使用 print in 循环会减慢循环速度

Using print in a loop slows down the loop. Printing something ^{(I tried with Hello!)} 100 times take ~2 sec, without it, it takes 0.0 second. I accurately captured the time using module time. Here is a loop that prints in it and shows the time taken at the end:

import time

t = time.time()
for _ in range(100):
    print("Hello! ",end = "")

print("\n",time.time()-t)

输出：

Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! Hello! 
 1.9912450313568115

这是另一个循环，仅将字符串添加到变量中需要 0.0 秒.

import time

t = time.time()
output = ""
for _ in range(100):
    output += "Hello! "

print(time.time()-t)

输出：

0.0

我尝试添加更多操作，但仍然需要0.0秒。例子：

import time

t = time.time()
output,num,count,abc = "",0,30,"H"
for _ in range(100):
    output += "Hello! "
    num += 10000
    count += 10000000
    abc += "Hello Guys!"

print(time.time()-t)

为什么print减慢循环速度，如何克服这个问题？

print() 会减慢你的循环速度。这是因为每次调用 print() 时都会进行系统调用和 IO 操作。系统调用会消耗大量的CPU周期并涉及CPU上下文切换。 IO 操作会阻塞进程的主线程一段时间。

因此，显而易见的解决方案是消除或减少 print() 调用的次数。

如果您确实需要一种机制来了解循环的当前迭代次数，那么您可以使用多线程来实现这一点。但仍然可能涉及系统调用和线程上下文切换。但我想它仍然比 print() 更快。

诀窍是在后台线程中执行循环，同时在主线程中打印进度。这样，调用 print() 时的 IO 操作就不会阻塞运行循环的线程。为了进一步优化它，您可以仅在用户按下某个键时打印进度。

import threading

lock = threading.Lock()
count = 0

def your_method_with_a_loop():
    global count
    with open("f.txt","w") as f:
        for q in range(1000000):
            f.write(str(q)+"\n")
            #your_logic_here
            #....
            lock.acquire()
            count = count + 1
            lock.release()

#Run the loop in a background thread
t1 = threading.Thread(target=your_method_with_a_loop)
t1.start()

while t1.isAlive():
    print("Press enter to see current loop iteration count\n")
    #use raw_input() instead if your are using python 2.x.
    input() #Remove this line if you want to print progress continuously.
    lock.acquire() 
    current_count = count
    lock.release()
    print("Current loop iteration count is ",count,"\n")

该解决方案通过两种方式改善了这种情况

1. 工作线程上不进行IO操作。是的，使用锁涉及系统调用。但它仍然比 IO 块快。

2. 假设python解释器使用futex实现锁，那么获取和释放锁的成本非常慢，因为与工作线程相比，主线程很少持有锁。