我有一个Python命令行程序,需要一段时间才能完成。我想知道完成跑步所需的确切时间。
我看过timeit模块,但它似乎只适用于小代码片段。我想给整个节目计时。
我有一个Python命令行程序,需要一段时间才能完成。我想知道完成跑步所需的确切时间。
我看过timeit模块,但它似乎只适用于小代码片段。我想给整个节目计时。
当前回答
对于函数,我建议使用我创建的这个简单的修饰符。
def timeit(method):
def timed(*args, **kwargs):
ts = time.time()
result = method(*args, **kwargs)
te = time.time()
if 'log_time' in kwargs:
name = kwargs.get('log_name', method.__name__.upper())
kwargs['log_time'][name] = int((te - ts) * 1000)
else:
print('%r %2.22f ms' % (method.__name__, (te - ts) * 1000))
return result
return timed
@timeit
def foo():
do_some_work()
# foo()
# 'foo' 0.000953 ms
其他回答
您可以使用Python分析器cProfile来测量CPU时间,以及每个函数内部花费的时间以及每个函数被调用的次数。如果您想在不知道从哪里开始的情况下提高脚本的性能,这非常有用。对另一个堆栈溢出问题的回答很好。查看文档总是很好的。
以下是如何从命令行使用cProfile评测脚本的示例:
$ python -m cProfile euler048.py
1007 function calls in 0.061 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.061 0.061 <string>:1(<module>)
1000 0.051 0.000 0.051 0.000 euler048.py:2(<lambda>)
1 0.005 0.005 0.061 0.061 euler048.py:2(<module>)
1 0.000 0.000 0.061 0.061 {execfile}
1 0.002 0.002 0.053 0.053 {map}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler objects}
1 0.000 0.000 0.000 0.000 {range}
1 0.003 0.003 0.003 0.003 {sum}
在Linux或Unix中:
$ time python yourprogram.py
在Windows中,请参阅StackOverflow问题:如何在Windows命令行上测量命令的执行时间?
对于更详细的输出,
$ time -v python yourprogram.py
Command being timed: "python3 yourprogram.py"
User time (seconds): 0.08
System time (seconds): 0.02
Percent of CPU this job got: 98%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:00.10
Average shared text size (kbytes): 0
Average unshared data size (kbytes): 0
Average stack size (kbytes): 0
Average total size (kbytes): 0
Maximum resident set size (kbytes): 9480
Average resident set size (kbytes): 0
Major (requiring I/O) page faults: 0
Minor (reclaiming a frame) page faults: 1114
Voluntary context switches: 0
Involuntary context switches: 22
Swaps: 0
File system inputs: 0
File system outputs: 0
Socket messages sent: 0
Socket messages received: 0
Signals delivered: 0
Page size (bytes): 4096
Exit status: 0
我在查找两种不同方法的运行时间时遇到的问题,这两种方法用于查找所有<=一个数的素数。当在程序中进行用户输入时。
错误的方法
#Sample input for a number 20
#Sample output [2, 3, 5, 7, 11, 13, 17, 19]
#Total Running time = 0.634 seconds
import time
start_time = time.time()
#Method 1 to find all the prime numbers <= a Number
# Function to check whether a number is prime or not.
def prime_no(num):
if num<2:
return False
else:
for i in range(2, num//2+1):
if num % i == 0:
return False
return True
#To print all the values <= n
def Prime_under_num(n):
a = [2]
if n <2:
print("None")
elif n==2:
print(2)
else:
"Neglecting all even numbers as even numbers won't be prime in order to reduce the time complexity."
for i in range(3, n+1, 2):
if prime_no(i):
a.append(i)
print(a)
"When Method 1 is only used outputs of running time for different inputs"
#Total Running time = 2.73761 seconds #n = 100
#Total Running time = 3.14781 seconds #n = 1000
#Total Running time = 8.69278 seconds #n = 10000
#Total Running time = 18.73701 seconds #n = 100000
#Method 2 to find all the prime numbers <= a Number
def Prime_under_num(n):
a = [2]
if n <2:
print("None")
elif n==2:
print(2)
else:
for i in range(3, n+1, 2):
if n%i ==0:
pass
else:
a.append(i)
print(a)
"When Method 2 is only used outputs of running time for different inputs"
# Total Running time = 2.75935 seconds #n = 100
# Total Running time = 2.86332 seconds #n = 1000
# Total Running time = 4.59884 seconds #n = 10000
# Total Running time = 8.55057 seconds #n = 100000
if __name__ == "__main__" :
n = int(input())
Prime_under_num(n)
print("Total Running time = {:.5f} seconds".format(time.time() - start_time))
上述所有情况下获得的不同运行时间都是错误的。对于我们正在接受输入的问题,我们必须在接受输入后才开始计时。这里,用户键入输入所花费的时间也与运行时间一起计算。
正确的方法
我们必须从开头删除start_time=time.time()并将其添加到主块中。
if __name__ == "__main__" :
n = int(input())
start_time = time.time()
Prime_under_num(n)
print("Total Running time = {:.3f} seconds".format(time.time() - start_time))
因此,两种方法单独使用时的输出如下:-
# Method 1
# Total Running time = 0.00159 seconds #n = 100
# Total Running time = 0.00506 seconds #n = 1000
# Total Running time = 0.22987 seconds #n = 10000
# Total Running time = 18.55819 seconds #n = 100000
# Method 2
# Total Running time = 0.00011 seconds #n = 100
# Total Running time = 0.00118 seconds #n = 1000
# Total Running time = 0.00302 seconds #n = 10000
# Total Running time = 0.01450 seconds #n = 100000
现在我们可以看到,与错误方法相比,总运行时间有显著差异。即使方法2在两种方法中的性能优于方法1,但第一种方法(错误方法)是错误的。
我使用了一个非常简单的函数来计时代码执行的一部分:
import time
def timing():
start_time = time.time()
return lambda x: print("[{:.2f}s] {}".format(time.time() - start_time, x))
要使用它,只需在代码之前调用它来度量以检索函数计时,然后在代码之后调用带有注释的函数。时间将显示在评论前面。例如:
t = timing()
train = pd.read_csv('train.csv',
dtype={
'id': str,
'vendor_id': str,
'pickup_datetime': str,
'dropoff_datetime': str,
'passenger_count': int,
'pickup_longitude': np.float64,
'pickup_latitude': np.float64,
'dropoff_longitude': np.float64,
'dropoff_latitude': np.float64,
'store_and_fwd_flag': str,
'trip_duration': int,
},
parse_dates = ['pickup_datetime', 'dropoff_datetime'],
)
t("Loaded {} rows data from 'train'".format(len(train)))
然后输出将如下所示:
[9.35s] Loaded 1458644 rows data from 'train'
有一个timeit模块,可用于对Python代码的执行时间进行计时。
它在Python文档26.6中有详细的文档和示例。timeit-测量小代码片段的执行时间。