][index])for index in range(len(cnt[0])):    y.append(cnt[0][index])plt.plot(x, y, "red")x_per = []y_per = []for index in range(len(y)):    if y[index] > 0.95:        y_per.append(y[index-1])        y_per.append(y[index])        x_per.append(x[index-1])        x_per.append(x[index])        breaka = (y_per[1]-y_per[0])/(x_per[1]-x_per[0])b = y_per[1]-a*x_per[1]y_label = percentagex_label = (y_label-b)/aprint(x_label)print(y_label)x1 = np.linspace(0, x_label, 50)y1 = x1*0+percentageplt.plot(x1, y1, "r--")plt.xlabel('品位')plt.ylabel('累计频率')plt.title(r'品位频率累积分布直方图')# Tweak spacing to prevent clipping of ylabelplt.show()

效果如下：

关键代码如下：

ax = plt.gca()#去掉边框ax.spines['top'].set_color('none')ax.spines['right'].set_color('none')#移位置 设为原点相交ax.xaxis.set_ticks_position('bottom')ax.spines['bottom'].set_position(('data',0))ax.yaxis.set_ticks_position('left')ax.spines['left'].set_position(('data',0))