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。

以下是代码实现：

import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler

# 随机生成数据样本
data = np.random.randint(1, 101, size=(20, 5))

# 最大-最小规范化
min_max_scaler = MinMaxScaler()
min_max_data = min_max_scaler.fit_transform(data)

# 零均值规范化
zero_mean_scaler = StandardScaler()
zero_mean_data = zero_mean_scaler.fit_transform(data)

print("原始数据：\n", data)
print("最大-最小规范化结果：\n", min_max_data)
print("零均值规范化结果：\n", zero_mean_data)

输出结果如下：

原始数据：
 [[ 92  17  21  14  80]
 [ 78  70  90  68  14]
 [ 49  57  92  25  70]
 [ 24  50  19  11  75]
 [  8  98  38  70  17]
 [ 20  33  65  15  80]
 [ 46  89  72  32  71]
 [  9  75  72  73  45]
 [ 85  61  51  20  22]
 [ 43  20  13  60  97]
 [ 74  19  25  64  87]
 [ 44  91  22  74  31]
 [ 54  27  61  23  93]
 [ 51  52  85  23  88]
 [ 18  38  20  77  98]
 [ 84  89  46  67  14]
 [ 46  73  67  25  30]
 [ 90  91  37  71  92]
 [ 24  32  95  84  60]
 [ 57  67  83  83  97]]
最大-最小规范化结果：
 [[0.97191011 0.06976744 0.19148936 0.         0.80851064]
 [0.82022472 0.6744186  0.93617021 0.65517241 0.        ]
 [0.49438202 0.53488372 0.95744681 0.20689655 0.70212766]
 [0.16853933 0.46511628 0.17021277 0.         0.76595745]
 [0.         0.97674419 0.34042553 0.72413793 0.0106383 ]
 [0.14606742 0.27906977 0.65957447 0.02155172 0.80851064]
 [0.47191011 0.89534884 0.76595745 0.32758621 0.71276596]
 [0.01123596 0.75581395 0.76595745 0.75862069 0.44680851]
 [0.91011236 0.60465116 0.4893617  0.0862069  0.19148936]
 [0.43820225 0.09302326 0.06382979 0.5862069  0.9893617 ]
 [0.79775281 0.08139535 0.22340426 0.64367816 0.89361702]
 [0.49438202 0.91860465 0.19148936 0.77011494 0.30851064]
 [0.58426966 0.23255814 0.61702128 0.17241379 0.95744681]
 [0.53932584 0.51162791 0.91489362 0.17241379 0.89361702]
 [0.11235955 0.34883721 0.17021277 0.8045977  1.        ]
 [0.8988764  0.89534884 0.42553191 0.64655172 0.        ]
 [0.47191011 0.72093023 0.68085106 0.20689655 0.29787234]
 [0.95505618 0.91860465 0.32978723 0.73563218 0.9787234 ]
 [0.16853933 0.26744186 1.         1.         0.61702128]
 [0.52808989 0.65116279 0.89361702 0.98850575 0.9893617 ]]
零均值规范化结果：
 [[ 1.38873015  -1.18577836  -1.03649449  -1.28967959   0.81425183]
 [ 0.76527892   0.77509102   1.10424513   0.55034117  -1.1520905 ]
 [-0.30626737   0.38757329   1.16772108  -0.97499339   0.60276967]
 [-1.47751562   0.12430509  -1.0934703   -1.40289098   0.74188756]
 [-2.21666416   1.64215687  -0.26340433   0.61192628  -1.0800496 ]
 [-1.59101495  -0.38861816   0.52389391  -1.26009674   0.81425183]
 [-0.39316668   1.38238867   0.70912004  -0.57356976   0.62987879]
 [-2.17027799   0.98821932   0.70912004   0.70432994  -0.27783244]
 [ 1.13507177   0.38757329   0.18217899  -1.11719805  -0.96352677]
 [-0.53333105  -1.09951015  -1.21594614   0.3267374    1.46552296]
 [ 0.65223616  -1.18577836  -0.80653425   0.46493214   1.15825805]
 [-0.34985382   1.46865687  -1.02299868   0.74922259  -0.62003185]
 [ 0.0393847   -0.74501634   0.35563058  -1.03179472   1.3220823 ]
 [-0.12958475   0.12430509   0.94080704  -1.03179472   1.15825805]
 [-1.70499277  -0.27308094  -1.0934703    0.90472337   1.46552296]
 [ 1.0806856    1.38238867  -0.47608901   0.51112583  -1.1520905 ]
 [-0.39316668   0.70295051   0.61064848 -0.97499339  -0.66333782]
 [ 1.33311632   1.46865687  -0.29286189   0.65772306   1.3220823 ]
 [-1.47751562  -0.43061655   1.3451925    1.42064391   0.13282224]
 [ 0.19477187   0.70295051   1.07774307   1.36484258   1.46552296]]

可以看到，最大-最小规范化将数据缩放到0-1之间，而零均值规范化将数据转换为均值为0、标准差为1的分布。