blocks|key|4607946|text|如果您想测量CPU时间，可以使用Python3.3及以上版本的time.process_time()：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4607947|import+time
start+=+time.process_time()
#+your+code+here++++
print(time.process_time()+-+start)|code-block|syntax|javascript|4607948|第一个调用会打开计时器，第二个调用会告诉你已经过了多少秒。|4607949|还有一个函数time.clock()，但它是deprecated+since+Python+3.3，将在Python3.8中删除。|4607950|有更好的性能分析工具，如timeit和profile，但是time.process_time()将测量CPU时间，这就是您所询问的。|4607951|如果您想测量挂钟时间，请使用time.time()。|4607952|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/library/time.html#time.process_time|1|https://docs.python.org/3/library/time.html#time.clock^0|V|J|V|J|0|0|0|0|6|C|M|R|1|0|C|6|J|7|0|E|B|0^^$0|@$1|2|3|4|5|6|7|12|8|@$9|13|A|14|B|C]]|D|@$9|15|A|16|1|17]]|E|$]]|$1|F|3|G|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|19|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|O|3|P|5|6|7|1G|8|@$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1L|8|@$9|1M|A|1N|B|C]]|D|@]|E|$]]|$1|S|3|-4|5|6|7|1O|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z]]|10|$5|V|W|X|E|$Y|11]]]]

If you want to measure CPU time, can use <a href="https://docs.python.org/library/time.html#time.process_time" rel="noreferrer"><code>time.process_time()</code></a> for Python 3.3 and above:

<pre><code>import time
start = time.process_time()
# your code here 
print(time.process_time() - start)
</code></pre>

First call turns the timer on, and second call tells you how many seconds have elapsed.

There is also a function <code>time.clock()</code>, but it is <a href="https://docs.python.org/3/library/time.html#time.clock" rel="noreferrer">deprecated since Python 3.3</a> and will be removed in Python 3.8.

There are better profiling tools like <code>timeit</code> and <code>profile</code>, however time.process_time() will measure the CPU time and this is what you're are asking about.

If you want to measure wall clock time instead, use <code>time.time()</code>.

blocks|key|5090627|text|在一个小的方便类的帮助下，您可以像这样测量在缩进行中花费的时间：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|5090628|with+CodeTimer():
+++line_to_measure()
+++another_line()
+++#+etc...|code-block|syntax|javascript|5090629|这将在缩进行完成执行后显示以下内容：|5090630|Code+block+took:+x.xxx+ms|5090631|更新：你现在可以先用pip+install+linetimer获取类，然后再用from+linetimer+import+CodeTimer获取。参见this+GitHub+project。|CODE|5090632|上面类的代码：|5090633|import+timeit

class+CodeTimer:
++++def+__init__(self,+name=None):
++++++++self.name+=+"+'"++%2B+name+%2B+"'"+if+name+else+''

++++def+__enter__(self):
++++++++self.start+=+timeit.default_timer()

++++def+__exit__(self,+exc_type,+exc_value,+traceback):
++++++++self.took+=+(timeit.default_timer()+-+self.start)+*+1000.0
++++++++print('Code+block'+%2B+self.name+%2B+'+took:+'+%2B+str(self.took)+%2B+'+ms')|5090634|然后，您可以将要测量的代码块命名为：|5090635|with+CodeTimer('loop+1'):
+++for+i+in+range(100000):
++++++pass

with+CodeTimer('loop+2'):
+++for+i+in+range(100000):
++++++pass

Code+block+'loop+1'+took:+4.991+ms
Code+block+'loop+2'+took:+3.666+ms|5090636|和nest它们：|5090637|with+CodeTimer('Outer'):
+++for+i+in+range(100000):
++++++pass

+++with+CodeTimer('Inner'):
++++++for+i+in+range(100000):
+++++++++pass

+++for+i+in+range(100000):
++++++pass

Code+block+'Inner'+took:+2.382+ms
Code+block+'Outer'+took:+10.466+ms|5090638|关于timeit.default_timer()，它使用基于操作系统和Python版本的最佳计时器，请参阅this+answer。|5090639|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/justasb/linetimer|1|https://stackoverflow.com/a/85536/407108^0|J|C|0|0|0|0|0|3|A|L|13|V|23|J|0|0|0|0|0|0|1|4|0|0|2|M|1H|B|1|0^^$0|@$1|2|3|4|5|6|7|1F|8|@$9|1G|A|1H|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1I|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1J|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1K|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1L|8|@$9|1M|A|1N|B|C]|$9|1O|A|1P|B|Q]|$9|1Q|A|1R|B|Q]]|D|@$9|1S|A|1T|1|1U]]|E|$]]|$1|R|3|S|5|6|7|1V|8|@]|D|@]|E|$]]|$1|T|3|U|5|H|7|1W|8|@]|D|@]|E|$I|J]]|$1|V|3|W|5|6|7|1X|8|@]|D|@]|E|$]]|$1|X|3|Y|5|H|7|1Y|8|@]|D|@]|E|$I|J]]|$1|Z|3|10|5|6|7|1Z|8|@$9|20|A|21|B|C]]|D|@]|E|$]]|$1|11|3|12|5|H|7|22|8|@]|D|@]|E|$I|J]]|$1|13|3|14|5|6|7|23|8|@$9|24|A|25|B|Q]]|D|@$9|26|A|27|1|28]]|E|$]]|$1|15|3|-4|5|6|7|29|8|@]|D|@]|E|$]]]|16|$17|$5|18|19|1A|E|$1B|1C]]|1D|$5|18|19|1A|E|$1B|1E]]]]

With a help of a small convenience class, you can measure time spent in indented lines like this:

<pre><code>with CodeTimer():
 line_to_measure()
 another_line()
 # etc...
</code></pre>

Which will show the following after the indented line(s) finishes executing:

<pre><code>Code block took: x.xxx ms
</code></pre>

UPDATE: You can now get the class with <code>pip install linetimer</code> and then <code>from linetimer import CodeTimer</code>. See <a href="https://github.com/justasb/linetimer" rel="noreferrer">this GitHub project</a>.

The code for above class:

<pre><code>import timeit

class CodeTimer:
 def __init__(self, name=None):
 self.name = " '" + name + "'" if name else ''

 def __enter__(self):
 self.start = timeit.default_timer()

 def __exit__(self, exc_type, exc_value, traceback):
 self.took = (timeit.default_timer() - self.start) * 1000.0
 print('Code block' + self.name + ' took: ' + str(self.took) + ' ms')
</code></pre>

You could then name the code blocks you want to measure:

<pre><code>with CodeTimer('loop 1'):
 for i in range(100000):
 pass

with CodeTimer('loop 2'):
 for i in range(100000):
 pass

Code block 'loop 1' took: 4.991 ms
Code block 'loop 2' took: 3.666 ms
</code></pre>

And nest them:

<pre><code>with CodeTimer('Outer'):
 for i in range(100000):
 pass

 with CodeTimer('Inner'):
 for i in range(100000):
 pass

 for i in range(100000):
 pass

Code block 'Inner' took: 2.382 ms
Code block 'Outer' took: 10.466 ms
</code></pre>

Regarding <code>timeit.default_timer()</code>, it uses the best timer based on OS and Python version, see <a href="https://stackoverflow.com/a/85536/407108">this answer</a>.

blocks|key|1258874|text|您也可以使用time库：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1258875|import+time

start+=+time.time()

#+your+code

#+end

print(f'Time:+{time.time()+-+start}')|code-block|syntax|javascript|1258876|entityMap^0|6|4|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

You can also use <code>time</code> library:

<pre><code>import time

start = time.time()

# your code

# end

print(f'Time: {time.time() - start}')
</code></pre>

blocks|key|1258905|text|我总是喜欢以小时、分钟和秒(%25H:%25M:%25S)格式检查时间：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1258906|from+datetime+import+datetime
start+=+datetime.now()
#+your+code
end+=+datetime.now()
time_taken+=+end+-+start
print('Time:+',time_taken)+|code-block|syntax|javascript|1258907|输出：|1258908|Time:++0:00:00.000019|1258909|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

I always prefer to check time in hours, minutes and seconds (%H:%M:%S) format:

<pre><code>from datetime import datetime
start = datetime.now()
# your code
end = datetime.now()
time_taken = end - start
print('Time: ',time_taken) 
</code></pre>

output: 

<pre><code>Time: 0:00:00.000019
</code></pre>

blocks|key|4608083|text|我正在寻找一种方法，如何用最少的代码输出格式化的时间，所以这是我的解决方案。许多人使用Pandas，所以在某些情况下，这可以节省额外的库导入。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4608084|import+pandas+as+pd
start+=+pd.Timestamp.now()
#+code
print(pd.Timestamp.now()-start)|code-block|syntax|javascript|4608085|输出：|4608086|0+days+00:05:32.541600|4608087|如果时间精度不是最重要的，我建议使用这个库，否则使用time库：|offset|length|style|CODE|4608088|%25timeit+pd.Timestamp.now()每环输出3.29µs±214+ns|4608089|%25timeit+time.time()每环输出154纳秒±13.3纳秒|4608090|entityMap^0|0|0|0|0|Q|4|0|0|Q|0|0|J|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|10|8|@$M|11|N|12|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|6|7|13|8|@$M|14|N|15|O|P]]|9|@]|A|$]]|$1|S|3|T|5|6|7|16|8|@$M|17|N|18|O|P]]|9|@]|A|$]]|$1|U|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|V|$]]

I was looking for a way how to output a formatted time with minimal code, so here is my solution. Many people use Pandas anyway, so in some cases this can save from additional library imports.

<pre><code>import pandas as pd
start = pd.Timestamp.now()
# code
print(pd.Timestamp.now()-start)
</code></pre>

Output:

<pre><code>0 days 00:05:32.541600
</code></pre>

I would recommend using this if time precision is not the most important, otherwise use <code>time</code> library:

<code>%timeit pd.Timestamp.now()</code> outputs 3.29 µs ± 214 ns per loop

<code>%timeit time.time()</code> outputs 154 ns ± 13.3 ns per loop

blocks|key|1258954|text|你也可以试试这个：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1258955|from+time+import+perf_counter

t0+=+perf_counter()

...

t1+=+perf_counter()
time_taken+=+t1+-+t0|code-block|syntax|javascript|1258956|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can try this as well:

<pre class="lang-py prettyprint-override"><code>from time import perf_counter

t0 = perf_counter()

...

t1 = perf_counter()
time_taken = t1 - t0
</code></pre>

blocks|key|1259000|text|将代码放在函数中，然后使用装饰器进行计时是另一种选择。(Source)这种方法的优点是，您只需定义一次计时器，然后将其与一起使用，这是每个函数的一个简单的附加行。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|1259001|首先，定义timer装饰器：|CODE|1259002|import+functools
import+time

def+timer(func):
++++@functools.wraps(func)
++++def+wrapper(*args,+**kwargs):
++++++++start_time+=+time.perf_counter()
++++++++value+=+func(*args,+**kwargs)
++++++++end_time+=+time.perf_counter()
++++++++run_time+=+end_time+-+start_time
++++++++print("Finished+{}+in+{}+secs".format(repr(func.__name__),+round(run_time,+3)))
++++++++return+value

++++return+wrapper|code-block|syntax|javascript|1259003|然后，在定义函数时使用装饰器：|1259004|@timer
def+doubled_and_add(num):
++++res+=+sum([i*2+for+i+in+range(num)])
++++print("Result+:+{}".format(res))|1259005|让我们试一试：|1259006|doubled_and_add(100000)
doubled_and_add(1000000)|1259007|输出：|1259008|Result+:+9999900000
Finished+'doubled_and_add'+in+0.0119+secs
Result+:+999999000000
Finished+'doubled_and_add'+in+0.0897+secs|1259009|注意:我不确定为什么要用time.perf_counter而不是time.time。欢迎提出意见。|1259010|entityMap|0|LINK|mutability|MUTABLE|url|https://medium.com/better-programming/decorators-in-python-72a1d578eac4^0|1O|K|S|6|0|0|5|5|0|0|0|0|0|0|0|0|C|H|W|9|0^^$0|@$1|2|3|4|5|6|7|19|8|@$9|1A|A|1B|B|C]]|D|@$9|1C|A|1D|1|1E]]|E|$]]|$1|F|3|G|5|6|7|1F|8|@$9|1G|A|1H|B|H]]|D|@]|E|$]]|$1|I|3|J|5|K|7|1I|8|@]|D|@]|E|$L|M]]|$1|N|3|O|5|6|7|1J|8|@]|D|@]|E|$]]|$1|P|3|Q|5|K|7|1K|8|@]|D|@]|E|$L|M]]|$1|R|3|S|5|6|7|1L|8|@]|D|@]|E|$]]|$1|T|3|U|5|K|7|1M|8|@]|D|@]|E|$L|M]]|$1|V|3|W|5|6|7|1N|8|@]|D|@]|E|$]]|$1|X|3|Y|5|K|7|1O|8|@]|D|@]|E|$L|M]]|$1|Z|3|10|5|6|7|1P|8|@$9|1Q|A|1R|B|H]|$9|1S|A|1T|B|H]]|D|@]|E|$]]|$1|11|3|-4|5|6|7|1U|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18]]]]

Putting the code in a function, then using a decorator for timing is another option. (<a href="https://medium.com/better-programming/decorators-in-python-72a1d578eac4" rel="noreferrer">Source</a>) The advantage of this method is that you define timer once and use it with a simple additional line for every function.
First, define <code>timer</code> decorator:
<pre><code>import functools
import time

def timer(func):
 @functools.wraps(func)
 def wrapper(*args, **kwargs):
 start_time = time.perf_counter()
 value = func(*args, **kwargs)
 end_time = time.perf_counter()
 run_time = end_time - start_time
 print(&quot;Finished {} in {} secs&quot;.format(repr(func.__name__), round(run_time, 3)))
 return value

 return wrapper
</code></pre>
Then, use the decorator while defining the function:
<pre><code>@timer
def doubled_and_add(num):
 res = sum([i*2 for i in range(num)])
 print(&quot;Result : {}&quot;.format(res))
</code></pre>
Let's try:
<pre><code>doubled_and_add(100000)
doubled_and_add(1000000)
</code></pre>
Output:
<pre><code>Result : 9999900000
Finished 'doubled_and_add' in 0.0119 secs
Result : 999999000000
Finished 'doubled_and_add' in 0.0897 secs
</code></pre>
Note: I'm not sure why to use <code>time.perf_counter</code> instead of <code>time.time</code>. Comments are welcome.

blocks|key|5090833|text|让我在https://stackoverflow.com/a/63665115/7412781解决方案中添加更多内容。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|5090834|5090835|删除了对functools.|unordered-list-item|style|CODE|5090836|Used进程所用时间的依赖，而不是time.perf_counter()的绝对计数器，因为进程可以通过内核进行上下文切换。|5090837|也使用原始函数指针time.process_time()来获取正确的类名。|5090838|5090839|这是装饰器代码。|5090840|import+time

def+decorator_time_taken(fnc):
++++def+inner(*args):
++++++++start+=+time.process_time()
++++++++ret+=+fnc(*args)
++++++++end+=+time.process_time()
++++++++print("{}+took+{}+seconds".format(fnc,+round((end+-+start),+6)))
++++++++return+ret
++++return+inner|code-block|syntax|javascript|5090841|这是使用示例代码。它正在检查193939是否为质数。|5090842|class+PrimeBrute:
++++@decorator_time_taken
++++def+isPrime(self,+a):
++++++++for+i+in+range(a-2):
+++++++++++if+a+%25+(i%2B2)+==+0:+return+False
++++++++return+True

inst+=+PrimeBrute()
print(inst.isPrime(193939))|5090843|这是输出。|5090844|<function+PrimeBrute.isPrime+at+0x7fc0c6919ae8>+took+0.015789+seconds
True|5090845|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/63665115/7412781^0|3|18|0|0|0|4|9|0|H|J|0|9|J|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@$A|1C|B|1D|1|1E]]|C|$]]|$1|D|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|1G|8|@$A|1H|B|1I|H|I]]|9|@]|C|$]]|$1|J|3|K|5|G|7|1J|8|@$A|1K|B|1L|H|I]]|9|@]|C|$]]|$1|L|3|M|5|G|7|1M|8|@$A|1N|B|1O|H|I]]|9|@]|C|$]]|$1|N|3|-4|5|6|7|1P|8|@]|9|@]|C|$]]|$1|O|3|P|5|6|7|1Q|8|@]|9|@]|C|$]]|$1|Q|3|R|5|S|7|1R|8|@]|9|@]|C|$T|U]]|$1|V|3|W|5|6|7|1S|8|@]|9|@]|C|$]]|$1|X|3|Y|5|S|7|1T|8|@]|9|@]|C|$T|U]]|$1|Z|3|10|5|6|7|1U|8|@]|9|@]|C|$]]|$1|11|3|12|5|S|7|1V|8|@]|9|@]|C|$T|U]]|$1|13|3|-4|5|6|7|1W|8|@]|9|@]|C|$]]]|14|$15|$5|16|17|18|C|$19|1A]]]]

Let me add a little more to <a href="https://stackoverflow.com/a/63665115/7412781">https://stackoverflow.com/a/63665115/7412781</a> solution.
<ul>
<li>Removed dependency on <code>functools</code>.</li>
<li>Used process time taken <code>time.process_time()</code> instead of absolute counter of <code>time.perf_counter()</code> because the process can be context switched out via kernel.</li>
<li>Used the raw function pointer print to get the correct class name as well.</li>
</ul>
This is the decorator code.
<pre><code>import time

def decorator_time_taken(fnc):
 def inner(*args):
 start = time.process_time()
 ret = fnc(*args)
 end = time.process_time()
 print(&quot;{} took {} seconds&quot;.format(fnc, round((end - start), 6)))
 return ret
 return inner
</code></pre>
This is the usage sample code. It's checking if 193939 is prime or not.
<pre><code>class PrimeBrute:
 @decorator_time_taken
 def isPrime(self, a):
 for i in range(a-2):
 if a % (i+2) == 0: return False
 return True

inst = PrimeBrute()
print(inst.isPrime(193939))
</code></pre>
This is the output.
<pre><code>&lt;function PrimeBrute.isPrime at 0x7fc0c6919ae8&gt; took 0.015789 seconds
True
</code></pre>

blocks|key|5090903|text|使用timeit模块对您的性能进行基准测试：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|5090904|def+test():
++++print("test")
++++emptyFunction()
++++for+i+in+[x+for+x+in+range(10000)]:
++++++++i**i


def+emptyFunction():
++++pass

if+__name__+==+"__main__":
++++import+timeit
++++print(timeit.timeit("test()",+number+=+5,+globals+=+globals()))
++++#print(timeit.timeit("test()",+setup+=+"from+__main__+import+test",
++++#++++number+=+5))|code-block|syntax|javascript|5090905|在本例中，第一个参数定义了我们想要执行test的代码&+number定义了您想要重复执行的次数。|BOLD|5090906|输出：|5090907|test
test
test
test
test
36.81822113099952|5090908|entityMap^0|2|6|0|0|J|4|S|6|16|2|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@$9|U|A|V|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|W|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|X|8|@$9|Y|A|Z|B|C]|$9|10|A|11|B|C]|$9|12|A|13|B|M]]|D|@]|E|$]]|$1|N|3|O|5|6|7|14|8|@]|D|@]|E|$]]|$1|P|3|Q|5|H|7|15|8|@]|D|@]|E|$I|J]]|$1|R|3|-4|5|6|7|16|8|@]|D|@]|E|$]]]|S|$]]

Use <code>timeit</code> module to benchmark your performance:
<pre><code>def test():
 print(&quot;test&quot;)
 emptyFunction()
 for i in [x for x in range(10000)]:
 i**i


def emptyFunction():
 pass

if __name__ == &quot;__main__&quot;:
 import timeit
 print(timeit.timeit(&quot;test()&quot;, number = 5, globals = globals()))
 #print(timeit.timeit(&quot;test()&quot;, setup = &quot;from __main__ import test&quot;,
 # number = 5))
</code></pre>
the first parameter defines the piece of code which we want to execute <code>test</code> in this case &amp; <code>number</code> defines how many times you want to repeat the execution.
<h2>Output:</h2>
<pre><code>test
test
test
test
test
36.81822113099952
</code></pre>

So in Java, we can do <a href="https://stackoverflow.com/questions/313893/how-to-measure-time-taken-by-a-function-to-execute">How to measure time taken by a function to execute</a>

But how is it done in python? To measure the time start and end time between lines of codes?
Something that does this:

<pre><code>import some_time_library

starttime = some_time_library.some_module()
code_tobe_measured() 
endtime = some_time_library.some_module()

time_taken = endtime - starttime
</code></pre>

How to measure time taken between lines of code in python?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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所以在Java语言中，我们可以做但是在python中是如何做到的呢？测量代码行之间的时间开始和结束时间？这样做的东西：import some_time_librarystarttime = some_time_library.some_module()code_tobe_measured() endtime = som...

问如何在python中测量代码行之间所用的时间？
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问如何在python中测量代码行之间所用的时间？EN