What I want is to start counting time somewhere in my code and then get the passed time, to measure the time it took to execute few function. I think I'm using the timeit module wrong, but the docs are just confusing for me.
import timeit
start = timeit.timeit()
print("hello")
end = timeit.timeit()
print(end - start)
This question is related to
python
performance
measure
timeit
The answer is
One more way to use timeit:
from timeit import timeit
def func():
return 1 + 1
time = timeit(func, number=1)
print(time)
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!
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.
based on the contextmanager solution given by https://stackoverflow.com/a/30024601/5095636, hereunder the lambda free version, as flake8 warns on the usage of lambda as per E731:
from contextlib import contextmanager
from timeit import default_timer
@contextmanager
def elapsed_timer():
start_time = default_timer()
class _Timer():
start = start_time
end = default_timer()
duration = end - start
yield _Timer
end_time = default_timer()
_Timer.end = end_time
_Timer.duration = end_time - start_time
test:
from time import sleep
with elapsed_timer() as t:
print("start:", t.start)
sleep(1)
print("end:", t.end)
t.start
t.end
t.duration
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
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
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:
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.
If you want to be able to time functions conveniently, you can use a simple decorator:
def timing_decorator(func):
def wrapper(*args, **kwargs):
start = time.time()
original_return_val = func(*args, **kwargs)
end = time.time()
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)
Then any time you call function_to_time, it will print how long it took and the name of the function being timed.
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
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.
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}
If you just want to measure the elapsed wall-clock time between two points, you could use time.time():
import time
start = time.time()
print("hello")
end = time.time()
print(end - start)
This gives the execution time in seconds.
Another option since 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()orprocess_time()instead, depending on your requirements, to have a well defined behaviour.
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)
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
The timeit module is good for timing a small piece of Python code. It can be used at least in three forms:
1- As a command-line module
python2 -m timeit 'for i in xrange(10): oct(i)'
2- For a short code, pass it as arguments.
import timeit
timeit.Timer('for i in xrange(10): oct(i)').timeit()
3- For longer code as:
import timeit
code_to_test = """
a = range(100000)
b = []
for i in a:
b.append(i*2)
"""
elapsed_time = timeit.timeit(code_to_test, number=100)/100
print(elapsed_time)
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:
timeitselects the best timer available on your OS and Python version.timeitdisables 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 tand thent.elapsed). - Ability to keep gc enabled for block timing.
import time
def getElapsedTime(startTime, units):
elapsedInSeconds = time.time() - startTime
if units == 'sec':
return elapsedInSeconds
if units == 'min':
return elapsedInSeconds/60
if units == 'hour':
return elapsedInSeconds/(60*60)
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')
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
Measuring time in seconds:
from timeit import default_timer as timer
from datetime import timedelta
start = timer()
end = timer()
print(timedelta(seconds=end-start))
Output:
0:00:01.946339
Time can also be measured by %timeit magic function as follow:
%timeit -t -n 1 print("hello")
n 1 is for running function only 1 time.
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
Measure execution time of small code snippets.
Unit of time: measured in seconds as a float
import timeit
t = timeit.Timer('li = list(map(lambda x:x*2,[1,2,3,4,5]))')
t.timeit()
t.repeat()
>[1.2934070999999676, 1.3335035000000062, 1.422568500000125]
The repeat() method is a convenience to call timeit() multiple times and return a list of results.
repeat(repeat=3)¶With this list we can take a mean of all times.
By default, timeit() temporarily turns off garbage collection during the timing. time.Timer() solves this problem.
Pros:
timeit.Timer() makes independent timings more comparable. The gc may be an important component of the performance of the function being measured. If so, gc(garbage collector) can be re-enabled as the first statement in the setup string. For example:
timeit.Timer('li = list(map(lambda x:x*2,[1,2,3,4,5]))',setup='gc.enable()')
Source Python Docs!
(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
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.
In addition to %timeit in ipython you can also use %%timeit for multi-line code snippets:
In [1]: %%timeit
...: complex_func()
...: 2 + 2 == 5
...:
...:
1 s ± 1.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Also it can be used in jupyter notebook the same way, just put magic %%timeit at the beginning of cell.
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:
The only way I can think of is using time.time().
import time
start = time.time()
sleep(5) #just to give it some delay to show it working
finish = time.time()
elapsed = finish - start
print(elapsed)
Hope that will help.
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.
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
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.
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()
print(time.ctime())
<operations, programs>
print('minutes: ',(time.monotonic() - start_time)/60)
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).
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
on python3:
from time import sleep, perf_counter as pc
t0 = pc()
sleep(1)
print(pc()-t0)
elegant and short.
Source: Stackoverflow.com