zancun

import numpy as np
import matplotlib.pyplot as plt

mu = 1  #期望为1
sigma = 3  #标准差为3
num = 10000  #个数为10000

rand_data = np.random.normal(mu, sigma, num)
print(rand_data.shape,type(rand_data))

count, bins, ignored = plt.hist(rand_data, 30, normed=True)
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (bins - mu)**2 / (2 * sigma**2)), linewidth=2, color='r')
plt.show()

np.arange(5)  list(range(5))
np.array([a,b])
np.arange(0,60,5) .reshape(3,4) 
np.linspace(0,20) #在指定的间隔内返回均匀间隔的数字。

np.random.random(10) #(0,1）以内10个随机浮点数
np.random.randint(1,100,[5,5]) #(1,100）以内的5行5列随机整数
np.random.rand(2,3) #产生2行3列均匀分布随机数组
np.random.randn(3,3) #3行3列正态分布随机数据
import numpy

from sklearn.datasets import load_iris    
data = load_iris()
print(data)
petal_length = data['data'][,3]
data1 = np.max(petal_length)
data2 = np.min(petal_length)
data3 = np.meanpetal_length)
data4 = np.std(petal_length)
data5 = np.median(petal_length