了解嵌套列表理解 [英] Understanding nested list comprehension

问题描述

我想了解嵌套列表的理解. 下面，我列出了一个列表理解表达式及其for循环等效项.
我不知道我的理解是否正确.

例如，

[(min([row[i] for row in rows]),max([row[i] for row in rows])) 
for i in range(len(rows[0]))]

等效于

result=[]
for i in range(len(rows[0])):
  innerResult=[]
  for row in rows:
    innerResult.append(row[i])
  innerResult2=[]
  for row in rows:
    innerResult2.append(row[i])
  tuple=(min(innerResult), max(innerResult2))
  result.append(tuple)

我可以概括一下

[exp2([exp1 for x in xSet]) for y in ySet]

表格可以翻译为以下内容. (我希望我是对的)

result=[]
for y in ySet:
  innerResult =[]
  for x in xSet:
    innerResult.append(exp1)
  exp2Result = exp2(innerResult)
  result.append(exp2Result)

为简单起见，

[exp1 for x in xSet for y in ySet] 

等于

result=[] 
for x in xSet:
  for y in ySet: 
    result.append(exp1)

而

[[exp1 for x in xSet] for y in ySet]

等于

result=[]
for y in ySet:
  innerResult=[]
  for x in xSet:
    innerResult.append(exp1)
  result.append(innerResult)

我在与复杂列表的循环表达式等效的问题上也提出了类似的问题理解力
在了解表单的内部功能之后，给出的答案将重新构建表单.
我想知道它是如何系统地工作的，因此我可以将该概念应用于其他稍有不同的示例.

解决方案

简短的答案是:是的，您的理解是正确的.

唯一的问题是:通常在python代码中使用嵌套列表理解的方法是对多维数组进行操作.

一个典型的例子是当您对矩阵进行运算时:

>>> matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> [[el - 1 for el in row] for row in matrix]
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]

如您所见，嵌套"是通过对矩阵的每个维度进行操作来实现的.

在您提供的示例中，似乎ySet [不幸的名字是btw，如 zip 内置函数:

>>> zip(rows[0], rows[1])
[(1, 10), (2, 20), (3, 30)]

或为了最大程度地简洁和优雅:

>>> zip(*rows)
[(1, 10), (2, 20), (3, 30)]

HTH！

I want to understand nested list comprehension. Below, I listed a list comprehension expression and their for loop equivalent.
I wonder if my understanding is correct on those.

For example,

[(min([row[i] for row in rows]),max([row[i] for row in rows])) 
for i in range(len(rows[0]))]

is equivalent to

result=[]
for i in range(len(rows[0])):
  innerResult=[]
  for row in rows:
    innerResult.append(row[i])
  innerResult2=[]
  for row in rows:
    innerResult2.append(row[i])
  tuple=(min(innerResult), max(innerResult2))
  result.append(tuple)

If I may generalize, I guess

[exp2([exp1 for x in xSet]) for y in ySet]

form can be translated to the following. (I hope I'm correct on this)

result=[]
for y in ySet:
  innerResult =[]
  for x in xSet:
    innerResult.append(exp1)
  exp2Result = exp2(innerResult)
  result.append(exp2Result)

For simpler case,

[exp1 for x in xSet for y in ySet] 

is equal to

result=[] 
for x in xSet:
  for y in ySet: 
    result.append(exp1)

whereas,

[[exp1 for x in xSet] for y in ySet]

is equal to

result=[]
for y in ySet:
  innerResult=[]
  for x in xSet:
    innerResult.append(exp1)
  result.append(innerResult)

I asked a similar question on Equivalent for loop expression for complex list comprehension
The answers given there reconstruct the form after understanding what it does internally.
I'd like to know how it works systematically so I can apply the concept to other slightly varying examples.

解决方案

The short answer is: yes, you are correct in your understanding.

There's only a catch: the way you normally use nested list comprehension in python code is to operate on multidimensional arrays.

A typical example is when you operate on matrices:

>>> matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> [[el - 1 for el in row] for row in matrix]
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]

As you can see the "nesting" works by operating on each dimension of the matrix.

In the examples you provided, it seems that ySet [unfortunate name btw, as sets are one of the types provided with python] is just a generic counter, which makes a bit harder to follow what is going on under the hood.

As for your first example:

>>> rows = ([1, 2, 3], [10, 20, 30])
>>> [(min([row[i] for row in rows]),max([row[i] for row in rows])) for i in range(len(rows[0]))]
[(1, 10), (2, 20), (3, 30)]

You might wish to look into the zip built-in function:

>>> zip(rows[0], rows[1])
[(1, 10), (2, 20), (3, 30)]

or for maximum brevity and elegance:

>>> zip(*rows)
[(1, 10), (2, 20), (3, 30)]

HTH!

这篇关于了解嵌套列表理解的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！