Python机器学习与深度学习之二：数据预处理

一、最值化调整数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = MinMaxScaler(feature_range=(0,1))

newX  = transformer.fit_transform(X)
np.set_printoptions(precision = 3)

print(newX)

运行结果：

二、标准化数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = StandardScaler().fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

三、正态化（归一化）数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import Normalizer

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = Normalizer().fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

四、二值化数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import Binarizer

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = Binarizer(threshold = 0.0).fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

运行结果：