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
Here is how you can test which piece of code is faster: % python -mtimeit “l=[]” 10000000 loops, best of 3: 0.0711 usec per loop % python -mtimeit “l=list()” 1000000 loops, best of 3: 0.297 usec per loop However, in practice, this initialization is most likely an extremely small part of your program, so worrying … Read more
Change this line: t = timeit.Timer(“foo()”) To this: t = timeit.Timer(“foo()”, “from __main__ import foo”) Check out the link you provided at the very bottom. To give the timeit module access to functions you define, you can pass a setup parameter which contains an import statement: I just tested it on my machine and it … Read more
Yes, this is expected; the Counter() constructor uses Counter.update() which uses self.get() to load initial values rather than rely on __missing__. Moreover, the defaultdict __missing__ factory is handled entirely in C code, especially when using another type like int() that is itself implemented in C. The Counter source is pure Python and as such the … Read more
%timeit is an IPython magic function, which can be used to time a particular piece of code (a single execution statement, or a single method). From the documentation: %timeit Time execution of a Python statement or expression Usage, in line mode: %timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] statement To use it, for example if … Read more
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 … Read more
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
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
You can use time.time() or time.clock() before and after the block you want to time. import time t0 = time.time() code_block t1 = time.time() total = t1-t0 This method is not as exact as timeit (it does not average several runs) but it is straightforward. time.time() (in Windows and Linux) and time.clock() (in Linux) are … Read more
If you want to use timeit in an interactive Python session, there are two convenient options: Use the IPython shell. It features the convenient %timeit special function: In [1]: def f(x): …: return x*x …: In [2]: %timeit for x in range(100): f(x) 100000 loops, best of 3: 20.3 us per loop In a standard … Read more