https://github.com/MLEveryday/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.md

data set:

code:

import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

path = 'C:/Users/liky/Desktop/100-Days-Of-ML-Code-master/datasets/Data.csv'
dataset = pd.read_csv(path)
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values

# 处理丢失数据
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])


# 解析分类数据
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])


# 创建虚拟变量
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)


# 拆分数据集为训练集合和测试集合
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)


# 特征量化
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)