考慮一個問題：一個python的字典，有1000萬個key-value對，新插入1000對鍵值對，怎麼速度才最快

自己測試了一部分程式碼，慢速的要300秒，加速的只要0.3秒，原因是慢速的程式碼每次查詢非常費時，

if k in C14.keys()可能是這句話的問題，
改進後使用

defaultdict（int）方法提速！不要用dict()初始化方法了...

原始程式碼：極其慢（尤其是原始字典很大的時候）

#test slower code
import pandas as pd
import pickle
from collections import Counter
import os
from tqdm import
 
 tqdm
import time
from collections import defaultdict

C14 = dict() #注意這裡沒有用defaultdict
for i in tqdm(range(10000000)):
    C14[i] = i

print("start processing test data:")
s_time = time.time()


data = pd.read_csv('../../test.gz')
print("read test.gz over")

print("start to process C14:")
s_tt = time.time()

C14_list 
 
= data['C14'].values  #data是dataframe格式，data['C14'].values相當於一個list，比如[42,523,23,24,3,4,1,5,3]
for k,v in tqdm(Counter(C14_list).items()):

　　if k in C14.keys():  #判斷所消耗的時間很長
         C14[k] += v
　　else:
         C14[k] = v
        
e_tt = time.time()
print("C14 over,cost time:{} seconds".format(e_tt-s_tt))
            
    

e_time 
 
= time.time()
print("test data processing over, cost {} minutes".format((e_time-s_time)/60))

改進後的程式碼：極快

#test code
import pandas as pd
import pickle
from collections import Counter
import os
from tqdm import tqdm
import time
from collections import defaultdict

C14 = defaultdict(int)   #使用python的defaultdict方法，意思是，如果key[value]的value不存在時，預設value值是int的0
for i in tqdm(range(10000000)):
    C14[i] = i

print("start processing test data:")
s_time = time.time()

data = pd.read_csv('../../test.gz')
print("read test.gz over")

print("start to process C14:")
s_tt = time.time()

C14_list = data['C14'].values
for k,v in tqdm(Counter(C14_list).items()):
    C14[k] += v
#下面四行話可以全部註釋掉了
     #if k in C14.keys():  
         #C14[k] += v
     #else:
         #C14[k] = v
        
e_tt = time.time()
print("C14 over,cost time:{} seconds".format(e_tt-s_tt))
            
    
e_time = time.time()
print("test data processing over, cost {} minutes".format((e_time-s_time)/60))