高效的python數組到numpy數組轉換

Question

我從python標准庫得到一個數組格式的大數組（圖像為12 Mpix）。 由於我想對這些數組執行操作，我希望將其轉換為numpy數組。 我嘗試了以下方法：

import numpy
import array
from datetime import datetime
test = array.array('d', [0]*12000000)
t = datetime.now()
numpy.array(test)
print datetime.now() - t

我得到一到兩秒之間的結果：相當於python中的循環。

有沒有更有效的方法來進行這種轉換？

Answer 1

np.array(test)                                       # 1.19s

np.fromiter(test, dtype=int)                         # 1.08s

np.frombuffer(test)                                  # 459ns !!!

Answer 2

asarray(x)幾乎總是任何類似數組的對象的最佳選擇。

array和fromiter很慢，因為它們執行副本。 使用asarray可以省略此副本：

>>> import array
>>> import numpy as np
>>> test = array.array('d', [0]*12000000)

# very slow - this makes multiple copies that grow each time
>>> %timeit np.fromiter(test, dtype=test.typecode)
626 ms ± 3.97 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# fast memory copy
>>> %timeit np.array(test)
63.5 ms ± 639 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

# which is equivalent to doing the fast construction followed by a copy
>>> %timeit np.asarray(test).copy()
63.4 ms ± 371 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

# so doing just the construction is way faster
>>> %timeit np.asarray(test)
1.73 µs ± 70.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

# marginally faster, but at the expense of verbosity and type safety if you
# get the wrong type
>>> %timeit np.frombuffer(test, dtype=test.typecode)
1.07 µs ± 27.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)