哪种方法更快?就像他们俩不一样吗?
start = time.time()
arr = np.array([1,2,3,4,5,6,7,8,9,0,12])
total_price = np.sum(arr[arr < 7])* 2.14
print(total_price)
print('Duration: {} seconds'.format(time.time() - start))
start = time.time()
arr = np.array([1,2,3,4,5,6,7,8,9,0,12])
total_price = (arr[arr<7]).sum()* 2.14
print(total_price)
print('Duration: {} seconds'.format(time.time() - start))
在运行代码时,它们一次又一次地给出不同的结果执行时间。有时,前一种方法更快,而有时则更高。
np.sum
的代码是
def sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
initial=np._NoValue, where=np._NoValue):
if isinstance(a, _gentype):
# 2018-02-25, 1.15.0
warnings.warn(
"Calling np.sum(generator) is deprecated, and in the future will give a different result. "
"Use np.sum(np.fromiter(generator)) or the python sum builtin instead.",
DeprecationWarning, stacklevel=3)
res = _sum_(a)
if out is not None:
out[...] = res
return out
return res
return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
initial=initial, where=where)
因此,它将对参数进行一些检查,然后将任务传递给add.reduce
。 sum
方法是“内置”的,但是在编译后的代码中必须做类似的事情。
在这些测试中,计算时间本身足够短,以至于调用方法有所不同:
In [607]: timeit np.sum(np.arange(1000))
15.4 µs ± 42.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [608]: timeit np.arange(1000).sum()
12.2 µs ± 29.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [609]: timeit np.add.reduce(np.arange(1000))
9.19 µs ± 17.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
numpy
具有许多这样的功能/方法对。使用最方便的方式-在您的代码中看起来最漂亮!