Mutiple Plots on One Graph
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.2)) #1.0 = mean, 0.2 = DS
plt.show()
使用plt.savefig 所保存图片为空白:
解决方法:在plt.show()之前调用plt.savefig
画散点图
from pylab import randn X = randn(10000) Y = randn(10000) plt.scatter(X,Y) #注意顺序,先画图再添加坐标轴 axes = plt.axes() axes.set_xlim([0, 1]) axes.set_ylim([0, 4]) plt.show()
covariance:协方差
协方差>0:x,y同向变化,且协方差越大同向程度越高
协方差<0:x,y反向变化,且协方差绝对值越大反向程度越高
correlation计算:covariance/SD
-1:perfect inverse correlation,0:no correlation,1:perfect correlation
贝叶斯公式
#字典,计算不同年龄段人群购买数量 from numpy import random random.seed(0) totals = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0} purchases = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0} totalPurchases = 0 for _ in range(100000): age = random.choice([20, 30, 40, 50, 60, 70]) purchaseProbability = float(age) / 100.0 #除法运算float totals[age] += 1 if (random.random() < purchaseProbability): totalPurchases += 1 purchases[age] += 1