# c.请使用for、len、range输出元祖的索引 for idx in range(len(tu)): print(idx) # d.请使用enumerate输出元祖元素和序号(序号从10开始) for idx, elem in enumerate(tu, 10): print(idx, elem) tu =( "alex", [ 11, 22, { "k1":'v1', "k2":["age","name"], "k3":(11,22,33) ...
`max` 函数的参数是一个可迭代对象,其中的元素是元组,通过比较元组中的第一个元素(`val`,即隶属度值)来找到最大值。 5. 整体表达式 `max((val, idx) for (idx, val) in enumerate(membership_mat[i]))` 返回的是具有最大隶属度值的元组 `(max_val, idx)`,其中 `max_val` 是最大的隶属度值,`i...
for task in data: tags.extend(task['tags']) tags = list(set(tags)) # 去重一下 sections = '' counter = 1 for idx, tag in enumerate(tags): section = f'section {tag}\n' sections += section for task in data: if tag in task['tags']: item = f"{task['name']} :t{counter}...
feature_names=feature_names) foridx, speciesinenumerate(dataset.target_names): X, y = dataset.data, dataset.target clf.fit(X, y == idx) rules = clf.rules_[0:3] print("Rules for iris", species) forruleinrules: print(rule) print print(20*'=') print 注意: 如果出现如下错误: 解决方...
ls=[iforiinrange(0,10)]# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]lss=ls# [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]foridx,vinenumerate(lss):lss[idx]=10lss# [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]ls# [10, 10, 10, 10, 10, 10, 10, 10, 10, 10] ...
请注意,与 不同readline,索引不会推进迭代器,因此for idx, item in enumerate(seq):仍会迭代“qux”和“troz”。 适用于任何迭代的方法是使用附加变量来跟踪迭代中的状态。这样做的好处是您不必了解如何手动推进迭代;缺点是对循环内的逻辑进行推理比较困难,因为它暴露了额外的副作用。 first = "Start" first_en...
The coefficientageThe coefficientsex_femaleThe coefficientforsex_maleis-8.762584065506853The coefficientforbmiis0.3807106266997645The coefficientforchildren_0is-0.06605803000190659The coefficientforchildren_1is-0.946643170369065The coefficientforchildren_2is0.2108032984623088The coefficientforchildren_3is0.8800441822437507The ...
for i in range(1,9): # 输出偶数 if int(i)%2 == 0: print(i) 输出:2468 1. 2. 3. 4. 5. 6. 7. 8. enumerate enumerate为循环的list加上index,这个index是编号是从0开始的 list_val = [1,2,3,5,8] for idx,val in enumerate(list_val): ...
( dataset, batch_size=1, shuffle=False, collate_fn=LazyDataset.ignore_none_collate, ) prediction=[] for page_num,page_as_tensor in tqdm(enumerate(dataloader)): model_output = model.inference(image_tensors=page_as_tensor[0]) output = markdown_compatible(model_output["predictions"][0]) ...
# 使用enumerate()实现 ints = [8, 23, 45, 12, 78] for idx, val in enumerate(ints): print(idx, val) ints = [8, 23, 45, 12, 78] for index, item in enumerate(ints, start=0): # 默认是从 0 开始 print(index, item) ints = [8, 23, 45, 12, 78] for index, item in...