在嵌套列表上列出理解？

Question

我有这个嵌套列表：

l = [['40', '20', '10', '30'], ['20', '20', '20', '20', '20', '30', '20'], ['30', '20', '30', '50', '10', '30', '20', '20', '20'], ['100', '100'], ['100', '100', '100', '100', '100'], ['100', '100', '100', '100']]

现在，我想要做的是将列表中的每个元素转换为float。我的解决方案是：

newList = []
for x in l:
  for y in x:
    newList.append(float(y))

但这可以使用嵌套列表理解来完成吗，对吗？

我所做的是：

[float(y) for y in x for x in l]

但结果是一堆100的总和为2400。

任何解决方案，将非常感谢解释。谢谢！

Answer 1

以下是使用嵌套列表解析的方法：

[[float(y) for y in x] for x in l]

这会给你一个列表列表，类似于你开始的列表，除了浮点数而不是字符串。如果您想要一个平面列表，那么您将使用[float(y) for x in l for y in x]。

Answer 2

以下是如何将嵌套for循环转换为嵌套列表解析：

以下是嵌套列表理解的工作原理：

            l a b c d e f
            ↓ ↓ ↓ ↓ ↓ ↓ ↓
In [1]: l = [ [ [ [ [ [ 1 ] ] ] ] ] ]
In [2]: for a in l:
   ...:     for b in a:
   ...:         for c in b:
   ...:             for d in c:
   ...:                 for e in d:
   ...:                     for f in e:
   ...:                         print(float(f))
   ...:                         
1.0

In [3]: [float(f)
         for a in l
   ...:     for b in a
   ...:         for c in b
   ...:             for d in c
   ...:                 for e in d
   ...:                     for f in e]
Out[3]: [1.0]

#Which can be written in single line as
In [4]: [float(f) for a in l for b in a for c in b for d in c for e in d for f in e]
Out[4]: [1.0]

Answer 3

>>> l = [['40', '20', '10', '30'], ['20', '20', '20', '20', '20', '30', '20'], ['30', '20', '30', '50', '10', '30', '20', '20', '20'], ['100', '100'], ['100', '100', '100', '100', '100'], ['100', '100', '100', '100']]
>>> new_list = [float(x) for xs in l for x in xs]
>>> new_list
[40.0, 20.0, 10.0, 30.0, 20.0, 20.0, 20.0, 20.0, 20.0, 30.0, 20.0, 30.0, 20.0, 30.0, 50.0, 10.0, 30.0, 20.0, 20.0, 20.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]

Answer 4

不确定您想要的输出是什么，但是如果您正在使用列表理解，则顺序遵循嵌套循环的顺序，这是您向后的顺序。所以我得到了我认为你想要的东西：

<div id="wrapper">
    <div class="row grid-space-0">
    ...
    </div>
</div>

原则是：使用与编写嵌套for循环时使用的顺序相同的顺序。

Answer 5

由于我在这里很晚，但我想分享实际列表理解是如何工作的，尤其是嵌套列表理解：

   New_list= [[float(y) for x in l]

实际上与：

相同

New_list=[]
for x in l:
    New_list.append(x)

现在嵌套列表理解：

[[float(y) for y in x] for x in l]

与;

相同

new_list=[]
for x in l:
    sub_list=[]
    for y in x:
        sub_list.append(float(y))

    new_list.append(sub_list)

print(new_list)

输出：

[[40.0, 20.0, 10.0, 30.0], [20.0, 20.0, 20.0, 20.0, 20.0, 30.0, 20.0], [30.0, 20.0, 30.0, 50.0, 10.0, 30.0, 20.0, 20.0, 20.0], [100.0, 100.0], [100.0, 100.0, 100.0, 100.0, 100.0], [100.0, 100.0, 100.0, 100.0]]

Answer 6

如果你不喜欢嵌套列表推导，你也可以使用map函数，

>>> from pprint import pprint

>>> l = l = [['40', '20', '10', '30'], ['20', '20', '20', '20', '20', '30', '20'], ['30', '20', '30', '50', '10', '30', '20', '20', '20'], ['100', '100'], ['100', '100', '100', '100', '100'], ['100', '100', '100', '100']] 

>>> pprint(l)
[['40', '20', '10', '30'],
['20', '20', '20', '20', '20', '30', '20'],
['30', '20', '30', '50', '10', '30', '20', '20', '20'],
['100', '100'],
['100', '100', '100', '100', '100'],
['100', '100', '100', '100']]

>>> float_l = [map(float, nested_list) for nested_list in l]

>>> pprint(float_l)
[[40.0, 20.0, 10.0, 30.0],
[20.0, 20.0, 20.0, 20.0, 20.0, 30.0, 20.0],
[30.0, 20.0, 30.0, 50.0, 10.0, 30.0, 20.0, 20.0, 20.0],
[100.0, 100.0],
[100.0, 100.0, 100.0, 100.0, 100.0],
[100.0, 100.0, 100.0, 100.0]]

Answer 7

我有一个类似的问题需要解决，所以我遇到了这个问题。我对安德鲁·克拉克和纳拉扬的答案进行了性能比较，我想分享一下。

两个答案之间的主要区别在于它们如何迭代内部列表。其中一个使用内置map，而另一个使用列表理解。 Map function has slight performance advantage to its equivalent list comprehension if it doesn't require the use lambdas。因此，在这个问题的上下文中，map应该比列表理解稍微好一些。

让我们做一个性能基准，看看它是否真的如此。我使用python版本3.5.0来执行所有这些测试。在第一组测试中，我希望每个列表中的元素保持 10 并且从 10-100,000

中改变列表的数量

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,10))]*10]"
>>> 100000 loops, best of 3: 15.2 usec per loop   
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,10))]*10]"
>>> 10000 loops, best of 3: 19.6 usec per loop 

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,10))]*100]"
>>> 100000 loops, best of 3: 15.2 usec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,10))]*100]"
>>> 10000 loops, best of 3: 19.6 usec per loop 

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,10))]*1000]"
>>> 1000 loops, best of 3: 1.43 msec per loop   
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,10))]*1000]"
>>> 100 loops, best of 3: 1.91 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,10))]*10000]"
>>> 100 loops, best of 3: 13.6 msec per loop   
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,10))]*10000]"
>>> 10 loops, best of 3: 19.1 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,10))]*100000]"
>>> 10 loops, best of 3: 164 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,10))]*100000]"
>>> 10 loops, best of 3: 216 msec per loop

在下一组测试中，我想将每个列表的元素数量增加到 100 。

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,100))]*10]"
>>> 10000 loops, best of 3: 110 usec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,100))]*10]"
>>> 10000 loops, best of 3: 151 usec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,100))]*100]"
>>> 1000 loops, best of 3: 1.11 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,100))]*100]"
>>> 1000 loops, best of 3: 1.5 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,100))]*1000]"
>>> 100 loops, best of 3: 11.2 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,100))]*1000]"
>>> 100 loops, best of 3: 16.7 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,100))]*10000]"
>>> 10 loops, best of 3: 134 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,100))]*10000]"
>>> 10 loops, best of 3: 171 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,100))]*100000]"
>>> 10 loops, best of 3: 1.32 sec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,100))]*100000]"
>>> 10 loops, best of 3: 1.7 sec per loop

让我们采取勇敢的步骤，将列表中的元素数量修改为 1000

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,1000))]*10]"
>>> 1000 loops, best of 3: 800 usec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,1000))]*10]"
>>> 1000 loops, best of 3: 1.16 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,1000))]*100]"
>>> 100 loops, best of 3: 8.26 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,1000))]*100]"
>>> 100 loops, best of 3: 11.7 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,1000))]*1000]"
>>> 10 loops, best of 3: 83.8 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,1000))]*1000]"
>>> 10 loops, best of 3: 118 msec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,1000))]*10000]"
>>> 10 loops, best of 3: 868 msec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,1000))]*10000]"
>>> 10 loops, best of 3: 1.23 sec per loop

>>> python -m timeit "[list(map(float,k)) for k in [list(range(0,1000))]*100000]"
>>> 10 loops, best of 3: 9.2 sec per loop
>>> python -m timeit "[[float(y) for y in x] for x in [list(range(0,1000))]*100000]"
>>> 10 loops, best of 3: 12.7 sec per loop

从这些测试中我们可以得出结论，map在这种情况下具有优于列表理解的性能优势。如果您尝试转换为int或str，这也适用。对于每个列表中元素较少的少量列表，差异可以忽略不计。对于每个列表包含更多元素的较大列表，可能希望使用map而不是列表理解，但这完全取决于应用程序的需求。

但是我个人认为列表理解比map更具可读性和惯用性。它是python中事实上的标准。通常人们使用列表理解比map更熟练和舒适（特别是初学者）。

Answer 8

是的，您可以使用以下代码执行此操作：

l = [[float(y) for y in x] for x in l]

Answer 9

这个问题可以在不使用for循环的情况下解决。单行代码就足够了。使用带有lambda函数的嵌套映射也可以在这里工作。

l = [['40', '20', '10', '30'], ['20', '20', '20', '20', '20', '30', '20'], ['30', '20', '30', '50', '10', '30', '20', '20', '20'], ['100', '100'], ['100', '100', '100', '100', '100'], ['100', '100', '100', '100']]

map(lambda x:map(lambda y:float(y),x),l)

输出列表如下：

[[40.0, 20.0, 10.0, 30.0], [20.0, 20.0, 20.0, 20.0, 20.0, 30.0, 20.0], [30.0, 20.0, 30.0, 50.0, 10.0, 30.0, 20.0, 20.0, 20.0], [100.0, 100.0], [100.0, 100.0, 100.0, 100.0, 100.0], [100.0, 100.0, 100.0, 100.0]]

Answer 10

在我看来，最好的方法是使用python的itertools包。

>>>import itertools
>>>l1 = [1,2,3]
>>>l2 = [10,20,30]
>>>[l*2 for l in itertools.chain(*[l1,l2])]
[2, 4, 6, 20, 40, 60]

Answer 11

是的，您可以执行以下操作。

[[float(y) for y in x] for x in l]

Answer 12

    deck = [] 
    for rank in ranks:
        for suit in suits:
            deck.append(('%s%s')%(rank, suit))

这可以使用列表理解来实现：

[deck.append((rank,suit)) for suit in suits for rank in ranks ]