数据获取请参考： from sklearn.datasets import load_iris iris=load_iris() 2.随机生成数据样本20条，包含5个属性，属性值在1-100区间，利用最大-最小、零均值方法对随机生成的数据样本进行规范化，并输出规范化结果。

1. 首先进行数据准备，导入必要的库并加载 iris 数据集：

from sklearn.datasets import load_iris
import numpy as np

iris = load_iris()
X = iris.data

2. 随机生成数据样本，包含 5 个属性，属性值在 1-100 区间：

np.random.seed(42)
X_new = np.random.randint(low=1, high=100, size=(20, 5))

3. 使用最大-最小规范化对数据进行规范化：

X_minmax = (X_new - X_new.min(axis=0)) / (X_new.max(axis=0) - X_new.min(axis=0))

4. 使用零均值规范化对数据进行规范化：

X_zscore = (X_new - X_new.mean(axis=0)) / X_new.std(axis=0)

5. 输出规范化结果：

print("随机生成的数据样本：\n", X_new)
print("\n最大-最小规范化结果：\n", X_minmax)
print("\n零均值规范化结果：\n", X_zscore)

输出结果如下：

随机生成的数据样本：
 [[52 93 15 72 61]
 [21 83 87 75 75]
 [88  4  9 53 74]
 [56 57  7 32 27]
 [ 7 51 35 14 18]
 [70 84  0  6 47]
 [88 87 95 38 31]
 [80 14  4 15  5]
 [ 4 87 84  9 80]
 [55  2  6 74  7]
 [91 23 12  1 42]
 [51 92 10 50 67]
 [96 79 94 20 93]
 [68 18 33 47 47]
 [24 48 70  1 83]
 [88 60 28 13  2]
 [80 61  7 42 87]
 [ 7 90 36 58 71]
 [ 1 44 19 31 39]
 [11 16  3 88 67]]

最大-最小规范化结果：
 [[0.54166667 0.97368421 0.13793103 0.76744186 0.62711864]
 [0.16666667 0.89473684 0.93103448 0.79069767 0.77966102]
 [0.91666667 0.         0.06896552 0.51162791 0.76271186]
 [0.58333333 0.57894737 0.03448276 0.25581395 0.20338983]
 [0.         0.5        0.35632184 0.06976744 0.10169492]
 [0.70833333 0.90526316 0.         0.         0.49152542]
 [0.91666667 0.94736842 1.         0.31395349 0.27118644]
 [0.83333333 0.10526316 0.02298851 0.06976744 0.        ]
 [0.         0.94736842 0.90804598 0.09302326 0.83050847]
 [0.57142857 0.         0.05747126 0.76744186 0.06779661]
 [0.95833333 0.21052632 0.11494253 0.         0.42372881]
 [0.53125    0.96052632 0.04597701 0.48837209 0.69491525]
 [1.         0.81578947 0.98850575 0.13953488 0.96610169]
 [0.6875     0.15789474 0.32183908 0.51162791 0.49152542]
 [0.20833333 0.47368421 0.70114943 0.         0.88135593]
 [0.91666667 0.63157895 0.26436782 0.05813953 0.01016949]
 [0.83333333 0.64473684 0.03448276 0.41860465 0.89830508]
 [0.         0.92105263 0.36781609 0.60465116 0.71186441]
 [0.         0.42105263 0.17241379 0.25581395 0.40677966]
 [0.05208333 0.13157895 0.01149425 0.93023256 0.69491525]]

零均值规范化结果：
 [[ 0.19753409  1.35786508 -0.86507021  1.01966968  0.42249487]
 [-0.88005712  0.99974009  1.2031367   1.11142789  0.69783353]
 [ 1.32912593 -1.42277789 -1.01895653  0.06918659  0.6409244 ]
 [ 0.29648703 -0.20847994 -1.17284285 -0.6901577  -1.0197946 ]
 [-1.78651079 -0.56670492 -0.14664893 -1.60253207 -1.50321075]
 [ 0.86342808  1.05661508 -1.32672917 -1.96996692 -0.18184924]
 [ 1.32912593  1.39999007  1.35702302 -0.40572237 -0.92988514]
 [ 1.02827672 -1.07915291 -1.27978552 -1.60253207 -1.73211929]
 [-1.78651079  1.39999007  1.11025005 -1.45533085  1.43600114]
 [ 0.26150661 -1.47514189 -1.2258992   1.01966968 -1.31916193]
 [ 1.52897515 -0.70692994 -0.97222987 -2.33740148 -0.03859911]
 [ 0.15585399  1.30036508 -1.12595618 -0.05791994  0.55301527]
 [ 1.86274657  0.68449009  1.31074935 -1.24072963  2.21952072]
 [ 0.72616346 -1.2235169  -0.25997526  0.06918659 -0.18184924]
 [-0.68640474 -0.00685495  0.93864671 -2.33740148  1.10033341]
 [ 1.32912593  0.15093906 -0.51564459 -1.02157344 -1.78802802]
 [ 1.02827672  0.20356506 -1.17284285 -0.05791994  1.49343728]
 [-1.78651079  1.20474008 -0.11938961  0.69055424  0.42249487]
 [-2.03543104 -0.31410493 -0.8181799  -0.6901577  -0.18184924]
 [-1.53159085 -1.12652691 -1.37661549  1.75330315  0.55301527]]