这是一段具有良好的Python性的代码，并且平衡了简单性和明显性，我认为，这应该也适用于相当老的Python版本。

def all_eq(lst):
    for idx, itm in enumerate(lst):
        if not idx:   # == 0
            prev = itm
        if itm != prev:
            return False
        prev = itm
    return True

最佳答案

Twitter上有一个不错的帖子，介绍了实现all_equal()函数的各种方法。

给定一个列表输入，最好的提交是:

 t.count(t[0]) == len(t)  

其他方法

下面是线程的结果:

Have groupby() compare adjacent entries. This has an early-out for a mismatch, does not use extra memory, and it runs at C speed. g = itertools.groupby(s) next(g, True) and not next(g, False) Compare two slices offset from one another by one position. This uses extra memory but runs at C speed. s[1:] == s[:-1] Iterator version of slice comparison. It runs at C speed and does not use extra memory; however, the eq calls are expensive. all(map(operator.eq, s, itertools.islice(s, 1, None))) Compare the lowest and highest values. This runs at C speed, doesn't use extra memory, but does cost two inequality tests per datum. min(s) == max(s) # s must be non-empty Build a set. This runs at C speed and uses little extra memory but requires hashability and does not have an early-out. len(set(t))==1. At great cost, this handles NaNs and other objects with exotic equality relations. all(itertools.starmap(eq, itertools.product(s, repeat=2))) Pull out the first element and compare all the others to it, stopping at the first mismatch. Only disadvantage is that this doesn't run at C speed. it = iter(s) a = next(it, None) return all(a == b for b in it) Just count the first element. This is fast, simple, elegant. It runs at C speed, requires no additional memory, uses only equality tests, and makes only a single pass over the data. t.count(t[0]) == len(t)

您可以使用.nunique()来查找列表中唯一项的数量。

def identical_elements(list):
    series = pd.Series(list)
    if series.nunique() == 1: identical = True
    else:  identical = False
    return identical



identical_elements(['a', 'a'])
Out[427]: True

identical_elements(['a', 'b'])
Out[428]: False

出现使用itertools。Groupby(参见itertools食谱):

from itertools import groupby

def all_equal(iterable):
    g = groupby(iterable)
    return next(g, True) and not next(g, False)

或不带groupby:

def all_equal(iterator):
    iterator = iter(iterator)
    try:
        first = next(iterator)
    except StopIteration:
        return True
    return all(first == x for x in iterator)

您可以考虑使用许多其他的一行程序:

Converting the input to a set and checking that it only has one or zero (in case the input is empty) items def all_equal2(iterator): return len(set(iterator)) <= 1 Comparing against the input list without the first item def all_equal3(lst): return lst[:-1] == lst[1:] Counting how many times the first item appears in the list def all_equal_ivo(lst): return not lst or lst.count(lst[0]) == len(lst) Comparing against a list of the first element repeated def all_equal_6502(lst): return not lst or [lst[0]]*len(lst) == lst

但它们也有一些缺点，即:

all_equal and all_equal2 can use any iterators, but the others must take a sequence input, typically concrete containers like a list or tuple. all_equal and all_equal3 stop as soon as a difference is found (what is called "short circuit"), whereas all the alternatives require iterating over the entire list, even if you can tell that the answer is False just by looking at the first two elements. In all_equal2 the content must be hashable. A list of lists will raise a TypeError for example. all_equal2 (in the worst case) and all_equal_6502 create a copy of the list, meaning you need to use double the memory.

在Python 3.9中，使用perfplot，我们得到这些计时(越低的Runtime [s]越好):

这是一个有趣的阅读和思考。谢谢大家! 我不认为任何依赖于纯计数的方法对所有情况都是可靠的。sum也可以工作，但只适用于数字或长度(再次导致计数场景)。

但我喜欢简单，所以我想出了这个:

all(i==lst[c-1] for c, i in enumerate(lst))

或者，我确实认为@kennytm的这个聪明的方法也适用于所有情况(有趣的是，它可能是最快的)。所以我承认它可能比我的好:

[lst[0]]*len(lst) == lst

我认为一个聪明的小奖励也会起作用，因为set消除了重复(聪明是有趣的，但通常不是维护代码的最佳实践)。我认为@kennytm的方法仍然会更快，但只适用于大型列表:

len(set(lst)) == 1

但是Python的简单和聪明是我最喜欢的语言之一。再想一下，如果你必须修改列表，就像我实际上做的那样，因为我正在比较地址(并将删除开头/结尾空格并转换为小写以消除可能的不一致，我的将更适合这项工作)。所以“更好”是主观的，因为我在使用这个词时使用了引号!但是你也可以事先清理列表。

祝你好运!

lambda lst: reduce(lambda a,b:(b,b==a[0] and a[1]), lst, (lst[0], True))[1]

下一个会短路:

all(itertools.imap(lambda i:yourlist[i]==yourlist[i+1], xrange(len(yourlist)-1)))