sklearn快速入门教程：独热编码

import pandas as pd
data = pd.read_csv(r"D:\本科\kaggle数据挖掘\titanic\train.csv", index_col = 0)
data.head()

 处理缺失值：

from sklearn.impute import SimpleImputer
Embarked = data.loc[:, "Embarked"].values.reshape(-1,1)
imp_mode = SimpleImputer(strategy = "most_frequent") #most_frequent == 众数
data.loc[:,"Embarked"] = imp_mode.fit_transform(Embarked)
data.info()

 

Int64Index: 891 entries, 1 to 891
Data columns (total 4 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Survived    891 non-null    int64 
 1   Sex         891 non-null    object
 2   Embarked    891 non-null    object
 3   Survived.1  891 non-null    int64 
dtypes: int64(2), object(2)
memory usage: 34.8+ KB

from sklearn.preprocessing import OneHotEncoder
X = data.iloc[:,0:-1] 
X

 

enc = OneHotEncoder(categories = 'auto').fit(X) #categories是自动属性——不用人为输入有什么属性（男、女），自动识别
result = enc.transform(X).toarray() #to array 转化成array
result

array([[0., 1., 0., 0., 1.],
       [1., 0., 1., 0., 0.],
       [1., 0., 0., 0., 1.],
       ...,
       [1., 0., 0., 0., 1.],
       [0., 1., 1., 0., 0.],
       [0., 1., 0., 1., 0.]])

result.shape #查看result的shape

(891, 5)

891行，5列（3+2：

男：10

女：01

S:100

C:010

Q:001

result = pd.DataFrame(result)
result

newdata = pd.concat([X,result], axis = 1)
newdata.head()

 

newdata.drop(["Sex", "Embarked"], axis = 1, inplace = True) #删除掉Sex和Embarked列
newdata.columns = ["Female", "Male", "Embarked_C", "Embarked_Q", "Embarked_S"]
newdata.head()