python 信息熵的计算代码 打包时报错的问题：'DataFrame' object has no attribute 'lisan_NO_imformation_S'

把之前的计算信息熵的代码给打包为一个class，创建实例也成功了，但是在将dataframe对象传入方法时却报错了
代码如下：

class entropy:
    def __init__(self,datafr):
        self.datafr=datafr
    def quchonghanshu(one_list):
        return list(set(one_list))
    def lisan_NO_imformation_S(self,jieguo,string,attributes_num):
        all_sum=np.array(self.datafr[string]).shape[0]#参数在这里修改，要求得到这一columns的
        temp_dic={}
        for i in jieguo:
            count=0
            temp_dic[i]=count
            for s in range(all_sum):
                if self.datafr.ix[s,string]==i:
                    count=count+1
            if count==0:
                count=1
            temp_dic[i]=count
        countlog=0
        for i in jieguo: 
            countlog=countlog+temp_dic[i]/all_sum*math.log(temp_dic[i]/all_sum,
                                      attributes_num)
        countlog=-countlog
        return countlog
    def lisan_condition_imformation_S(self,attributes_value,condition_string,
                                      result_string,result_count):
        temp_list=[]
        for k in range(len(self.datafr.index)):
            if self.datafr.ix[k,condition_string]==attributes_value:
                temp_list.append(self.datafr.ix[k,result_string])
        series=pd.Series(temp_list)
        dataframe=pd.DataFrame(series,columns=[result_string])
        temp_list=quchonghanshu(temp_list)
        iu=lisan_NO_imformation_S(temp_list,dataframe,result_string,result_count)
        return iu
    def lisan_imformation_S(self,condition_string,result_string,result1_count):
        all_sum1=np.array(self.datafr[condition_string]).shape[0]
        temp_dic1={}
        for i in quchonghanshu(list(self.datafr[condition_string])):
            count1=0
            for s in range(all_sum1):
                if self.datafr.ix[s,condition_string]==i:
                    count1=count1+1
            temp_dic1[i]=count1
        count1_average=0
        for i in quchonghanshu(list(self.datafr[condition_string])):
            count1_average=count1_average+temp_dic1[i]/all_sum1*lisan_condition_imformation_S(self.datafr,i,condition_string,result_string,result1_count)
        return count1_average