数据预处理之标准化

数据处理的标准化主要调用sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)

下面用代码带领着我们去看一下它的使用方式：

from sklearn.preprocessing import StandardScaler
import numpy as np
##给出数据，若数据只有一行，要转为列向量，否则会报错
data = np.array(range(100)).reshape(-1, 1)
out:
array([[ 0],
       [ 1],
       [ 2],
       [ 3],
       [ 4],
       [ 5],
       [ 6],
       [ 7],
       [ 8],
       [ 9],
       [10],
       [11],
       [12],
       [13],
       [14],
       [15],
       [16],
       [17],
       [18],
       [19],
       [20],
       [21],
       [22],
       [23],
       [24],
       [25],
       [26],
       [27],
       [28],
       [29],
       [30],
       [31],
       [32],
       [33],
       [34],
       [35],
       [36],
       [37],
       [38],
       [39],
       [40],
       [41],
       [42],
       [43],
       [44],
       [45],
       [46],
       [47],
       [48],
       [49],
       [50],
       [51],
       [52],
       [53],
       [54],
       [55],
       [56],
       [57],
       [58],
       [59],
       [60],
       [61],
       [62],
       [63],
       [64],
       [65],
       [66],
       [67],
       [68],
       [69],
       [70],
       [71],
       [72],
       [73],
       [74],
       [75],
       [76],
       [77],
       [78],
       [79],
       [80],
       [81],
       [82],
       [83],
       [84],
       [85],
       [86],
       [87],
       [88],
       [89],
       [90],
       [91],
       [92],
       [93],
       [94],
       [95],
       [96],
       [97],
       [98],
       [99]])

scaler = StandardScaler()
scaler.fit(data)
out:
StandardScaler(copy=True, with_mean=True, with_std=True)  
##下面的这几行主要是观测参数，没有多大作用
scaler.scale_ 
scaler.mean_  #样本均值
scaler.var_   #样本方差
scaler.n_samples_seen_ #样本的shape

##对数据进行标准化，下面的这两行作用相同
scaler.fit_transform(data)
scaler.transform(data)
out:
array([[-1.71481604],
       [-1.68017329],
       [-1.64553055],
       [-1.6108878 ],
       [-1.57624505],
       [-1.5416023 ],
       [-1.50695955],
       [-1.4723168 ],
       [-1.43767406],
       [-1.40303131],
       [-1.36838856],
       [-1.33374581],
       [-1.29910306],
       [-1.26446031],
       [-1.22981757],
       [-1.19517482],
       [-1.16053207],
       [-1.12588932],
       [-1.09124657],
       [-1.05660382],
       [-1.02196108],
       [-0.98731833],
       [-0.95267558],
       [-0.91803283],
       [-0.88339008],
       [-0.84874733],
       [-0.81410459],
       [-0.77946184],
       [-0.74481909],
       [-0.71017634],
       [-0.67553359],
       [-0.64089084],
       [-0.6062481 ],
       [-0.57160535],
       [-0.5369626 ],
       [-0.50231985],
       [-0.4676771 ],
       [-0.43303435],
       [-0.39839161],
       [-0.36374886],
       [-0.32910611],
       [-0.29446336],
       [-0.25982061],
       [-0.22517786],
       [-0.19053512],
       [-0.15589237],
       [-0.12124962],
       [-0.08660687],
       [-0.05196412],
       [-0.01732137],
       [ 0.01732137],
       [ 0.05196412],
       [ 0.08660687],
       [ 0.12124962],
       [ 0.15589237],
       [ 0.19053512],
       [ 0.22517786],
       [ 0.25982061],
       [ 0.29446336],
       [ 0.32910611],
       [ 0.36374886],
       [ 0.39839161],
       [ 0.43303435],
       [ 0.4676771 ],
       [ 0.50231985],
       [ 0.5369626 ],
       [ 0.57160535],
       [ 0.6062481 ],
       [ 0.64089084],
       [ 0.67553359],
       [ 0.71017634],
       [ 0.74481909],
       [ 0.77946184],
       [ 0.81410459],
       [ 0.84874733],
       [ 0.88339008],
       [ 0.91803283],
       [ 0.95267558],
       [ 0.98731833],
       [ 1.02196108],
       [ 1.05660382],
       [ 1.09124657],
       [ 1.12588932],
       [ 1.16053207],
       [ 1.19517482],
       [ 1.22981757],
       [ 1.26446031],
       [ 1.29910306],
       [ 1.33374581],
       [ 1.36838856],
       [ 1.40303131],
       [ 1.43767406],
       [ 1.4723168 ],
       [ 1.50695955],
       [ 1.5416023 ],
       [ 1.57624505],
       [ 1.6108878 ],
       [ 1.64553055],
       [ 1.68017329],
       [ 1.71481604]])
##下面是对数据进行还原
scaler.inverse_transform(scaler.fit_transform(data))