我需要解析如下所示的arff文件，而不使用任何外部库。我不确定如何将属性与数值关联起来。比如，如何表示每行中的第一个数值是年龄，而第二个数值是性别？你能提供一些用于解析类似场景的Python代码链接吗？
@relation cleveland-14-heart-disease@attribute 'age' real@attribute 'sex' { female, male}@attribute 'cp' { typ_angina, asympt, non_anginal, atyp_angina}@attribute 'trestbps' real@attribute 'chol' real@attribute 'fbs' { t, f}@attribute 'restecg' { left_vent_hyper, normal, st_t_wave_abnormality}@attribute 'thalach' real@attribute 'exang' { no, yes}@attribute 'oldpeak' real@attribute 'slope' { up, flat, down}@attribute 'ca' real@attribute 'thal' { fixed_defect, normal, reversable_defect}@attribute 'class' { negative, positive}@data63,male,typ_angina,145,233,t,left_vent_hyper,150,no,2.3,down,0,fixed_defect,negative37,male,non_anginal,130,250,f,normal,187,no,3.5,down,0,normal,negative41,female,atyp_angina,130,204,f,left_vent_hyper,172,no,1.4,up,0,normal,negative56,male,atyp_angina,120,236,f,normal,178,no,0.8,up,0,normal,negative57,female,asympt,120,354,f,normal,163,yes,0.6,up,0,normal,negative57,male,asympt,140,192,f,normal,148,no,0.4,flat,0,fixed_defect,negative56,female,atyp_angina,140,294,f,left_vent_hyper,153,no,1.3,flat,0,normal,negative44,male,atyp_angina,120,263,f,normal,173,no,0,up,0,reversable_defect,negative52,male,non_anginal,172,199,t,normal,162,no,0.5,up,0,reversable_defect,negative这是我编写的一个示例代码：
arr=[]arff_file = open("heart_train.arff")count=0for line in arff_file: count+=1 #line=line.strip("\n") #line=line.split(',') if not (line.startswith("@")): if not (line.startswith("%")): line=line.strip("\n") line=line.split(',') arr.append(line)print(arr[1:30])然而，输出结果与我预期的非常不同：
[['37', 'male', 'non_anginal', '130', '250', 'f', 'normal', '187', 'no', '3.5', 'down', '0', 'normal', 'negative'], ['41', 'female', 'atyp_angina', '130', '204', 'f', 'left_vent_hyper', '172', 'no', '1.4', 'up', '0', 'normal', 'negative'], ['56', 'male', 'atyp_angina', '120', '236', 'f', 'normal', '178', 'no', '0.8', 'up', '0', 'normal', 'negative'], ['57', 'female', 'asympt', '120', '354', 'f', 'normal', '163', 'yes', '0.6', 'up', '0', 'normal', 'negative'], ['57', 'male', 'asympt', '140', '192', 'f', 'normal', '148', 'no', '0.4', 'flat', '0', 'fixed_defect', 'negative'], ['56', 'female', 'atyp_angina', '140', '294', 'f', 'left_vent_hyper', '153', 'no', '1.3', 'flat', '0', 'normal', 'negative'], ['44', 'male', 'atyp_angina', '120', '263', 'f', 'normal', '173', 'no', '0', 'up', '0', 'reversable_defect', 'negative'], ['52', 'male', 'non_anginal', '172', '199', 't', 'normal', '162', 'no', '0.5', 'up', '0', 'reversable_defect', 'negative'], ['57', 'male', 'non_anginal', '150', '168', 'f', 'normal', '174', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['54', 'male', 'asympt', '140', '239', 'f', 'normal', '160', 'no', '1.2', 'up', '0', 'normal', 'negative'], ['48', 'female', 'non_anginal', '130', '275', 'f', 'normal', '139', 'no', '0.2', 'up', '0', 'normal', 'negative'], ['49', 'male', 'atyp_angina', '130', '266', 'f', 'normal', '171', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['64', 'male', 'typ_angina', '110', '211', 'f', 'left_vent_hyper', '144', 'yes', '1.8', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'typ_angina', '150', '283', 't', 'left_vent_hyper', '162', 'no', '1', 'up', '0', 'normal', 'negative'], ['50', 'female', 'non_anginal', '120', '219', 'f', 'normal', '158', 'no', '1.6', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'non_anginal', '120', '340', 'f', 'normal', '172', 'no', '0', 'up', '0', 'normal', 'negative'], ['66', 'female', 'typ_angina', '150', '226', 'f', 'normal', '114', 'no', '2.6', 'down', '0', 'normal', 'negative'], ['43', 'male', 'asympt', '150', '247', 'f', 'normal', '171', 'no', '1.5', 'up', '0', 'normal', 'negative'], ['69', 'female', 'typ_angina', '140', '239', 'f', 'normal', '151', 'no', '1.8', 'up', '2', 'normal', 'negative'], ['59', 'male', 'asympt', '135', '234', 'f', 'normal', '161', 'no', '0.5', 'flat', '0', 'reversable_defect', 'negative'], ['44', 'male', 'non_anginal', '130', '233', 'f', 'normal', '179', 'yes', '0.4', 'up', '0', 'normal', 'negative'], ['42', 'male', 'asympt', '140', '226', 'f', 'normal', '178', 'no', '0', 'up', '0', 'normal', 'negative'], ['61', 'male', 'non_anginal', '150', '243', 't', 'normal', '137', 'yes', '1', 'flat', '0', 'normal', 'negative'], ['40', 'male', 'typ_angina', '140', '199', 'f', 'normal', '178', 'yes', '1.4', 'up', '0', 'reversable_defect', 'negative'], ['71', 'female', 'atyp_angina', '160', '302', 'f', 'normal', '162', 'no', '0.4', 'up', '2', 'normal', 'negative'], ['59', 'male', 'non_anginal', '150', '212', 't', 'normal', '157', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['51', 'male', 'non_anginal', '110', '175', 'f', 'normal', '123', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['65', 'female', 'non_anginal', '140', '417', 't', 'left_vent_hyper', '157', 'no', '0.8', 'up', '1', 'normal', 'negative'], ['53', 'male', 'non_anginal', '130', '197', 't', 'left_vent_hyper', '152', 'no', '1.2', 'down', '0', 'normal', 'negative']]你知道如何得到如下由arff库（来自Weka）创建的输出吗？
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