It specifies the number of repeats, the number of repeats are used to determine the average. For example: %timeit -n 250 a = 2 # 61.9 ns ± 1.01 ns per loop (mean ± std. dev. of 7 runs, 250 loops each) %timeit -n 250 -r 2 a = 2 # 62.6 ns ± 0 … Read more
Expanding on Paulo’s answer, generator expressions are often slower than list comprehensions because of the overhead of function calls. In this case, the short-circuiting behavior of in offsets that slowness if the item is found fairly early, but otherwise, the pattern holds. I ran a simple script through the profiler for a more detailed analysis. … Read more
True and False are not keywords in Python 2. They must resolve at runtime. This has been changed in Python 3 Same test on Python 3: >>> timeit.timeit(‘test1()’,setup=”from __main__ import test1″, number=10000000) 2.806439919999889 >>> timeit.timeit(‘test2()’,setup=”from __main__ import test2″, number=10000000) 2.801301520000038 >>> timeit.timeit(‘test3()’,setup=”from __main__ import test3″, number=10000000) 2.7952816800000164 >>> timeit.timeit(‘test4()’,setup=”from __main__ import test4″, number=10000000) 2.7862537199999906 Time … Read more
For Python 3.5 you can override the value of timeit.template timeit.template = “”” def inner(_it, _timer{init}): {setup} _t0 = _timer() for _i in _it: retval = {stmt} _t1 = _timer() return _t1 – _t0, retval “”” unutbu’s answer works for python 3.4 but not 3.5 as the _template_func function appears to have been removed in … Read more
The return value is seconds as a float. It is the total time taken to run the test (not counting the setup), so the average time per test is that number divided by the number argument, which defaults to 1 million. See the Time.timeit() documentation: Time number executions of the main statement. This executes the … Read more
The short answer to your question is that each of these three methods of reading bits of a file have different use cases. As noted above, f.read() reads the file as an individual string, and so allows relatively easy file-wide manipulations, such as a file-wide regex search or substitution. f.readline() reads a single line of … Read more
According to the Python documentation, it has to do with the accuracy of the time function in different operating systems: The default timer function is platform dependent. On Windows, time.clock() has microsecond granularity but time.time()‘s granularity is 1/60th of a second; on Unix, time.clock() has 1/100th of a second granularity and time.time() is much more … Read more
As Martijn commented, the difference is Python’s garbage collection, which timeit.timeit disables during its run. And zip creates 10 million iterator objects, one for each of the 10 million iterables it’s given. So, garbage-collecting 10 million objects simply takes a lot of time, right? Mystery solved! Well… no. That’s not really what happens, and it’s … Read more
There is such functionality in latest version of requests: https://requests.readthedocs.io/en/latest/api/?highlight=elapsed#requests.Response.elapsed For example: requests.get(“http://127.0.0.1”).elapsed.total_seconds()
Use wrapping from functools to improve Matt Alcock’s answer. from functools import wraps from time import time def timing(f): @wraps(f) def wrap(*args, **kw): ts = time() result = f(*args, **kw) te = time() print ‘func:%r args:[%r, %r] took: %2.4f sec’ % \ (f.__name__, args, kw, te-ts) return result return wrap In an example: @timing def … Read more