注：數(shù)據(jù)導(dǎo)入見：Python學(xué)習(xí)之?dāng)?shù)據(jù)導(dǎo)入

1、讀取數(shù)據(jù)（X：獨(dú)立數(shù)據(jù)、Y：聯(lián)動(dòng)數(shù)據(jù)）

處理之前的數(shù)據(jù)

#導(dǎo)入包

import numpy as np #矩陣

import matplotlib.pyplot as plt #數(shù)據(jù)展示、可視化

import pandas as pd? ? #數(shù)據(jù)預(yù)處理

#import dataset

datasets = pd.read_csv('Data.csv')

#missing data 丟失數(shù)據(jù)處理 1、去最大值 最小值，2、平均數(shù) 3、刪除

X = datasets.iloc[:,:-1].values? #取出獨(dú)立變量

Y = datasets.iloc[:,3].values

#數(shù)據(jù)預(yù)處理,補(bǔ)充缺失數(shù)據(jù)

from sklearn.preprocessing import Imputer

#mean 缺失的用平均數(shù)填充

#怎么處理數(shù)據(jù)

imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)

#處理哪里的數(shù)據(jù)

imputer = imputer.fit( X[:, 1:3])

X[:,1:3] = imputer.transform( X[:,1:3])

#查看補(bǔ)充缺失數(shù)據(jù)之后的數(shù)據(jù)

X

解釋：“imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)：

NaN:缺失數(shù)據(jù)

strategy：缺失數(shù)據(jù)處理方式，平均值，

If “mean”, then replace missing values using the mean along the axis.

If “median”, then replace missing values using the median along the axis.

If “most_frequent”, then replace missing using the most frequent value along the axis.

axis：

Ifaxis=0, then impute along columns.Ifaxis=1, then impute along rows.

2、查看補(bǔ)充缺失數(shù)據(jù)之后的數(shù)據(jù)

補(bǔ)充缺失之后的數(shù)據(jù)