Python 字典 : the size affects timing?

Question

假设您在字典 A 中有一个键，而在字典 B 中有 10 亿个键

从算法上讲，查找操作是 O(1)

但是，根据dict的大小，实际查找的时间(程序执行时间)不同？

onekey_stime = time.time()
print one_key_dict.get('firstkey')
onekey_dur = time.time() - onekey_stime

manykeys_stime = time.time()
print manykeys_dict.get('randomkey')
manykeys_dur = time.time() - manykey_stime

我会看到 onekey_dur 和 manykeys_dur 之间有任何时间差异吗？

Answer 1

在使用小型和大型 dict 的测试中几乎相同:

In [31]: random_key = lambda: ''.join(np.random.choice(list(string.ascii_letters), 20))

In [32]: few_keys = {random_key(): np.random.random() for _ in xrange(100)}

In [33]: many_keys = {random_key(): np.random.random() for _ in xrange(1000000)}

In [34]: few_lookups = np.random.choice(few_keys.keys(), 50)

In [35]: many_lookups = np.random.choice(many_keys.keys(), 50)

In [36]: %timeit [few_keys[k] for k in few_lookups]
100000 loops, best of 3: 6.25 µs per loop

In [37]: %timeit [many_keys[k] for k in many_lookups]
100000 loops, best of 3: 7.01 µs per loop

编辑:对你来说，@ShadowRanger——错过的查找也非常接近:

In [38]: %timeit [few_keys.get(k) for k in many_lookups]
100000 loops, best of 3: 7.99 µs per loop

In [39]: %timeit [many_keys.get(k) for k in few_lookups]
100000 loops, best of 3: 8.78 µs per loop