http:\/\/search.aol.com\/aol\/search?q=python+running+performance+analysis<\/a><\/p>\n Python includes a profiler called cProfile. It not only gives the total running time, but also times each function separately, and tells you how many times each function was called, making it easy to determine where you should make optimizations.<\/p>\n You can call it from within your code, or from the interpreter, like this<\/strong><\/span>:<\/p>\n Even more usefully, you can invoke the cProfile when running a script<\/strong><\/span>:<\/p>\n To make it even easier, I made a little batch file called ‘profile.bat’:<\/p>\n So all I have to do is run:<\/p>\n And I get this:<\/p>\n EDIT: Updated link to a good video resource from PyCon 2013: http:\/\/lanyrd.com\/2013\/pycon\/scdywg\/<\/a><\/p>\n ==<\/p>\n http:\/\/search.aol.com\/aol\/search?q=python+memory+leakage<\/a><\/p>\n ==<\/p>\n Given test.py:<\/p>\n run timeit like this:<\/p>\n The following example shows how the Command-Line Interface can be used to compare three different expressions:<\/p>\n This can be achieved from the Python Interface with:<\/p>\n Note however that timeit will automatically determine the number of repetitions only when the command-line interface is used. In the Examples section you can find more advanced examples.<\/p>\n\u53c2\u8003\u7ed3\u8bba\uff1a<\/h6>\n
import cProfile\ncProfile.run('foo()')<\/pre>\n$ python -m cProfile myscript.py<\/pre>\n
python -m cProfile %1<\/pre>\n
$ profile euler048.py<\/pre>\n
1007 function calls in 0.061 CPU seconds\n\nOrdered by: standard name\nncalls tottime percall cumtime percall filename:lineno(function)\n 1 0.000 0.000 0.061 0.061 <string>:1(<module>)\n 1000 0.051 0.000 0.051 0.000 euler048.py:2(<lambda>)\n 1 0.005 0.005 0.061 0.061 euler048.py:2(<module>)\n 1 0.000 0.000 0.061 0.061 {execfile}\n 1 0.002 0.002 0.053 0.053 {map}\n 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler objects}\n 1 0.000 0.000 0.000 0.000 {range}\n 1 0.003 0.003 0.003 0.003 {sum}<\/pre>\n\u53c2\u8003\u94fe\u63a5\uff1a<\/h6>\n
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...\ndef InsertionSort():\n\t...\ndef TimSort():\n\t...<\/pre>\n
% python -mtimeit -s'import test' 'test.InsertionSort()'\n% python -mtimeit -s'import test' 'test.TimSort()'<\/pre>\n
$ python -m timeit '\"-\".join(str(n) for n in range(100))'\n10000 loops, best of 3: 40.3 usec per loop\n$ python -m timeit '\"-\".join([str(n) for n in range(100)])'\n10000 loops, best of 3: 33.4 usec per loop\n$ python -m timeit '\"-\".join(map(str, range(100)))'\n10000 loops, best of 3: 25.2 usec per loop<\/pre>\n
>>> import timeit\n>>> timeit.timeit('\"-\".join(str(n) for n in range(100))', number=10000)\n0.8187260627746582\n>>> timeit.timeit('\"-\".join([str(n) for n in range(100)])', number=10000)\n0.7288308143615723\n>>> timeit.timeit('\"-\".join(map(str, range(100)))', number=10000)\n0.5858950614929199<\/pre>\n