start_time = time.time()
# your code
elapsed_time = time.time() - start_time

你也可以编写简单的装饰器来简化各种函数执行时间的测量:

import time
from functools import wraps

PROF_DATA = {}

def profile(fn):
    @wraps(fn)
    def with_profiling(*args, **kwargs):
        start_time = time.time()

        ret = fn(*args, **kwargs)

        elapsed_time = time.time() - start_time

        if fn.__name__ not in PROF_DATA:
            PROF_DATA[fn.__name__] = [0, []]
        PROF_DATA[fn.__name__][0] += 1
        PROF_DATA[fn.__name__][1].append(elapsed_time)

        return ret

    return with_profiling

def print_prof_data():
    for fname, data in PROF_DATA.items():
        max_time = max(data[1])
        avg_time = sum(data[1]) / len(data[1])
        print "Function %s called %d times. " % (fname, data[0]),
        print 'Execution time max: %.3f, average: %.3f' % (max_time, avg_time)

def clear_prof_data():
    global PROF_DATA
    PROF_DATA = {}

用法:

@profile
def your_function(...):
    ...

您可以同时分析多个函数。然后要打印测量值，只需调用print_prof_data():

start_time = time.time()
# your code
elapsed_time = time.time() - start_time

你也可以编写简单的装饰器来简化各种函数执行时间的测量:

import time
from functools import wraps

PROF_DATA = {}

def profile(fn):
    @wraps(fn)
    def with_profiling(*args, **kwargs):
        start_time = time.time()

        ret = fn(*args, **kwargs)

        elapsed_time = time.time() - start_time

        if fn.__name__ not in PROF_DATA:
            PROF_DATA[fn.__name__] = [0, []]
        PROF_DATA[fn.__name__][0] += 1
        PROF_DATA[fn.__name__][1].append(elapsed_time)

        return ret

    return with_profiling

def print_prof_data():
    for fname, data in PROF_DATA.items():
        max_time = max(data[1])
        avg_time = sum(data[1]) / len(data[1])
        print "Function %s called %d times. " % (fname, data[0]),
        print 'Execution time max: %.3f, average: %.3f' % (max_time, avg_time)

def clear_prof_data():
    global PROF_DATA
    PROF_DATA = {}

用法:

@profile
def your_function(...):
    ...

您可以同时分析多个函数。然后要打印测量值，只需调用print_prof_data():

另一种计算时间的好方法是使用with python结构。

With结构会自动调用__enter__和__exit__方法，这正是我们为事情计时所需要的。

让我们创建一个Timer类。

from time import time

class Timer():
    def __init__(self, message):
        self.message = message
    def __enter__(self):
        self.start = time()
        return None  # could return anything, to be used like this: with Timer("Message") as value:
    def __exit__(self, type, value, traceback):
        elapsed_time = (time() - self.start) * 1000
        print(self.message.format(elapsed_time))

然后，你可以像这样使用Timer类:

with Timer("Elapsed time to compute some prime numbers: {}ms"):
    primes = []
    for x in range(2, 500):
        if not any(x % p == 0 for p in primes):
            primes.append(x)
    print("Primes: {}".format(primes))

结果如下:

Primes:[2、3、5、7、11、13、17、19、23、29、31、37、41、43、47、53、59、61、67， 71、73、79、83、89、97、101、103、107、109、113、127、131、137、139、149、151、 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239， 241、251、257、263、269、271、277、281、283、293、307、311、313、317、331、337、 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433， 439、443、449、457、461、463、467、479、487、491、499)

计算一些质数所需的时间:5.01704216003418ms

这是一个更新的Vadim Shender的聪明的代码与表格输出:

import collections
import time
from functools import wraps

PROF_DATA = collections.defaultdict(list)

def profile(fn):
    @wraps(fn)
    def with_profiling(*args, **kwargs):
        start_time = time.time()
        ret = fn(*args, **kwargs)
        elapsed_time = time.time() - start_time
        PROF_DATA[fn.__name__].append(elapsed_time)
        return ret
    return with_profiling

Metrics = collections.namedtuple("Metrics", "sum_time num_calls min_time max_time avg_time fname")

def print_profile_data():
    results = []
    for fname, elapsed_times in PROF_DATA.items():
        num_calls = len(elapsed_times)
        min_time = min(elapsed_times)
        max_time = max(elapsed_times)
        sum_time = sum(elapsed_times)
        avg_time = sum_time / num_calls
        metrics = Metrics(sum_time, num_calls, min_time, max_time, avg_time, fname)
        results.append(metrics)
    total_time = sum([m.sum_time for m in results])
    print("\t".join(["Percent", "Sum", "Calls", "Min", "Max", "Mean", "Function"]))
    for m in sorted(results, reverse=True):
        print("%.1f\t%.3f\t%d\t%.3f\t%.3f\t%.3f\t%s" % (100 * m.sum_time / total_time, m.sum_time, m.num_calls, m.min_time, m.max_time, m.avg_time, m.fname))
    print("%.3f Total Time" % total_time)

在编程中，主要有两种测量时间的方法，结果不同:

>>> print(time.process_time()); time.sleep(10); print(time.process_time())
0.11751394000000001
0.11764988400000001  # took  0 seconds and a bit
>>> print(time.perf_counter()); time.sleep(10); print(time.perf_counter())
3972.465770326
3982.468109075       # took 10 seconds and a bit

Processor Time: This is how long this specific process spends actively being executed on the CPU. Sleep, waiting for a web request, or time when only other processes are executed will not contribute to this. Use time.process_time() Wall-Clock Time: This refers to how much time has passed "on a clock hanging on the wall", i.e. outside real time. Use time.perf_counter() time.time() also measures wall-clock time but can be reset, so you could go back in time time.monotonic() cannot be reset (monotonic = only goes forward) but has lower precision than time.perf_counter()

瓦迪姆·申德的反应很棒。你也可以使用一个简单的装饰器，如下所示:

import datetime
def calc_timing(original_function):                            
    def new_function(*args,**kwargs):                        
        start = datetime.datetime.now()                     
        x = original_function(*args,**kwargs)                
        elapsed = datetime.datetime.now()                      
        print("Elapsed Time = {0}".format(elapsed-start))     
        return x                                             
    return new_function()  

@calc_timing
def a_func(*variables):
    print("do something big!")