Python字典：大小影響時間？

Question

讓我們說你有在字典中的一個關鍵VS在字典中乙1個十億鍵

算法上查找op是O（1）

然而，實際的時間（程序執行時間）來查找不同基於字典的大小？

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 

我會看到onekey_dur和manykeys_dur之間的時間差？

Answer 1

與小型和大型字典測試差不多相同：

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 

編輯：對於你來說，@ShadowRanger - 錯過查找是相當接近太：

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