#导入pandas库import pandas as pd#OneHotEncoder用来将数值型类别变量转换为0-1的标志性变量#LabelEncoder用来将字符串型变量转换为数值型变量from sklearn.preprocessing import OneHotEncoder,LabelEncoder #生成数据df=pd.DataFrame({'id':[321313,246852,447902],                'sex':['male','Female','Female'],                'level':['high','low','middle'],                'score':[1,2,3]})print(df)       id     sex   level  score0  321313    male    high      11  246852  Female     low      22  447902  Female  middle      3#拆分id和数据列id_data=df[['id']] #获得id列raw_convert_data=df.iloc[:,1:] #指定要转换的列print(raw_convert_data)      sex   level  score0    male    high      11  Female     low      22  Female  middle      3#将数值型分类向量转换为标志变量model_enc=OneHotEncoder() #建立标志转换模型对象df_new2=model_enc.fit_transform(raw_convert_data).toarray() #标志转换#合并数据df_all=pd.concat((id_data,pd.DataFrame(df_new2)),axis=1) #重新组合为新数据框print(df_all) #打印输出转换后的数据框       id    0    1    2    3    4    5    6    70  321313  0.0  1.0  1.0  0.0  0.0  1.0  0.0  0.01  246852  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.02  447902  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0# 使用pandas的get_dummies做标志转换df_new3 = pd.get_dummies(raw_convert_data)df_all2 = pd.concat((id_data, pd.DataFrame(df_new3)), axis=1)  # 重新组合为数据框print(df_all2)  # 打印输出转换后的数据框       id  score  sex_Female  sex_male  level_high  level_low  level_middle0  321313      1           0         1           1          0             01  246852      2           1         0           0          1             02  447902      3           1         0           0          0             1