成功解决TypeError: Encoders require their input to be uniformly strings or numbers. Got [‘float‘, ‘int‘,‘str’]

今天在给数据编码的时候，在将离散特征转换为整数编码时，出现了报错如下：
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后来发现可以先将要编码的离散变量全部转换成字符型的，例如作出如下修改：

from sklearn.preprocessing import LabelEncoder
clean_loan_of[attri_cols] = clean_loan_of[attri_cols].astype('str') 
dfobject = clean_loan_df[attri_cols].select_dtypes(['object'])
def labelencode(columnlabel):
    clean_loan_df[columnlabel] = LabelEncoder().fit_transform(clean_loan_df[columnlabel])
for i in range(0, len(dfobject.columns)):
    labelencode(dfobject.columns[i])

就可以完成转换啦！