简单来说 LabelEncoder 是对不连续的数字或者文本进行编号

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit([1,5,67,100])
le.transform([1,1,100,67,5])
输出： array([0,0,3,2,1])

OneHotEncoder 用于将表示分类的数据扩维：

from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
ohe.fit([[1],[2],[3],[4]])
ohe.transform([2],[3],[1],[4]).toarray()
输出：[ [0,1,0,0] , [0,0,1,0] , [1,0,0,0] ,[0,0,0,1] ]

哑变量转换

model_dummy = pd.get_dummies(df_type6['model_id'])
model_dummy.head()