列表推导式是 Python 3 中“列表（生成器表达式）”的语法糖吗？

在 Python 3 中，列表理解是一个简单的语法糖，用于输入到生成器表达式中list功能？

例如是下面的代码：

squares = [x**2 for x in range(1000)]

实际上在后台转换成以下内容？

squares = list(x**2 for x in range(1000))

我知道输出是相同的，Python 3 修复了列表推导式对周围名称空间的令人惊讶的副作用，但就 CPython 解释器在幕后的作用而言，前者是否转换为后者，或者有什么区别代码如何执行？

背景

我在评论部分发现了这种等同的说法这个问题 https://stackoverflow.com/questions/47789/generator-expressions-vs-list-comprehension，并且快速谷歌搜索显示了同样的说法here http://python3porting.com/improving.html.

文中也有提到这一点Python 3.0 文档中的新增功能 https://docs.python.org/3/whatsnew/3.0.html#changed-syntax，但措辞有些模糊：

另请注意，列表推导式具有不同的语义：它们更接近于 list() 构造函数内的生成器表达式的语法糖，特别是循环控制变量不再泄漏到周围的作用域中。

两者的工作方式不同。列表理解版本利用特殊的字节码LIST_APPEND https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Python/ceval.c#L1817哪个调用PyList_Append https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Objects/listobject.c#L319直接为我们服务。因此它避免了属性查找list.append以及 Python 级别的函数调用。

>>> def func_lc():
    [x**2 for x in y]
...
>>> dis.dis(func_lc)
  2           0 LOAD_CONST               1 (<code object <listcomp> at 0x10d3c6780, file "<ipython-input-42-ead395105775>", line 2>)
              3 LOAD_CONST               2 ('func_lc.<locals>.<listcomp>')
              6 MAKE_FUNCTION            0
              9 LOAD_GLOBAL              0 (y)
             12 GET_ITER
             13 CALL_FUNCTION            1 (1 positional, 0 keyword pair)
             16 POP_TOP
             17 LOAD_CONST               0 (None)
             20 RETURN_VALUE

>>> lc_object = list(dis.get_instructions(func_lc))[0].argval
>>> lc_object
<code object <listcomp> at 0x10d3c6780, file "<ipython-input-42-ead395105775>", line 2>
>>> dis.dis(lc_object)
  2           0 BUILD_LIST               0
              3 LOAD_FAST                0 (.0)
        >>    6 FOR_ITER                16 (to 25)
              9 STORE_FAST               1 (x)
             12 LOAD_FAST                1 (x)
             15 LOAD_CONST               0 (2)
             18 BINARY_POWER
             19 LIST_APPEND              2
             22 JUMP_ABSOLUTE            6
        >>   25 RETURN_VALUE

另一方面list()version 只是将生成器对象传递给列表__init__ https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Objects/listobject.c#L2697然后调用它的方法extend https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Objects/listobject.c#L853内部方法。由于对象不是列表或元组，因此 CPython首先获取它的迭代器 https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Objects/listobject.c#L904然后简单地将项目添加到列表中，直到迭代器已耗尽 https://github.com/python/cpython/blob/3549ca313a6103a3adb281ef3a849298b7d7f72c/Objects/listobject.c#L932:

>>> def func_ge():
    list(x**2 for x in y)
...
>>> dis.dis(func_ge)
  2           0 LOAD_GLOBAL              0 (list)
              3 LOAD_CONST               1 (<code object <genexpr> at 0x10cde6ae0, file "<ipython-input-41-f9a53483f10a>", line 2>)
              6 LOAD_CONST               2 ('func_ge.<locals>.<genexpr>')
              9 MAKE_FUNCTION            0
             12 LOAD_GLOBAL              1 (y)
             15 GET_ITER
             16 CALL_FUNCTION            1 (1 positional, 0 keyword pair)
             19 CALL_FUNCTION            1 (1 positional, 0 keyword pair)
             22 POP_TOP
             23 LOAD_CONST               0 (None)
             26 RETURN_VALUE
>>> ge_object = list(dis.get_instructions(func_ge))[1].argval
>>> ge_object
<code object <genexpr> at 0x10cde6ae0, file "<ipython-input-41-f9a53483f10a>", line 2>
>>> dis.dis(ge_object)
  2           0 LOAD_FAST                0 (.0)
        >>    3 FOR_ITER                15 (to 21)
              6 STORE_FAST               1 (x)
              9 LOAD_FAST                1 (x)
             12 LOAD_CONST               0 (2)
             15 BINARY_POWER
             16 YIELD_VALUE
             17 POP_TOP
             18 JUMP_ABSOLUTE            3
        >>   21 LOAD_CONST               1 (None)
             24 RETURN_VALUE
>>>

时序比较：

>>> %timeit [x**2 for x in range(10**6)]
1 loops, best of 3: 453 ms per loop
>>> %timeit list(x**2 for x in range(10**6))
1 loops, best of 3: 478 ms per loop
>>> %%timeit
out = []
for x in range(10**6):
    out.append(x**2)
...
1 loops, best of 3: 510 ms per loop

由于属性查找速度慢，正常循环会稍微慢一些。缓存它并再次进行。

>>> %%timeit
out = [];append=out.append
for x in range(10**6):
    append(x**2)
...
1 loops, best of 3: 467 ms per loop

除了列表理解不再泄漏变量这一事实之外，还有一个区别是这样的东西不再有效：

>>> [x**2 for x in 1, 2, 3] # Python 2
[1, 4, 9]
>>> [x**2 for x in 1, 2, 3] # Python 3
  File "<ipython-input-69-bea9540dd1d6>", line 1
    [x**2 for x in 1, 2, 3]
                    ^
SyntaxError: invalid syntax

>>> [x**2 for x in (1, 2, 3)] # Add parenthesis
[1, 4, 9]
>>> for x in 1, 2, 3: # Python 3: For normal loops it still works
    print(x**2)
...
1
4
9