当仅在attrgetter('attributename')和之间lambda o: o.attributename作为排序键进行选择时，则使用attrgetter()是两者中 较快的 选项。
请记住，在排序之前，键函数仅对列表中的每个元素应用一次，因此，为了进行比较，我们可以在时间试用中直接使用它们：
>>> from timeit import Timer
>>> from random import randint
>>> from dataclasses import dataclass, field
>>> @dataclass
... class Foo:
... bar: int = field(default_factory=lambda: randint(1, 10**6))
...
>>> testdata = [Foo() for _ in range(1000)]
>>> def test_function(objects, key):
... [key(o) for o in objects]
...
>>> stmt = 't(testdata, key)'
>>> setup = 'from __main__ import test_function as t, testdata; '
>>> tests = {
... 'lambda': setup + 'key=lambda o: o.bar',
... 'attrgetter': setup + 'from operator import attrgetter; key=attrgetter("bar")'
... }
>>> for name, tsetup in tests.items():
... count, total = Timer(stmt, tsetup).autorange()
... print(f"{name:>10}: {total / count * 10 ** 6:7.3f} microseconds ({count} repetitions)")
...
lambda: 130.495 microseconds (2000 repetitions)
attrgetter: 92.850 microseconds (5000 repetitions)
因此，应用attrgetter('bar')1000次大约比a快40μs lambda。这是因为调用 Python 函数具有一定的开销，而不是调用诸如产生的原生函数attrgetter()。
这种速度优势也转化为更快的排序：
>>> def test_function(objects, key):
... sorted(objects, key=key)
...
>>> for name, tsetup in tests.items():
... count, total = Timer(stmt, tsetup).autorange()
... print(f"{name:>10}: {total / count * 10 ** 6:7.3f} microseconds ({count} repetitions)")
...
lambda: 218.715 microseconds (1000 repetitions)
attrgetter: 169.064 microseconds (2000 repetitions)
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