如果需要按位置选择，请使用
iloc：
train_features = train_df.iloc[:, [0,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]]print (train_features) age default housing loan equities contact duration campaign pdays 56 1 1 1 1 0 261 1 999 1 37 1 0 1 1 0 226 1 999 2 56 1 1 0 1 0 307 1 999 previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m 0 21.1 93.994 -36.4 3.299552 1 0 21.1 93.994 -36.4 0.743751 2 0 21.1 93.994 -36.4 1.282652 nr.employed 0 5191 1 5191 2 5191
另一个解决方案是
drop不必要的列：
cols= ['job','marital','education','y']train_features = train_df.drop(cols, axis=1)print (train_features) age default housing loan equities contact duration campaign pdays 56 1 1 1 1 0 261 1 999 1 37 1 0 1 1 0 226 1 999 2 56 1 1 0 1 0 307 1 999 previous poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m 0 21.1 93.994 -36.4 3.299552 1 0 21.1 93.994 -36.4 0.743751 2 0 21.1 93.994 -36.4 1.282652 nr.employed 0 5191 1 5191 2 5191
