整个数据框python的最小最大缩放

如何解决整个数据框python的最小最大缩放

我正在使用从sklearn.preprocessing导入MinMaxScaler 具有以下代码和数据集：

df = pd.DataFrame({
  "A" : [-0.5624105,-0.5637749,0.2973856,0.619784,0.007297921,0.8146919,0.1082434,-0.2311236,-0.6945567,-0.6807524,-0.1017431,0.5889628,0.5384794,0.3906553,0.3843442,0.4408366,0.4035791,0.05258237,-0.4847771
],"B" : [-0.5068743,0.1422121,0.6444226,0.4959088,-0.2260773,0.3420533,0.2346546,0.1177824,-0.7701161,-0.7566853,-0.5091485,0.4509938,0.4209853,0.304058,0.3753832,0.6958977,0.6763205,0.05536954,-0.9857719
]})

min_max_scaler = MinMaxScaler(feature_range=(0,255))

print(df)

df[df.columns] = min_max_scaler.fit_transform(df[df.columns])

print(df)
print(type(df))

我想用整个数据集中的最小值和整个数据集中的最大值进行缩放如何使用相同的代码进行管理？有可能吗？

           A         B
0  -0.562411 -0.506874
1  -0.563775  0.142212
2   0.297386  0.644423
3   0.619784  0.495909
4   0.007298 -0.226077
5   0.814692  0.342053
6   0.108243  0.234655
7  -0.231124  0.117782
8  -0.694557 -0.770116
9  -0.680752 -0.756685
10 -0.101743 -0.509149
11  0.588963  0.450994
12  0.538479  0.420985
13  0.390655  0.304058
14  0.384344  0.375383
15  0.440837  0.695898
16  0.403579  0.676320
17  0.052582  0.055370
18 -0.484777 -0.985772
             A           B
0    22.327190   72.617646
1    22.096664  171.041874
2   167.596834  247.194572
3   222.068703  224.674680
4   118.584127  115.196304
5   255.000000  201.344798
6   135.639699  185.059394
7    78.300845  167.337476
8     0.000000   32.700971
9     2.332350   34.737551
10  100.160748   72.272798
11  216.861207  217.863993
12  208.331620  213.313653
13  183.355519  195.583380
14  182.289206  206.398778
15  191.834063  255.000000
16  185.539101  252.031411
17  126.235309  157.873501
18   35.443994    0.000000

我不希望每列都有不同的映射，我需要使用-0.985772 0.814692（b列第18行，a列第5行）进行映射

解决方法

您有2种方法可以做到这一点：

# Manually:
min_value,max_value = df.min().min(),df.max().max()
scaled1 = (df - min_value) * 255 / (max_value - min_value)

# Using MinMaxScaler
min_max_scaler = MinMaxScaler(feature_range=(0,255))    
# Stack everything into a single column to scale by the global min / max
tmp = df.to_numpy().reshape(-1,1)
scaled2 = min_max_scaler.fit_transform(tmp).reshape(len(df),2)

两者返回相同的结果：

np.isclose(scaled1,scaled2).all()
# True

您可以使用缩放后的值制作一个新的DataFrame：

scaled = pd.DataFrame(scaled1,index=df.index,columns=df.columns)

或将它们分配回df：

df.loc[:] = scaled1