python 數組迭代與“if in”的巨大性能差異

Question

下面的兩個代碼片段都檢查數組中是否存在元素，但第一種方法需要 < 100 毫秒，而第二種方法需要約 6 秒。

有誰知道為什么？

import numpy as np
import time

xs = np.random.randint(90000000, size=8000000)

start = time.monotonic()
is_present = -4 in xs

end = time.monotonic()

print( 'exec time:', round(end-start, 3) , 'sec ') // 100 milliseconds

start = time.monotonic()
for x in xs:
  if (x == -4):
    break

end = time.monotonic()

print( 'exec time:', round(end-start, 3) , 'sec ') // 6000 milliseconds ```

重新鏈接

Answer 1

numpy is specifically built to accelerate this kind of code, it is written in c with almost all of the python overhead removed, comparatively your second attempt is pure python so it takes much longer to loop through all the elements