import time scale=50 print("执行开始".center(scale//2,'-')) t=time.perf_counter() for i in range(scale+1): a='*'*i b='.'*(scale-i) c=(i/scale)*100 t-= time.perf_counter() print("\n{:^3.0f}%[{}->{}]{:.2f}s".format(c,a,b,-t),end='') time.sleep(0.1) print("\n"+"执行结束".center(scale//2,'-'))改写这段代码
时间: 2023-05-25 22:04:42 浏览: 43
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相关问题
抄写下面的程序,并比较两种不同矩阵运算方式的速度: import numpy as np import time my_arr = np.arange(1000000) my_list = list(range(1000000)) beginTime = time.perf_counter() for _ in range(10): my_arr2 = my_arr * 2 tNp = time.perf_counter() -beginTime beginTime = time.perf_counter() for _ in range(10): my_list2 = [x * 2 for x in my_list] tList = time.perf_counter() -beginTime print("tNp = ", tNp) print("tList = ", tList)
代码如下:
```python
import numpy as np
import time
my_arr = np.arange(1000000)
my_list = list(range(1000000))
beginTime = time.perf_counter()
for _ in range(10):
my_arr2 = my_arr * 2
tNp = time.perf_counter() - beginTime
beginTime = time.perf_counter()
for _ in range(10):
my_list2 = [x * 2 for x in my_list]
tList = time.perf_counter() - beginTime
print("tNp = ", tNp)
print("tList = ", tList)
```
这段代码分别使用了 NumPy 库和 Python 自带的列表来对一个长度为 1000000 的数组进行矩阵运算,比较两种运算方式的速度。其中,重要的是 `time.perf_counter()` 函数,它返回一个 CPU 时间,以秒为单位。
程序的运行结果可能略有不同,但一般来说,NumPy 库的速度要远远快于 Python 自带的列表。
from odoo import time def work_one(): begin_time = time.perf_counter() for i in range(10000): print(i) print(time.perf_counter() - begin_time)为什么没有结果
您的代码中似乎缺少了缩进。请尝试将其缩进,并确保在导入模块时使用正确的语法。以下是修改后的代码示例:
```python
import time
def work_one():
begin_time = time.perf_counter()
for i in range(10000):
print(i)
print(time.perf_counter() - begin_time)
work_one()
```
这个程序应该输出一个计数器,从0到9999,然后输出程序的执行时间。如果您仍然没有看到输出,请确保在正确的地方调用了函数 `work_one()`。