python几种用法的性能比较1.5，,import tim

python几种用法的性能比较1.5，,import tim

import timeitsum_by_for = """for d in data:    s += d"""sum_by_sum = """sum(data)"""sum_by_numpy_sum = """import numpynumpy.sum(data)"""def timeit_using_list(n, loops):    list_setup = """data =[1] * {}s = 0""".format(n)    print(‘list result:‘)    print(timeit.timeit(sum_by_for, list_setup, number = loops))    print(timeit.timeit(sum_by_sum, list_setup, number = loops))    print(timeit.timeit(sum_by_numpy_sum, list_setup, number = loops))def timeit_using_array(n, loops):    array_setup = """import arraydata = array.array(‘L‘, [1] * {})s = 0""".format(n)    print(‘array result:‘)    print(timeit.timeit(sum_by_for, array_setup, number = loops))    print(timeit.timeit(sum_by_sum, array_setup, number = loops))    print(timeit.timeit(sum_by_numpy_sum, array_setup, number = loops))def timeit_using_numpy(n, loops):    numpy_setup = """import numpydata = numpy.array([1] * {})s = 0""".format(n)    print(‘numpy result:‘)    print(timeit.timeit(sum_by_for, numpy_setup, number = loops))    print(timeit.timeit(sum_by_sum, numpy_setup, number = loops))    print(timeit.timeit(sum_by_numpy_sum, numpy_setup, number = loops))if __name__ == ‘__main__‘:    timeit_using_list(30000, 500)    timeit_using_array(30000, 500)    timeit_using_numpy(30000, 500)

python几种用法的性能比较1.5