print ("List after not in-place shuffle : ", new_list) Original list : [5, 10, 15, 20, 25] List after not in-place shuffle : [25, 5, 10, 20, 15] 如您所见,我们使用了一个示例方法来执行非就地无序播放。 我们首先定义了一个新的列表来存储新的...
random.shuffle(string_list) print("第二次shuffle后的字符串列表:",string_list) 原始字符串列表: ['Paint It Black', 'Gimme Shelter', '同情魔鬼', '满意', '你不能总是得到你想要的东西'] 第一次shuffle之后的字符串列表: ['Gimme Shelter', '你不能总是得到你想要的东西', '同情魔鬼', 'Paint...
If you want to perform shuffling as per your need, you can pass a custom function in the place of therandomargument, which will dictate theshuffle()function on how to randomize a list’s items. Example: – importrandom number_list = [7,14,21,28,35,42,49,56,63,70]# Original listpr...
如果想快速解决问题,那么是列表直接使用方法(1),如果是numpy.array()请使用方法(2)。 欲知区别和原因,请仔细看下面的分析: 首先需要区分两个random.shuffle()使用方法,一个是random中的,一个是numpy自带的。 (1)使用random带的random.shuffle() 使用方法: &nbs... ...
The random.shuffle() is a commonly used and recommended method to shuffle a list in Python. This shuffle method actually shuffles the element in place meaning it modifies the original list, hence, the ordering of the original elements in the List is lost. However, this may not be a proble...
random.shuffle(deck) # Sort by poker order and then by suit deck.sort(key=by_poker_order) deck.sort(key=by_suit) for k, g in groupby(deck, key=lambda c: c[-1]): print(k, list(g)) The code example creates a deck of cards. It groups the cards by suit and sorts them. ...
for i in range(1, 6) # 这里缺少冒号 s = s + i print( s) 6.IndexError: list index out of range 越界访问列表,下标超出了列表的范围。 a = [10, 20, 30] print(a[3]) # 由于下标是从0开始计数,此处最大下标为2,修改成a[2] ...
你可以把random.choice(someList)看成是someList[random.randint(0, len(someList) – 1]的一个简称。 random.shuffle()函数将对列表中的项目进行重新排序。这个函数原地修改列表,而不是返回一个新的列表。在交互式 Shell 中输入以下内容: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>> import rando...
系统自带函数shuffle import random test = [1, 2, 3, 4, 5] random.shuffle(test) print(test) Python...的random.shuffle()函数可以用来乱序序列,它是在序列的本身打乱,而不是新生成一个序列。...附:python中shuffle函数 def shuffle(self, x, random=None): """Shuffle list x in place, and ...
(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False) input_dim = X_train.shape[1] # 输入维度 hidden_dim = 128 output_dim = len(label_encoder.classes_) # 输出维度 model = TextClassifier(input_dim, hidden_dim, output_dim) ...