数据预处理Python代码

以下是Python中数据预处理的代码示例：
1.导入标准库

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

2.导入数据集

dataset = pd.read_csv('data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

3.处理缺失数据

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer.fit(X[:,1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

4.处理分类数据

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder='passthrough')
X = ct.fit_transform(X)
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

5.特征缩放

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X[:, 3:] = sc_X.fit_transform(X[:, 3:])

6.小数定标规范化

def deci_sca(data):
    new_data=data/(10**(np.ceil(np.log10(data.max()))))
    return new_data