train_df['FareBin'] = pd.qcut(train_df['Fare'],4)
train_df['AgeBin'] = pd.cut(train_df['Age'],5)

from sklearn.preprocessing import LabelEncoder
label_encode = LabelEncoder()
labels = ['Sex','Embarked','AgeBin','FareBin']
for label in labels:
    print(label, type(label))
    new_label = label + '_code'
    train_df[new_label] = label_encode.fit_transform(train_df[label])