是否有更多pythonic方式编写以下最大值函数：

>>> def greatest_orig(values):
...     value_generator = (v for k,v in values)
...     max_value = max(value_generator)
...     return (k for k,v in values if v == max_value)
... 
>>> def greatest_max_key(values):
...     max_value = max(values,key=itemgetter(1))[1]
...     return (k for k,v in values if v == max_value)
... 
>>> sample_data = tuple(('id' + str(i),random.randrange(0,1000)) for i in range(10000))
>>> list(greatest_orig(sample_data)) == list(greatest_max_key(sample_data))
True
>>> %timeit list(greatest_orig(sample_data))
1000 loops,best of 3: 1.67 ms per loop
>>> %timeit list(greatest_max_key(sample_data))
1000 loops,best of 3: 1.74 ms per loop

当然,如果你不喜欢将你的生成器分配给一个名字,你可以直接将生成器传递给max – 比max更可读(values,key = itemgetter(1))[1],恕我直言：

>>> def greatest_max_iter(values):
...     max_value = max((v for k,v in values))
...     return (k for k,v in values if v == max_value)
...                                                
>>> list(greatest_orig(sample_data)) == list(greatest_max_iter(sample_data))
True
>>> %timeit list(greatest_max_iter(sample_data))
1000 loops,best of 3: 1.67 ms per loop

Python允许您在执行以下操作时省略外部的parens：

>>> def greatest_max_iter(values):
...     max_value = max(v for k,v in values)
...     return (k for k,v in values if v == max_value)
...

但由于我不明白的原因,这样做会稍微慢一些：

>>> %timeit list(greatest_max_iter(sample_data))
1000 loops,best of 3: 1.69 ms per loop

这些都是真正的微观优化,不太重要.但我认为可读性优于max(v为k,v为值)或max((v为k,v为值))超过max(values,key = itemgetter(1))[1].