python性能優化

1。去除沒必要要的顯式for循環，使用向量化計算。

 1 import time
 2 import numpy as np
 3 
 4 
 5 def for_time():
 6     """Make a array, len = 1000000, use for loop add one."""
 7     start = time.time()
 8     list_data = np.arange(0, 10000000, 1)
 9     for i in range(1000000):
10         list_data[i] += 1
11     print 'for loop used time: ', time.time() - start
12 
13 
14 def vector_time():
15     """make a array, use vector calculation add one."""
16     start = time.time()
17     list_data = np.arange(0, 10000000, 1)
18     list_data += 1
19     print 'vector calculation used time: ', time.time() - start
20 
21 
22 if __name__ == '__main__':
23     for_time()
24     vector_time()

for loop used time:  0.359999895096
vector calculation used time:  0.0160000324249

 

2. 使用多進程，開核。

1 import multiprocessing
2 
3 
4 def use_pool(func, args):
5     pool = multiprocessing.Pool(processes=2)
6     res = pool.map(func, args)
7     pool.close()
8     pool.join()
9     return res

3.使用sklearn.extenals.joblib 擴展庫

1 from sklearn.externals.joblib import Parallel, delayed
2 
3 
4 def parallel(func, arg):
5     Parallel(-1)(delayed(func)(i) for i in arg)

4. 使用bottleneck庫。

該庫基於Cpython實現，着眼於高性能。