运行总计的列表理解

发布于 2024-09-13 12:00:59 字数 752 浏览 4 评论 0原文

我想从数字列表中获取运行总计。

的数字顺序列表开始

a = range(20)

runningTotal = []
for n in range(len(a)):
    new = runningTotal[n-1] + a[n] if n > 0 else a[n]
    runningTotal.append(new)

# This one is a syntax error
# runningTotal = [a[n] for n in range(len(a)) if n == 0 else runningTotal[n-1] + a[n]]

for i in zip(a, runningTotal):
    print "{0:>3}{1:>5}".format(*i)

出于演示目的，我从使用 range产量

如您所见，我初始化一个空列表 []，然后 append()< /code> 在每个循环迭代中。有没有更优雅的方法，比如列表理解？

原文

I want to get a running total from a list of numbers.

For demo purposes, I start with a sequential list of numbers using range

a = range(20)

runningTotal = []
for n in range(len(a)):
    new = runningTotal[n-1] + a[n] if n > 0 else a[n]
    runningTotal.append(new)

# This one is a syntax error
# runningTotal = [a[n] for n in range(len(a)) if n == 0 else runningTotal[n-1] + a[n]]

for i in zip(a, runningTotal):
    print "{0:>3}{1:>5}".format(*i)

yields

As you can see, I initialize an empty list [], then append() in each loop iteration. Is there a more elegant way to this, like a list comprehension?

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烟柳画桥 2024-09-20 12:00:59

列表理解没有好的（干净的、可移植的）方式来引用它正在构建的列表。一种好的而优雅的方法可能是在生成器中完成这项工作：

def running_sum(a):
  tot = 0
  for item in a:
    tot += item
    yield tot

当然，要使用列表来获取它作为列表，请使用list(running_sum(a))。

A list comprehension has no good (clean, portable) way to refer to the very list it's building. One good and elegant approach might be to do the job in a generator:

def running_sum(a):
  tot = 0
  for item in a:
    tot += item
    yield tot

to get this as a list instead, of course, use list(running_sum(a)).

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￡冰雨忧蓝° 2024-09-20 12:00:59

如果您可以使用 numpy，它有一个名为 cumsum 的内置函数可以执行此操作。

import numpy as np
tot = np.cumsum(a)  # returns a np.ndarray
tot = list(tot)     # if you prefer a list

If you can use numpy, it has a built-in function named cumsum that does this.

import numpy as np
tot = np.cumsum(a)  # returns a np.ndarray
tot = list(tot)     # if you prefer a list

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疧_╮線 2024-09-20 12:00:59

我不确定“优雅”，但我认为以下内容更简单、更直观（以额外变量为代价）：

a = range(20)

runningTotal = []

total = 0
for n in a:
  total += n
  runningTotal.append(total)

做同样事情的功能方法是：

a = range(20)
runningTotal = reduce(lambda x, y: x+[x[-1]+y], a, [0])[1:]

……但这可读性要差得多/可维护等。

@Omnifarous 建议这应该改进为：

a = range(20)
runningTotal = reduce(lambda l, v: (l.append(l[-1] + v) or l), a, [0])

...但我仍然发现这不如我最初的建议那么容易理解。

请记住 Kernighan 的话：“调试的难度是最初编写代码的两倍。因此，如果您尽可能巧妙地编写代码，那么根据定义，您就不够聪明，无法调试它。”

I'm not sure about 'elegant', but I think the following is much simpler and more intuitive (at the cost of an extra variable):

a = range(20)

runningTotal = []

total = 0
for n in a:
  total += n
  runningTotal.append(total)

The functional way to do the same thing is:

a = range(20)
runningTotal = reduce(lambda x, y: x+[x[-1]+y], a, [0])[1:]

...but that's much less readable/maintainable, etc.

@Omnifarous suggests this should be improved to:

a = range(20)
runningTotal = reduce(lambda l, v: (l.append(l[-1] + v) or l), a, [0])

...but I still find that less immediately comprehensible than my initial suggestion.

Remember the words of Kernighan: "Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it."

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夜访吸血鬼 2024-09-20 12:00:59

使用 itertools.accumulate()。这是一个示例：

from itertools import accumulate

a = range(20)
runningTotals = list(accumulate(a))

for i in zip(a, runningTotals):
    print "{0:>3}{1:>5}".format(*i)

这只适用于 Python 3。在 Python 2 上，您可以使用 more 中的反向移植-itertools 包。

Use itertools.accumulate(). Here is an example:

from itertools import accumulate

a = range(20)
runningTotals = list(accumulate(a))

for i in zip(a, runningTotals):
    print "{0:>3}{1:>5}".format(*i)

This only works on Python 3. On Python 2 you can use the backport in the more-itertools package.

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素染倾城色 2024-09-20 12:00:59

这可以在 Python 中用两行代码实现。

使用默认参数就无需在外部维护 aux 变量，然后我们只需对列表进行映射即可。

def accumulate(x, l=[0]): l[0] += x; return l[0];
map(accumulate, range(20))

This can be implemented in 2 lines in Python.

Using a default parameter eliminates the need to maintain an aux variable outside, and then we just do a map to the list.

def accumulate(x, l=[0]): l[0] += x; return l[0];
map(accumulate, range(20))

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听你说爱我 2024-09-20 12:00:59

当我们对列表求和时，我们指定一个累加器（memo），然后遍历列表，将二元函数“x+y”应用于每个元素和累加器。从程序上看，这看起来像：

def mySum(list):
    memo = 0
    for e in list:
        memo = memo + e
    return memo

这是一种常见的模式，除了求和之外，对于其他事情也很有用——我们可以将其推广到任何二进制函数，我们将其作为参数提供，并且还让调用者指定一个初始值。这为我们提供了一个名为 reduce、foldl 或 inject^[1] 的函数：

def myReduce(function, list, initial):
    memo = initial
    for e in list:
        memo = function(memo, e)
    return memo

def mySum(list):
    return myReduce(lambda memo, e: memo + e, list, 0)

在 Python 2 中，< code>reduce 是一个内置函数，但在 Python 3 中它已被移至 functools 模块：

from functools import reduce

我们可以使用 reduce 做各种很酷的事情取决于我们作为第一个参数提供的函数。如果我们用“列表串联”替换“sum”，用“空列表”替换“零”，我们就得到了（浅）copy函数：

def myCopy(list):
    return reduce(lambda memo, e: memo + [e], list, [])

myCopy(range(10))
> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

如果我们添加一个transform函数作为 copy 的另一个参数，并在连接之前应用它，我们得到 map：

def myMap(transform, list):
    return reduce(lambda memo, e: memo + [transform(e)], list, [])

myMap(lambda x: x*2, range(10))
> [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

如果我们添加一个采用 e 的 predicate 函数作为参数并返回一个布尔值，并用它来决定是否连接，我们得到filter：

def myFilter(predicate, list):
    return reduce(lambda memo, e: memo + [e] if predicate(e) else memo, list, [])

myFilter(lambda x: x%2==0, range(10))
> [0, 2, 4, 6, 8]

map和filter 是编写列表推导式的一种奇特方式 - 我们也可以说 [x*2 for x in range(10)] 或 [x for x in range(10)如果x%2==0]。 reduce 没有相应的列表理解语法，因为 reduce 根本不需要返回列表（正如我们之前在 sum 中看到的那样），Python 也恰好将其作为内置函数提供）。

事实证明，对于计算运行总和，reduce 的列表构建能力正是我们想要的，并且可能是解决此问题的最优雅的方法，尽管它享有盛誉（与 lambda）作为一种非Pythonic的陈词滥调。在运行时留下旧值副本的 reduce 版本称为 reductions 或 scanl^[1]，看起来像这样：

def reductions(function, list, initial):
    return reduce(lambda memo, e: memo + [function(memo[-1], e)], list, [initial])

装备齐全，我们现在可以定义：

def running_sum(list):
    first, rest = list[0], list[1:]
    return reductions(lambda memo, e: memo + e, rest, first)

running_sum(range(10))
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

虽然概念上很优雅，但这种精确的方法在 Python 实践中表现不佳。因为 Python 的 list.append() 会就地改变列表但不会返回它，所以我们无法在 lambda 中有效地使用它，而必须使用 +改为运算符。这会构造一个全新的列表，所花费的时间与到目前为止累积的列表的长度成正比（即 O(n) 操作）。由于执行此操作时我们已经处于 reduce 的 O(n) for 循环中，因此整体时间复杂度复合为 O(n²）。

在 Ruby^[2] 这样的语言中，array.push e 返回变异的 array，等效的运行时间为 O(n) 时间：

class Array
  def reductions(initial, &proc)
    self.reduce [initial] do |memo, e|
      memo.push proc.call(memo.last, e)
    end
  end
end

def running_sum(enumerable)
  first, rest = enumerable.first, enumerable.drop(1)
  rest.reductions(first, &:+)
end

running_sum (0...10)
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

在 JavaScript^[2] 中也是如此，其 array.push(e) 返回 e （不是 array），但是其匿名函数允许我们包含多个语句，我们可以使用这些语句来单独指定返回值：

function reductions(array, callback, initial) {
    return array.reduce(function(memo, e) {
        memo.push(callback(memo[memo.length - 1], e));
        return memo;
    }, [initial]);
}

function runningSum(array) {
    var first = array[0], rest = array.slice(1);
    return reductions(rest, function(memo, e) {
        return x + y;
    }, first);
}

function range(start, end) {
    return(Array.apply(null, Array(end-start)).map(function(e, i) {
        return start + i;
    }
}

runningSum(range(0, 10));
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

那么，我们如何解决这个问题，同时保留我们只需传递的 reductions 函数的概念简单性>lambda x, y: x + y 为了创建运行求和函数？让我们按程序重写reductions。我们可以解决意外二次问题，并且在我们解决这个问题时，将结果列表预先分配给避免堆抖动^[3]：

def reductions(function, list, initial):
    result = [None] * len(list)
    result[0] = initial
    for i in range(len(list)):
        result[i] = function(result[i-1], list[i])
    return result

def running_sum(list):
    first, rest = list[0], list[1:]
    return reductions(lambda memo, e: memo + e, rest, first)

running_sum(range(0,10))
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

这对我来说是最佳点：O(n) 性能，优化的过程代码隐藏在一个有意义的名称下，下次可以重复使用您需要编写一个将中间值累积到列表中的函数。

名称 reduce/reductions 来自 LISP 传统，foldl/scanl 来自 ML 传统，而 >inject 来自 Smalltalk 传统。
Python 的 List 和 Ruby 的 Array 都是自动调整大小的数据结构的实现，称为“动态数组”（或 C++ 中的 std::vector））。 JavaScript 的 Array 有点巴洛克风格，但只要您不分配给越界索引或改变 Array.length，其行为就相同。
每当列表的长度超过 2 的幂时，在 Python 运行时中形成列表后备存储的动态数组就会调整自身大小。调整列表大小意味着在堆上分配一个两倍于旧列表大小的新列表，将旧列表的内容复制到新列表中，并将旧列表的内存返回给系统。这是一个 O(n) 操作，但由于随着列表变得越来越大，它发生的频率越来越低，因此在平均情况下，追加到列表的时间复杂度为 O(1)。然而，旧列表留下的“洞”有时可能很难回收，这取决于它在堆中的位置。即使使用垃圾收集和强大的内存分配器，预分配已知大小的数组也可以节省底层系统的一些工作。在没有操作系统的嵌入式环境中，这种微观管理变得非常重要。

When we take the sum of a list, we designate an accumulator (memo) and then walk through the list, applying the binary function "x+y" to each element and the accumulator. Procedurally, this looks like:

def mySum(list):
    memo = 0
    for e in list:
        memo = memo + e
    return memo

This is a common pattern, and useful for things other than taking sums — we can generalize it to any binary function, which we'll supply as a parameter, and also let the caller specify an initial value. This gives us a function known as reduce, foldl, or inject^[1]:

def myReduce(function, list, initial):
    memo = initial
    for e in list:
        memo = function(memo, e)
    return memo

def mySum(list):
    return myReduce(lambda memo, e: memo + e, list, 0)

In Python 2, reduce was a built-in function, but in Python 3 it's been moved to the functools module:

from functools import reduce

We can do all kinds of cool stuff with reduce depending on the function we supply as its the first argument. If we replace "sum" with "list concatenation", and "zero" with "empty list", we get the (shallow) copy function:

def myCopy(list):
    return reduce(lambda memo, e: memo + [e], list, [])

myCopy(range(10))
> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

If we add a transform function as another parameter to copy, and apply it before concatenating, we get map:

def myMap(transform, list):
    return reduce(lambda memo, e: memo + [transform(e)], list, [])

myMap(lambda x: x*2, range(10))
> [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

If we add a predicate function that takes e as a parameter and returns a boolean, and use it to decide whether or not to concatenate, we get filter:

def myFilter(predicate, list):
    return reduce(lambda memo, e: memo + [e] if predicate(e) else memo, list, [])

myFilter(lambda x: x%2==0, range(10))
> [0, 2, 4, 6, 8]

map and filter are sort of unfancy ways of writing list comprehensions — we could also have said [x*2 for x in range(10)] or [x for x in range(10) if x%2==0]. There's no corresponding list comprehension syntax for reduce, because reduce isn't required to return a list at all (as we saw with sum, earlier, which Python also happens to offer as a built-in function).

It turns out that for computing a running sum, the list-building abilities of reduce are exactly what we want, and probably the most elegant way to solve this problem, despite its reputation (along with lambda) as something of an un-pythonic shibboleth. The version of reduce that leaves behind copies of its old values as it runs is called reductions or scanl^[1], and it looks like this:

def reductions(function, list, initial):
    return reduce(lambda memo, e: memo + [function(memo[-1], e)], list, [initial])

So equipped, we can now define:

def running_sum(list):
    first, rest = list[0], list[1:]
    return reductions(lambda memo, e: memo + e, rest, first)

running_sum(range(10))
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

While conceptually elegant, this precise approach fares poorly in practice with Python. Because Python's list.append() mutates a list in place but doesn't return it, we can't use it effectively in a lambda, and have to use the + operator instead. This constructs a whole new list, which takes time proportional to the length of the accumulated list so far (that is, an O(n) operation). Since we're already inside the O(n) for loop of reduce when we do this, the overall time complexity compounds to O(n²).

In a language like Ruby^[2], where array.push e returns the mutated array, the equivalent runs in O(n) time:

class Array
  def reductions(initial, &proc)
    self.reduce [initial] do |memo, e|
      memo.push proc.call(memo.last, e)
    end
  end
end

def running_sum(enumerable)
  first, rest = enumerable.first, enumerable.drop(1)
  rest.reductions(first, &:+)
end

running_sum (0...10)
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

same in JavaScript^[2], whose array.push(e) returns e (not array), but whose anonymous functions allow us to include multiple statements, which we can use to separately specify a return value:

function reductions(array, callback, initial) {
    return array.reduce(function(memo, e) {
        memo.push(callback(memo[memo.length - 1], e));
        return memo;
    }, [initial]);
}

function runningSum(array) {
    var first = array[0], rest = array.slice(1);
    return reductions(rest, function(memo, e) {
        return x + y;
    }, first);
}

function range(start, end) {
    return(Array.apply(null, Array(end-start)).map(function(e, i) {
        return start + i;
    }
}

runningSum(range(0, 10));
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

So, how can we solve this while retaining the conceptual simplicity of a reductions function that we just pass lambda x, y: x + y to in order to create the running sum function? Let's rewrite reductions procedurally. We can fix the accidentally quadratic problem, and while we're at it, pre-allocate the result list to avoid heap thrashing^[3]:

def reductions(function, list, initial):
    result = [None] * len(list)
    result[0] = initial
    for i in range(len(list)):
        result[i] = function(result[i-1], list[i])
    return result

def running_sum(list):
    first, rest = list[0], list[1:]
    return reductions(lambda memo, e: memo + e, rest, first)

running_sum(range(0,10))
> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

This is the sweet spot for me: O(n) performance, and the optimized procedural code is tucked away under a meaningful name where it can be re-used the next time you need to write a function that accumulates intermediate values into a list.

The names reduce/reductions come from the LISP tradition, foldl/scanl from the ML tradition, and inject from the Smalltalk tradition.
Python's List and Ruby's Array are both implementations of an automatically resizing data structure known as a "dynamic array" (or std::vector in C++). JavaScript's Array is a little more baroque, but behaves identically provided you don't assign to out of bounds indices or mutate Array.length.
The dynamic array that forms the backing store of the list in the Python runtime will resize itself every time the list's length crosses a power of two. Resizing a list means allocating a new list on the heap of twice the size of the old one, copying the contents of the old list into the new one, and returning the old list's memory to the system. This is an O(n) operation, but because it happens less and less frequently as the list grows larger and larger, the time complexity of appending to a list works out to O(1) in the average case. However, the "hole" left by the old list can sometimes be difficult to recycle, depending on its position in the heap. Even with garbage collection and a robust memory allocator, pre-allocating an array of known size can save the underlying systems some work. In an embedded environment without the benefit of an OS, this kind of micro-management becomes very important.

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奈何桥上唱咆哮 2024-09-20 12:00:59

从 Python 3.8 开始，并引入赋值表达式 (PEP 572)（:= 运算符），我们可以在列表理解中使用并递增变量：

# items = range(7)
total = 0
[(x, total := total + x) for x in items]
# [(0, 0), (1, 1), (2, 3), (3, 6), (4, 10), (5, 15), (6, 21)]

此：

将变量 total 初始化为 0< /code> 代表
每个项目的运行总和，这两者：
- 通过赋值表达式将total增加当前循环项（total :=total + x）
- 同时返回total的新值作为生成的映射元组的一部分

Starting Python 3.8, and the introduction of assignment expressions (PEP 572) (:= operator), we can use and increment a variable within a list comprehension:

# items = range(7)
total = 0
[(x, total := total + x) for x in items]
# [(0, 0), (1, 1), (2, 3), (3, 6), (4, 10), (5, 15), (6, 21)]

This:

Initializes a variable total to 0 which symbolizes the running sum
For each item, this both:
- increments total by the current looped item (total := total + x) via an assignment expression
- and at the same time returns the new value of total as part of the produced mapped tuple

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风和你 2024-09-20 12:00:59

我想做同样的事情来生成可以使用 bisect_left 的累积频率 - 这就是我生成列表的方式；

[ sum( a[:x] ) for x in range( 1, len(a)+1 ) ]

I wanted to do the same thing to generate cumulative frequencies that I could use bisect_left over - this is the way I've generated the list;

[ sum( a[:x] ) for x in range( 1, len(a)+1 ) ]

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旧情别恋 2024-09-20 12:00:59

这是一个线性时间解决方案：

list(reduce(lambda (c,s), a: (chain(c,[s+a]), s+a), l,(iter([]),0))[0])

示例：

l = range(10)
list(reduce(lambda (c,s), a: (chain(c,[s+a]), s+a), l,(iter([]),0))[0])
>>> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

简而言之，reduce 遍历列表累加总和并构造一个列表。最终的x[0] 返回列表，x[1] 将是运行总值。

Here's a linear time solution one liner:

list(reduce(lambda (c,s), a: (chain(c,[s+a]), s+a), l,(iter([]),0))[0])

Example:

l = range(10)
list(reduce(lambda (c,s), a: (chain(c,[s+a]), s+a), l,(iter([]),0))[0])
>>> [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]

In short, the reduce goes over the list accumulating sum and constructing an list. The final x[0] returns the list, x[1] would be the running total value.

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鹊巢 2024-09-20 12:00:59

又是一句单行话，线性时空。

def runningSum(a):
    return reduce(lambda l, x: l.append(l[-1]+x) or l if l else [x], a, None)

我在这里强调线性空间，因为我在其他建议的答案中看到的大多数单行代码 --- 那些基于模式 list + [sum] 或使用 chain code> 迭代器 --- 生成 O(n) 列表或生成器，并对垃圾收集器施加很大压力，以至于与此相比，它们的性能非常差。

Another one-liner, in linear time and space.

def runningSum(a):
    return reduce(lambda l, x: l.append(l[-1]+x) or l if l else [x], a, None)

I'm stressing linear space here, because most of the one-liners I saw in the other proposed answers --- those based on the pattern list + [sum] or using chain iterators --- generate O(n) lists or generators and stress the garbage collector so much that they perform very poorly, in comparison to this.

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撩起发的微风 2024-09-20 12:00:59

我会为此使用协程：

def runningTotal():
    accum = 0
    yield None
    while True:
        accum += yield accum

tot = runningTotal()
next(tot)
running_total = [tot.send(i) for i in xrange(N)]

I would use a coroutine for this:

def runningTotal():
    accum = 0
    yield None
    while True:
        accum += yield accum

tot = runningTotal()
next(tot)
running_total = [tot.send(i) for i in xrange(N)]

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小猫一只 2024-09-20 12:00:59

您正在寻找两件事：折叠（减少）和一个有趣的函数，该函数保留另一个函数的结果列表，我将其称为运行。我制作了带有和不带有初始参数的版本；不管怎样，这些都需要用初始的 [] 来减少。

def last_or_default(list, default):
    if len(list) > 0:
        return list[-1]
    return default

def initial_or_apply(list, f, y):
    if list == []:
        return [y]
    return list + [f(list[-1], y)]

def running_initial(f, initial):
    return (lambda x, y: x + [f(last_or_default(x,initial), y)])

def running(f):
    return (lambda x, y: initial_or_apply(x, f, y))

totaler = lambda x, y: x + y
running_totaler = running(totaler)
running_running_totaler = running_initial(running_totaler, [])

data = range(0,20)
running_total = reduce(running_totaler, data, [])
running_running_total = reduce(running_running_totaler, data, [])

for i in zip(data, running_total, running_running_total):
    print "{0:>3}{1:>4}{2:>83}".format(*i)

由于 + 运算符，在非常大的列表上这些将花费很长时间。在函数式语言中，如果正确完成，此列表构造将是 O(n)。

以下是输出的前几行：

0   0                      [0]
1   1                   [0, 1]
2   3                [0, 1, 3]
3   6             [0, 1, 3, 6]
4  10         [0, 1, 3, 6, 10]
5  15     [0, 1, 3, 6, 10, 15]
6  21 [0, 1, 3, 6, 10, 15, 21]

You are looking for two things: fold (reduce) and a funny function that keeps a list of the results of another function, which I have called running. I made versions both with and without an initial parameter; either way these need to go to reduce with an initial [].

def last_or_default(list, default):
    if len(list) > 0:
        return list[-1]
    return default

def initial_or_apply(list, f, y):
    if list == []:
        return [y]
    return list + [f(list[-1], y)]

def running_initial(f, initial):
    return (lambda x, y: x + [f(last_or_default(x,initial), y)])

def running(f):
    return (lambda x, y: initial_or_apply(x, f, y))

totaler = lambda x, y: x + y
running_totaler = running(totaler)
running_running_totaler = running_initial(running_totaler, [])

data = range(0,20)
running_total = reduce(running_totaler, data, [])
running_running_total = reduce(running_running_totaler, data, [])

for i in zip(data, running_total, running_running_total):
    print "{0:>3}{1:>4}{2:>83}".format(*i)

These will take a long time on really large lists due to the + operator. In a functional language, if done correctly, this list construction would be O(n).

Here are the first few lines of output:

0   0                      [0]
1   1                   [0, 1]
2   3                [0, 1, 3]
3   6             [0, 1, 3, 6]
4  10         [0, 1, 3, 6, 10]
5  15     [0, 1, 3, 6, 10, 15]
6  21 [0, 1, 3, 6, 10, 15, 21]

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无力看清 2024-09-20 12:00:59

这是低效的，因为它从一开始就每次都这样做，但可能是：

a = range(20)
runtot=[sum(a[:i+1]) for i,item in enumerate(a)]
for line in zip(a,runtot):
    print line

This is inefficient as it does it every time from beginning but possible it is:

a = range(20)
runtot=[sum(a[:i+1]) for i,item in enumerate(a)]
for line in zip(a,runtot):
    print line

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千里故人稀 2024-09-20 12:00:59

在 Python 3.8 及更高版本中，您现在可以使用 walrus 运算符

xs = range(20)
total = 0
run = [(total := total + d) for d in xs]

with Python 3.8 and above you can now use walrus operator

xs = range(20)
total = 0
run = [(total := total + d) for d in xs]

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