So in Java, we can do How to measure time taken by a function to execute
But how is it done in python? To measure the time start and end time between lines of code?
Something that does this:
import some_time_library
starttime = some_time_library.some_module()
code_tobe_measured()
endtime = some_time_library.some_module()
time_taken = endtime - starttime
If you want to measure CPU time, can use time.process_time() for Python 3.3 and above:
import time
start = time.process_time()
# your code here
print(time.process_time() - start)
First call turns the timer on, and second call tells you how many seconds have elapsed.
There is also a function time.clock(), but it is deprecated since Python 3.3 and will be removed in Python 3.8.
There are better profiling tools like timeit and profile, 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 time.time().
You can also use time library:
import time
start = time.time()
# your code
# end
print(f'Time: {time.time() - start}')
With a help of a small convenience class, you can measure time spent in indented lines like this:
with CodeTimer():
line_to_measure()
another_line()
# etc...
Which will show the following after the indented line(s) finishes executing:
Code block took: x.xxx ms
UPDATE: You can now get the class with pip install linetimer and then from linetimer import CodeTimer. See this GitHub project.
The code for above class:
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')
You could then name the code blocks you want to measure:
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
And nest them:
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
Regarding timeit.default_timer(), it uses the best timer based on OS and Python version, see this answer.
Putting the code in a function, then using a decorator for timing is another option. (Source) The advantage of this method is that you define timer once and use it with a simple additional line for every function.
First, define timer decorator:
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
Then, use the decorator while defining the function:
#timer
def doubled_and_add(num):
res = sum([i*2 for i in range(num)])
print("Result : {}".format(res))
Let's try:
doubled_and_add(100000)
doubled_and_add(1000000)
Output:
Result : 9999900000
Finished 'doubled_and_add' in 0.0119 secs
Result : 999999000000
Finished 'doubled_and_add' in 0.0897 secs
Note: I'm not sure why to use time.perf_counter instead of time.time. Comments are welcome.
I always prefer to check time in hours, minutes and seconds (%H:%M:%S) format:
from datetime import datetime
start = datetime.now()
# your code
end = datetime.now()
time_taken = end - start
print('Time: ',time_taken)
output:
Time: 0:00:00.000019
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.
import pandas as pd
start = pd.Timestamp.now()
# code
print(pd.Timestamp.now()-start)
Output:
0 days 00:05:32.541600
I would recommend using this if time precision is not the most important, otherwise use time library:
%timeit pd.Timestamp.now() outputs 3.29 µs ± 214 ns per loop
%timeit time.time() outputs 154 ns ± 13.3 ns per loop
You can try this as well:
from time import perf_counter
t0 = perf_counter()
...
t1 = perf_counter()
time_taken = t1 - t0
Let me add a little more to https://stackoverflow.com/a/63665115/7412781 solution.
Removed dependency on functools.
Used process time taken time.process_time() instead of absolute counter of time.perf_counter() because the process can be context switched out via kernel.
Used the raw function pointer print to get the correct class name as well.
This is the decorator code.
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
This is the usage sample code. It's checking if 193939 is prime or not.
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))
This is the output.
<function PrimeBrute.isPrime at 0x7fc0c6919ae8> took 0.015789 seconds
True
Use timeit module to benchmark your performance:
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))
the first parameter defines the piece of code which we want to execute test in this case & number defines how many times you want to repeat the execution.
Output:
test
test
test
test
test
36.81822113099952
Using the module time, we can calculate unix time at the start of the function and at the end of a function. Here is how the code might look like:
from time import time as unix
This code imports time.time which allows us to calculate unix time.
from time import sleep
This is not mandatory, but I am also importing time.sleep for one of the demonstrations.
START_TIME = unix()
This is what calculates unix time and puts it in a variable. Remember, the function unix is not an actual function. I imported time.time as unix, so if you did not put as unix in the first import, you will need to use time.time().
After this, we put whichever function or code we want.
At the end of the code snippet we put
TOTAL_TIME = unix()-START_TIME
This line of code does two things: It calculates unix time at the end of the function, and using the variable START_TIME from before, we calculate the amount of time it took to execute the code snippet.
We can then use this variable wherever we want, including for a print() function.
print("The snippet took {} seconds to execute".format(TOTAL_TIME))
Here I wrote a quick demonstration code that has two experiments as a demonstration. (Fully commented)
from time import time as unix # Import the module to measure unix time
from time import sleep
# Here are a few examples:
# 1. Counting to 100 000
START_TIME = unix()
for i in range(0, 100001):
print("Number: {}\r".format(i), end="")
TOTAL_TIME = unix() - START_TIME
print("\nFinal time (Expirement 1): {} s\n".format(TOTAL_TIME))
# 2. Precision of sleep
for i in range(10):
START_TIME = unix()
sleep(0.1)
TOTAL_TIME = unix() - START_TIME
print("Sleep(0.1): Index: {}, Time: {} s".format(i,TOTAL_TIME))
Here was my output:
Number: 100000
Final time (Expirement 1): 16.666812419891357 s
Sleep(0.1): Index: 0, Time: 0.10014867782592773 s
Sleep(0.1): Index: 1, Time: 0.10016226768493652 s
Sleep(0.1): Index: 2, Time: 0.10202860832214355 s
Sleep(0.1): Index: 3, Time: 0.10015869140625 s
Sleep(0.1): Index: 4, Time: 0.10014724731445312 s
Sleep(0.1): Index: 5, Time: 0.10013675689697266 s
Sleep(0.1): Index: 6, Time: 0.10014677047729492 s
Sleep(0.1): Index: 7, Time: 0.1001439094543457 s
Sleep(0.1): Index: 8, Time: 0.10044598579406738 s
Sleep(0.1): Index: 9, Time: 0.10014700889587402 s
>
import datetime
#this code before computation
%%timeit
~code~
I want to measure the time it took to execute a function. I couldn't get timeit to work:
import timeit
start = timeit.timeit()
print("hello")
end = timeit.timeit()
print(end - start)
Use time.time() to measure the elapsed wall-clock time between two points:
import time
start = time.time()
print("hello")
end = time.time()
print(end - start)
This gives the execution time in seconds.
Another option since Python 3.3 might be to use perf_counter or process_time, depending on your requirements. Before 3.3 it was recommended to use time.clock (thanks Amber). However, it is currently deprecated:
On Unix, return the current processor time as a floating point number
expressed in seconds. The precision, and in fact the very definition
of the meaning of “processor time”, depends on that of the C function
of the same name.
On Windows, this function returns wall-clock seconds elapsed since the
first call to this function, as a floating point number, based on the
Win32 function QueryPerformanceCounter(). The resolution is typically
better than one microsecond.
Deprecated since version 3.3: The behaviour of this function depends
on the platform: use perf_counter() or process_time() instead,
depending on your requirements, to have a well defined behaviour.
Use timeit.default_timer instead of timeit.timeit. The former provides the best clock available on your platform and version of Python automatically:
from timeit import default_timer as timer
start = timer()
# ...
end = timer()
print(end - start) # Time in seconds, e.g. 5.38091952400282
timeit.default_timer is assigned to time.time() or time.clock() depending on OS. On Python 3.3+ default_timer is time.perf_counter() on all platforms. See Python - time.clock() vs. time.time() - accuracy?
See also:
Optimizing code
How to optimize for speed
Python 3 only:
Since time.clock() is deprecated as of Python 3.3, you will want to use time.perf_counter() for system-wide timing, or time.process_time() for process-wide timing, just the way you used to use time.clock():
import time
t = time.process_time()
#do some stuff
elapsed_time = time.process_time() - t
The new function process_time will not include time elapsed during sleep.
Measuring time in seconds:
from timeit import default_timer as timer
from datetime import timedelta
start = timer()
# ....
# (your code runs here)
# ...
end = timer()
print(timedelta(seconds=end-start))
Output:
0:00:01.946339
Given a function you'd like to time,
test.py:
def foo():
# print "hello"
return "hello"
the easiest way to use timeit is to call it from the command line:
% python -mtimeit -s'import test' 'test.foo()'
1000000 loops, best of 3: 0.254 usec per loop
Do not try to use time.time or time.clock (naively) to compare the speed of functions. They can give misleading results.
PS. Do not put print statements in a function you wish to time; otherwise the time measured will depend on the speed of the terminal.
It's fun to do this with a context-manager that automatically remembers the start time upon entry to a with block, then freezes the end time on block exit. With a little trickery, you can even get a running elapsed-time tally inside the block from the same context-manager function.
The core library doesn't have this (but probably ought to). Once in place, you can do things like:
with elapsed_timer() as elapsed:
# some lengthy code
print( "midpoint at %.2f seconds" % elapsed() ) # time so far
# other lengthy code
print( "all done at %.2f seconds" % elapsed() )
Here's contextmanager code sufficient to do the trick:
from contextlib import contextmanager
from timeit import default_timer
#contextmanager
def elapsed_timer():
start = default_timer()
elapser = lambda: default_timer() - start
yield lambda: elapser()
end = default_timer()
elapser = lambda: end-start
And some runnable demo code:
import time
with elapsed_timer() as elapsed:
time.sleep(1)
print(elapsed())
time.sleep(2)
print(elapsed())
time.sleep(3)
Note that by design of this function, the return value of elapsed() is frozen on block exit, and further calls return the same duration (of about 6 seconds in this toy example).
I prefer this. timeit doc is far too confusing.
from datetime import datetime
start_time = datetime.now()
# INSERT YOUR CODE
time_elapsed = datetime.now() - start_time
print('Time elapsed (hh:mm:ss.ms) {}'.format(time_elapsed))
Note, that there isn't any formatting going on here, I just wrote hh:mm:ss into the printout so one can interpret time_elapsed
Here's another way to do this:
>> from pytictoc import TicToc
>> t = TicToc() # create TicToc instance
>> t.tic() # Start timer
>> # do something
>> t.toc() # Print elapsed time
Elapsed time is 2.612231 seconds.
Comparing with traditional way:
>> from time import time
>> t1 = time()
>> # do something
>> t2 = time()
>> elapsed = t2 - t1
>> print('Elapsed time is %f seconds.' % elapsed)
Elapsed time is 2.612231 seconds.
Installation:
pip install pytictoc
Refer to the PyPi page for more details.
The easiest way to calculate the duration of an operation:
import time
start_time = time.monotonic()
<operations, programs>
print('seconds: ', time.monotonic() - start_time)
Official docs here.
Here are my findings after going through many good answers here as well as a few other articles.
First, if you are debating between timeit and time.time, the timeit has two advantages:
timeit selects the best timer available on your OS and Python version.
timeit disables garbage collection, however, this is not something you may or may not want.
Now the problem is that timeit is not that simple to use because it needs setup and things get ugly when you have a bunch of imports. Ideally, you just want a decorator or use with block and measure time. Unfortunately, there is nothing built-in available for this so you have two options:
Option 1: Use timebudget library
The timebudget is a versatile and very simple library that you can use just in one line of code after pip install.
#timebudget # Record how long this function takes
def my_method():
# my code
Option 2: Use my small module
I created below little timing utility module called timing.py. Just drop this file in your project and start using it. The only external dependency is runstats which is again small.
Now you can time any function just by putting a decorator in front of it:
import timing
#timing.MeasureTime
def MyBigFunc():
#do something time consuming
for i in range(10000):
print(i)
timing.print_all_timings()
If you want to time portion of code then just put it inside with block:
import timing
#somewhere in my code
with timing.MeasureBlockTime("MyBlock"):
#do something time consuming
for i in range(10000):
print(i)
# rest of my code
timing.print_all_timings()
Advantages:
There are several half-backed versions floating around so I want to point out few highlights:
Use timer from timeit instead of time.time for reasons described earlier.
You can disable GC during timing if you want.
Decorator accepts functions with named or unnamed params.
Ability to disable printing in block timing (use with timing.MeasureBlockTime() as t and then t.elapsed).
Ability to keep gc enabled for block timing.
Using time.time to measure execution gives you the overall execution time of your commands including running time spent by other processes on your computer. It is the time the user notices, but is not good if you want to compare different code snippets / algorithms / functions / ...
More information on timeit:
Using the timeit Module
timeit – Time the execution of small bits of Python code
If you want a deeper insight into profiling:
http://wiki.python.org/moin/PythonSpeed/PerformanceTips#Profiling_Code
How can you profile a python script?
Update: I used http://pythonhosted.org/line_profiler/ a lot during the last year and find it very helpfull and recommend to use it instead of Pythons profile module.
Use profiler module. It gives a very detailed profile.
import profile
profile.run('main()')
it outputs something like:
5 function calls in 0.047 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 :0(exec)
1 0.047 0.047 0.047 0.047 :0(setprofile)
1 0.000 0.000 0.000 0.000 <string>:1(<module>)
0 0.000 0.000 profile:0(profiler)
1 0.000 0.000 0.047 0.047 profile:0(main())
1 0.000 0.000 0.000 0.000 two_sum.py:2(twoSum)
I've found it very informative.
The python cProfile and pstats modules offer great support for measuring time elapsed in certain functions without having to add any code around the existing functions.
For example if you have a python script timeFunctions.py:
import time
def hello():
print "Hello :)"
time.sleep(0.1)
def thankyou():
print "Thank you!"
time.sleep(0.05)
for idx in range(10):
hello()
for idx in range(100):
thankyou()
To run the profiler and generate stats for the file you can just run:
python -m cProfile -o timeStats.profile timeFunctions.py
What this is doing is using the cProfile module to profile all functions in timeFunctions.py and collecting the stats in the timeStats.profile file. Note that we did not have to add any code to existing module (timeFunctions.py) and this can be done with any module.
Once you have the stats file you can run the pstats module as follows:
python -m pstats timeStats.profile
This runs the interactive statistics browser which gives you a lot of nice functionality. For your particular use case you can just check the stats for your function. In our example checking stats for both functions shows us the following:
Welcome to the profile statistics browser.
timeStats.profile% stats hello
<timestamp> timeStats.profile
224 function calls in 6.014 seconds
Random listing order was used
List reduced from 6 to 1 due to restriction <'hello'>
ncalls tottime percall cumtime percall filename:lineno(function)
10 0.000 0.000 1.001 0.100 timeFunctions.py:3(hello)
timeStats.profile% stats thankyou
<timestamp> timeStats.profile
224 function calls in 6.014 seconds
Random listing order was used
List reduced from 6 to 1 due to restriction <'thankyou'>
ncalls tottime percall cumtime percall filename:lineno(function)
100 0.002 0.000 5.012 0.050 timeFunctions.py:7(thankyou)
The dummy example does not do much but give you an idea of what can be done. The best part about this approach is that I dont have to edit any of my existing code to get these numbers and obviously help with profiling.
Here's another context manager for timing code -
Usage:
from benchmark import benchmark
with benchmark("Test 1+1"):
1+1
=>
Test 1+1 : 1.41e-06 seconds
or, if you need the time value
with benchmark("Test 1+1") as b:
1+1
print(b.time)
=>
Test 1+1 : 7.05e-07 seconds
7.05233786763e-07
benchmark.py:
from timeit import default_timer as timer
class benchmark(object):
def __init__(self, msg, fmt="%0.3g"):
self.msg = msg
self.fmt = fmt
def __enter__(self):
self.start = timer()
return self
def __exit__(self, *args):
t = timer() - self.start
print(("%s : " + self.fmt + " seconds") % (self.msg, t))
self.time = t
Adapted from http://dabeaz.blogspot.fr/2010/02/context-manager-for-timing-benchmarks.html
Here is a tiny timer class that returns "hh:mm:ss" string:
class Timer:
def __init__(self):
self.start = time.time()
def restart(self):
self.start = time.time()
def get_time_hhmmss(self):
end = time.time()
m, s = divmod(end - self.start, 60)
h, m = divmod(m, 60)
time_str = "%02d:%02d:%02d" % (h, m, s)
return time_str
Usage:
# Start timer
my_timer = Timer()
# ... do something
# Get time string:
time_hhmmss = my_timer.get_time_hhmmss()
print("Time elapsed: %s" % time_hhmmss )
# ... use the timer again
my_timer.restart()
# ... do something
# Get time:
time_hhmmss = my_timer.get_time_hhmmss()
# ... etc
(With Ipython only) you can use %timeit to measure average processing time:
def foo():
print "hello"
and then:
%timeit foo()
the result is something like:
10000 loops, best of 3: 27 µs per loop
I like it simple (python 3):
from timeit import timeit
timeit(lambda: print("hello"))
Output is microseconds for a single execution:
2.430883963010274
Explanation:
timeit executes the anonymous function 1 million times by default and the result is given in seconds. Therefore the result for 1 single execution is the same amount but in microseconds on average.
For slow operations add a lower number of iterations or you could be waiting forever:
import time
timeit(lambda: time.sleep(1.5), number=1)
Output is always in seconds for the total number of iterations:
1.5015795179999714
on python3:
from time import sleep, perf_counter as pc
t0 = pc()
sleep(1)
print(pc()-t0)
elegant and short.
One more way to use timeit:
from timeit import timeit
def func():
return 1 + 1
time = timeit(func, number=1)
print(time)
To get insight on every function calls recursively, do:
%load_ext snakeviz
%%snakeviz
It just takes those 2 lines of code in a Jupyter notebook, and it generates a nice interactive diagram. For example:
Here is the code. Again, the 2 lines starting with % are the only extra lines of code needed to use snakeviz:
# !pip install snakeviz
%load_ext snakeviz
import glob
import hashlib
%%snakeviz
files = glob.glob('*.txt')
def print_files_hashed(files):
for file in files:
with open(file) as f:
print(hashlib.md5(f.read().encode('utf-8')).hexdigest())
print_files_hashed(files)
It also seems possible to run snakeviz outside notebooks. More info on the snakeviz website.
How to measure the time between two operations. Compare the time of two operations.
import time
b = (123*321)*123
t1 = time.time()
c = ((9999^123)*321)^123
t2 = time.time()
print(t2-t1)
7.987022399902344e-05
Here's a pretty well documented and fully type hinted decorator I use as a general utility:
from functools import wraps
from time import perf_counter
from typing import Any, Callable, Optional, TypeVar, cast
F = TypeVar("F", bound=Callable[..., Any])
def timer(prefix: Optional[str] = None, precision: int = 6) -> Callable[[F], F]:
"""Use as a decorator to time the execution of any function.
Args:
prefix: String to print before the time taken.
Default is the name of the function.
precision: How many decimals to include in the seconds value.
Examples:
>>> #timer()
... def foo(x):
... return x
>>> foo(123)
foo: 0.000...s
123
>>> #timer("Time taken: ", 2)
... def foo(x):
... return x
>>> foo(123)
Time taken: 0.00s
123
"""
def decorator(func: F) -> F:
#wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
nonlocal prefix
prefix = prefix if prefix is not None else f"{func.__name__}: "
start = perf_counter()
result = func(*args, **kwargs)
end = perf_counter()
print(f"{prefix}{end - start:.{precision}f}s")
return result
return cast(F, wrapper)
return decorator
Example usage:
from timer import timer
#timer(precision=9)
def takes_long(x: int) -> bool:
return x in (i for i in range(x + 1))
result = takes_long(10**8)
print(result)
Output:
takes_long: 4.942629056s
True
The doctests can be checked with:
$ python3 -m doctest --verbose -o=ELLIPSIS timer.py
And the type hints with:
$ mypy timer.py
If you want to be able to time functions conveniently, you can use a simple decorator:
import time
def timing_decorator(func):
def wrapper(*args, **kwargs):
start = time.perf_counter()
original_return_val = func(*args, **kwargs)
end = time.perf_counter()
print("time elapsed in ", func.__name__, ": ", end - start, sep='')
return original_return_val
return wrapper
You can use it on a function that you want to time like this:
#timing_decorator
def function_to_time():
time.sleep(1)
function_to_time()
Any time you call function_to_time, it will print how long it took and the name of the function being timed.
Kind of a super later response, but maybe it serves a purpose for someone. This is a way to do it which I think is super clean.
import time
def timed(fun, *args):
s = time.time()
r = fun(*args)
print('{} execution took {} seconds.'.format(fun.__name__, time.time()-s))
return(r)
timed(print, "Hello")
Keep in mind that "print" is a function in Python 3 and not Python 2.7. However, it works with any other function. Cheers!
You can use timeit.
Here is an example on how to test naive_func that takes parameter using Python REPL:
>>> import timeit
>>> def naive_func(x):
... a = 0
... for i in range(a):
... a += i
... return a
>>> def wrapper(func, *args, **kwargs):
... def wrapper():
... return func(*args, **kwargs)
... return wrapper
>>> wrapped = wrapper(naive_func, 1_000)
>>> timeit.timeit(wrapped, number=1_000_000)
0.4458435332577161
You don't need wrapper function if function doesn't have any parameters.
print_elapsed_time function is below
def print_elapsed_time(prefix=''):
e_time = time.time()
if not hasattr(print_elapsed_time, 's_time'):
print_elapsed_time.s_time = e_time
else:
print(f'{prefix} elapsed time: {e_time - print_elapsed_time.s_time:.2f} sec')
print_elapsed_time.s_time = e_time
use it in this way
print_elapsed_time()
.... heavy jobs ...
print_elapsed_time('after heavy jobs')
.... tons of jobs ...
print_elapsed_time('after tons of jobs')
result is
after heavy jobs elapsed time: 0.39 sec
after tons of jobs elapsed time: 0.60 sec
the pros and cons of this function is that you don't need to pass start time
We can also convert time into human-readable time.
import time, datetime
start = time.clock()
def num_multi1(max):
result = 0
for num in range(0, 1000):
if (num % 3 == 0 or num % 5 == 0):
result += num
print "Sum is %d " % result
num_multi1(1000)
end = time.clock()
value = end - start
timestamp = datetime.datetime.fromtimestamp(value)
print timestamp.strftime('%Y-%m-%d %H:%M:%S')
Although it's not strictly asked in the question, it is quite often the case that you want a simple, uniform way to incrementally measure the elapsed time between several lines of code.
If you are using Python 3.8 or above, you can make use of assignment expressions (a.k.a. the walrus operator) to achieve this in a fairly elegant way:
import time
start, times = time.perf_counter(), {}
print("hello")
times["print"] = -start + (start := time.perf_counter())
time.sleep(1.42)
times["sleep"] = -start + (start := time.perf_counter())
a = [n**2 for n in range(10000)]
times["pow"] = -start + (start := time.perf_counter())
print(times)
=>
{'print': 2.193450927734375e-05, 'sleep': 1.4210970401763916, 'power': 0.005671024322509766}
I made a library for this, if you want to measure a function you can just do it like this
from pythonbenchmark import compare, measure
import time
a,b,c,d,e = 10,10,10,10,10
something = [a,b,c,d,e]
#measure
def myFunction(something):
time.sleep(0.4)
#measure
def myOptimizedFunction(something):
time.sleep(0.2)
myFunction(input)
myOptimizedFunction(input)
https://github.com/Karlheinzniebuhr/pythonbenchmark
This unique class-based approach offers a printable string representation, customizable rounding, and convenient access to the elapsed time as a string or a float. It was developed with Python 3.7.
import datetime
import timeit
class Timer:
"""Measure time used."""
# Ref: https://stackoverflow.com/a/57931660/
def __init__(self, round_ndigits: int = 0):
self._round_ndigits = round_ndigits
self._start_time = timeit.default_timer()
def __call__(self) -> float:
return timeit.default_timer() - self._start_time
def __str__(self) -> str:
return str(datetime.timedelta(seconds=round(self(), self._round_ndigits)))
Usage:
# Setup timer
>>> timer = Timer()
# Access as a string
>>> print(f'Time elapsed is {timer}.')
Time elapsed is 0:00:03.
>>> print(f'Time elapsed is {timer}.')
Time elapsed is 0:00:04.
# Access as a float
>>> timer()
6.841332235
>>> timer()
7.970274425
(Python 2.7.8, Windows)
there is so many questions already about this particular subject, but I cannot seem to get any of them working.
So, what I'm trying to accomplish is timing how long a function takes to execute.
I have functions.py and main.py in following fashion:
#functions.py
def function(list):
does something
return list
...
#main.py
import functions
...stuff...
while:
list = gets list from file
functions.function(list) <--- this needs to get timed
Now I tried time.time() the start and end points first, but it's not accurate enough (difference tends to be 0.0), and after some googling it seems that this isn't the way to go anyway. Apparently what I should use(?) is timeit module. However I cannot understand how to get the function into it.
Any help?
As you mentioned, there's a Python module made for this very task, timeit. Its syntax, while a little idiosyncratic, is quite easy to understand:
timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000)
stmt is the function call to be measured, in your case: functions.function(list)
setup is the code you need to create the context necessary for stmt to execute, in your case: import functions; list = gets list from file
number is how many time timeit would run stmt to find its average execution time. You might want to change the number, since calling your function a million times might take a while.
tl;dr:
timeit.timeit(stmt='functions.function(list)', setup='import functions; list = gets list from file', number=100)
you see this demo: time.time
>>> def check(n):
... start = time.time()
... for x in range(n):
... pass
... stop = time.time()
... return stop-start
...
>>> check(1000)
0.0001239776611328125
>>> check(10000)
0.0012159347534179688
>>> check(100)
1.71661376953125e-05
the above function returns hum much time in sec taken by for for n loops.
so the algorithm is:
start = time.time()
# your stuff
stop = time.time()
time_taken = stop - start
start = timeit.default_timer()
my_function()
elapsed = timeit.default_timer() - start
I'm programming in python on windows and would like to accurately measure the time it takes for a function to run. I have written a function "time_it" that takes another function, runs it, and returns the time it took to run.
def time_it(f, *args):
start = time.clock()
f(*args)
return (time.clock() - start)*1000
i call this 1000 times and average the result. (the 1000 constant at the end is to give the answer in milliseconds.)
This function seems to work but i have this nagging feeling that I'm doing something wrong, and that by doing it this way I'm using more time than the function actually uses when its running.
Is there a more standard or accepted way to do this?
When i changed my test function to call a print so that it takes longer, my time_it function returns an average of 2.5 ms while the cProfile.run('f()') returns and average of 7.0 ms. I figured my function would overestimate the time if anything, what is going on here?
One additional note, it is the relative time of functions compared to each other that i care about, not the absolute time as this will obviously vary depending on hardware and other factors.
Use the timeit module from the Python standard library.
Basic usage:
from timeit import Timer
# first argument is the code to be run, the second "setup" argument is only run once,
# and it not included in the execution time.
t = Timer("""x.index(123)""", setup="""x = range(1000)""")
print t.timeit() # prints float, for example 5.8254
# ..or..
print t.timeit(1000) # repeat 1000 times instead of the default 1million
Instead of writing your own profiling code, I suggest you check out the built-in Python profilers (profile or cProfile, depending on your needs): http://docs.python.org/library/profile.html
You can create a "timeme" decorator like so
import time
def timeme(method):
def wrapper(*args, **kw):
startTime = int(round(time.time() * 1000))
result = method(*args, **kw)
endTime = int(round(time.time() * 1000))
print(endTime - startTime,'ms')
return result
return wrapper
#timeme
def func1(a,b,c = 'c',sleep = 1):
time.sleep(sleep)
print(a,b,c)
func1('a','b','c',0)
func1('a','b','c',0.5)
func1('a','b','c',0.6)
func1('a','b','c',1)
This code is very inaccurate
total= 0
for i in range(1000):
start= time.clock()
function()
end= time.clock()
total += end-start
time= total/1000
This code is less inaccurate
start= time.clock()
for i in range(1000):
function()
end= time.clock()
time= (end-start)/1000
The very inaccurate suffers from measurement bias if the run-time of the function is close to the accuracy of the clock. Most of the measured times are merely random numbers between 0 and a few ticks of the clock.
Depending on your system workload, the "time" you observe from a single function may be entirely an artifact of OS scheduling and other uncontrollable overheads.
The second version (less inaccurate) has less measurement bias. If your function is really fast, you may need to run it 10,000 times to damp out OS scheduling and other overheads.
Both are, of course, terribly misleading. The run time for your program -- as a whole -- is not the sum of the function run-times. You can only use the numbers for relative comparisons. They are not absolute measurements that convey much meaning.
If you want to time a python method even if block you measure may throw, one good approach is to use with statement. Define some Timer class as
import time
class Timer:
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, *args):
self.end = time.clock()
self.interval = self.end - self.start
Then you may want to time a connection method that may throw. Use
import httplib
with Timer() as t:
conn = httplib.HTTPConnection('google.com')
conn.request('GET', '/')
print('Request took %.03f sec.' % t.interval)
__exit()__ method will be called even if the connection request thows. More precisely, you'd have you use try finally to see the result in case it throws, as with
try:
with Timer() as t:
conn = httplib.HTTPConnection('google.com')
conn.request('GET', '/')
finally:
print('Request took %.03f sec.' % t.interval)
More details here.
This is neater
from contextlib import contextmanager
import time
#contextmanager
def timeblock(label):
start = time.clock()
try:
yield
finally:
end = time.clock()
print ('{} : {}'.format(label, end - start))
with timeblock("just a test"):
print "yippee"
Similar to #AlexMartelli's answer
import timeit
timeit.timeit(fun, number=10000)
can do the trick.