我需要将元组的NP数组(简称)转换为整数的NParray.
最明显的方法不起作用:
# array_of_tuples is given, this is just an example:
array_of_tuples = np.zeros(2, dtype=object)
array_of_tuples[0] = 1,2
array_of_tuples[1] = 2,3
np.array(array_of_tuples, dtype=int)
ValueError: setting an array element with a sequence.
它看起来像是将元组放到一个预先分配的固定大小的缓冲区中，而dtype是可行的方法.它似乎避免了大量与计算大小、粗糙程度和数据类型相关的开销.
以下是一些速度较慢的替代方案和一个基准:
您可以欺骗并创建具有所需数量的字段的dtype，因为NumPy支持将元组转换为定制数据类型:
dt = np.dtype([('', int) for _ in range(len(array_of_tuples[0]))])
res = np.empty((len(array_of_tuples), len(array_of_tuples[0])), int)
res.view(dt).ravel()[:] = array_of_tuples
您可以堆叠数组:
np.stack(array_of_tuples, axis=0)
不幸的是，这比其他提出的方法还要慢.
预分配没有多大帮助:
res = np.empty((len(array_of_tuples), len(array_of_tuples[0])), int)
np.stack(array_of_tuples, out=res, axis=0)
try 使用np.concatenate作弊，它允许您指定输出数据类型，也不会有太大帮助:
np.concatenate(array_of_tuples, dtype=int).reshape(len(array_of_tuples), len(array_of_tuples[0]))
预分配数组也不会:
res = np.empty((len(array_of_tuples), len(array_of_tuples[0])), int)
np.concatenate(array_of_tuples, out=res.ravel())
您也可以try 在python空间中进行连接，这也很慢:
np.array(sum(array_of_tuples, start=()), dtype=int).reshape(len(array_of_tuples), len(array_of_tuples[0]))
或
np.reshape(np.sum(array_of_tuples), (len(array_of_tuples), len(array_of_tuples[0])))
array_of_tuples = np.empty(100, dtype=object)
for i in range(len(array_of_tuples)):
array_of_tuples[i] = tuple(range(i, i + 100))
%%timeit
res = np.empty((len(array_of_tuples), len(array_of_tuples[0])), int)
for i, res[i] in enumerate(array_of_tuples):
pass
305 µs ± 8.55 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
dt = np.dtype([('', 'int',) for _ in range(100)])
%%timeit
res = np.empty((100, 100), int)
res.view(dt).ravel()[:] = array_of_tuples
334 µs ± 5.59 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit np.array(array_of_tuples.tolist())
478 µs ± 12.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%%timeit
res = np.empty((100, 100), int)
np.concatenate(array_of_tuples, out=res.ravel())
500 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit np.concatenate(array_of_tuples, dtype=int).reshape(100, 100)
504 µs ± 7.72 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%%timeit
res = np.empty((100, 100), int)
np.stack(array_of_tuples, out=res, axis=0)
557 µs ± 25.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit np.stack(array_of_tuples, axis=0)
577 µs ± 6.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit np.array(sum(array_of_tuples, start=()), dtype=int).reshape(100, 100)
1.06 ms ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%timeit np.reshape(np.sum(array_of_tuples), (100, 100))
1.26 ms ± 24.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)