Data preprocessing in Python (code)

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1. Import standard library

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

2. Import data set

dataset = pd.read_csv('data (1).csv')  # read_csv：读取csv文件
#创建一个包含所有自变量的矩阵，及因变量的向量
#iloc表示选取数据集的某行某列；逗号之前的表示行，之后的表示列；冒号表示选取全部，没有冒号，则表示选取第几列；values表示选取数据集里的数据。
X = dataset.iloc[:, :-1].values # 选取数据，不选取最后一列。
y = dataset.iloc[:, 3].values # 选取数据,选取每行的第3列数据

3. Missing data

from sklearn.preprocessing import Imputer #进行数据挖掘及数据分析的标准库，Imputer缺失数据的处理
#Imputer中的参数：missing_values 缺失数据，定义怎样辨认确实数据，默认值：nan ;strategy 策略，补缺值方式 ： mean-平均值 ， median-中值 ， most_frequent-出现次数最多的数 ； axis =0取列 =1取行
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 1:3])#拟合fit
X[:, 1:3] = imputer.transform(X[:, 1:3])

4. Classified data

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder_X=LabelEncoder()
X[:,0]=labelencoder_X.fit_transform(X[:,0])
onehotencoder=OneHotEncoder(categorical_features=[0])
X=onehotencoder.fit_transform(X).toarray()
#因为Purchased是因变量，Python里面的函数可以将其识别为分类数据，所以只需要LabelEncoder转换为分类数字
labelencoder_y=LabelEncoder()
y=labelencoder_y.fit_transform(y)

5. Divide the data set into a training set and a test set

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)
#X_train（训练集的字变量）,X_test（测试集的字变量）,y_train（训练集的因变量）,y_test（训练集的因变量）
#训练集所占的比重0.2~0.25，某些情况也可分配1/3的数据给训练集；train_size训练集所占的比重
#random_state决定随机数生成的方式，随机的将数据分配给训练集和测试集；random_state相同时会得到相同的训练集和测试集

6. Feature scaling

#特征缩放（两种方式：一：Standardisation(标准化）；二：Normalisation（正常化））
from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
X_train=sc_X.fit_transform(X_train)#拟合,对X_train进行缩放
X_test=sc_X.transform(X_test)#sc_X已经被拟合好了，所以对X_test进行缩放时，直接转换X_test

7. Data preprocessing template

(1) Import standard library
(2) Import data set
(3) Few missing and classified items Encounter
(4) Split the data set into a training set and a test set
(5) Feature scaling is not needed in most cases, but feature scaling is required in some cases

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