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:

1. timeit selects the best timer available on your OS and Python version.
2. 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:

1. Use timer from timeit instead of time.time for reasons described earlier.
2. You can disable GC during timing if you want.
3. Decorator accepts functions with named or unnamed params.
4. Ability to disable printing in block timing (use with timing.MeasureBlockTime() as t and then t.elapsed).
5. Ability to keep gc enabled for block timing.

on python3:

from time import sleep, perf_counter as pc t0 = pc() sleep(1) print(pc()-t0) 

elegant and short.

output:

1.001345009999568 

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.

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.

Here is an answer using:

• a concise context manager to time code snippets
• time.perf_counter() to compute time delta. It should be preferred as it is not adjustable (neither a sysadmin nor a daemon can change its value) contrary to time.time() (see doc)

import time from collections.abc import Iterator from contextlib import contextmanager @contextmanager def time_it() -> Iterator[None]: tic: float = time.perf_counter() try: yield finally: toc: float = time.perf_counter() print(f"Computation time = {1000*(toc - tic):.3f}ms") 

An example how to use it:

# Example: vector dot product computation with time_it(): A = B = range(1_000_000) dot = sum(a*b for a,b in zip(A,B)) # Computation time = 95.353ms 

Appendix

import time # to check adjustability assert time.get_clock_info('time').adjustable assert time.get_clock_info('perf_counter').adjustable is False 

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

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.

(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 

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 

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 

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

One more way to use timeit:

from timeit import timeit def func(): return 1 + 1 time = timeit(func, number=1) print(time) 

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 

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.

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

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.

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