将数据从数组b复制到数组a的最快方法是什么，而不修改数组a的地址。我需要这个，因为外部库（PyFFTW）使用指向我的数组的指针，该指针无法更改。
例如：
a = numpy.empty(n, dtype=complex)
for i in xrange(a.size):
a[i] = b[i]
没有循环可以做到吗？
答案 0 :(得分：78)
我相信
a = numpy.empty_like (b)
a[:] = b
将快速制作深层照片。正如Funsi所提到的，numpy的最新版本也具有copyto功能。
答案 1 :(得分：24)
numpy版本1.7具有numpy.copyto函数，可以执行您要查找的内容：
numpy.copyto（dst，src）
将值从一个数组复制到另一个数组，＆gt;必要时播放。
请参阅： http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.copyto.html
答案 2 :(得分：16)
a = numpy.array(b)
甚至比numpy v1.6的建议解决方案更快，并且也制作了数组的副本。然而，我可以不对copyto（a，b）进行测试，因为我没有numpy的最新版本。
答案 3 :(得分：11)
为了回答你的问题，我玩了一些变种并对它们进行了分析。
结论：要将数据从numpy数组复制到另一个数组，请尽可能使用内置的numpy函数numpy.array(src)或numpy.copyto(dst, src)。
（但是如果已经分配了dst的内存，则总是选择更晚的内存。重新使用内存。请参阅帖子末尾的分析。）
分析设置
import timeit
import numpy as np
import pandas as pd
from IPython.display import display
def profile_this(methods, setup='', niter=10 ** 4, p_globals=None, **kwargs):
if p_globals is not None:
print('globals: {0}, tested {1:.0e} times'.format(p_globals, niter))
timings = np.array([timeit.timeit(method, setup=setup, number=niter,
globals=p_globals, **kwargs) for
method in methods])
ranking = np.argsort(timings)
timings = np.array(timings)[ranking]
methods = np.array(methods)[ranking]
speedups = np.amax(timings) / timings
pd.set_option('html', False)
data = {'time (s)': timings,
'speedup': ['{:.2f}x'.format(s) if 1 != s else '' for s in speedups],
'methods': methods}
data_frame = pd.DataFrame(data, columns=['time (s)', 'speedup', 'methods'])
display(data_frame)
print()
个人资料代码
setup = '''import numpy as np; x = np.random.random(n)'''
methods = (
'''y = np.zeros(n, dtype=x.dtype); y[:] = x''',
'''y = np.zeros_like(x); y[:] = x''',
'''y = np.empty(n, dtype=x.dtype); y[:] = x''',
'''y = np.empty_like(x); y[:] = x''',
'''y = np.copy(x)''',
'''y = x.astype(x.dtype)''',
'''y = 1*x''',
'''y = np.empty_like(x); np.copyto(y, x)''',
'''y = np.empty_like(x); np.copyto(y, x, casting='no')''',
'''y = np.empty(n)\nfor i in range(x.size):\n\ty[i] = x[i]'''
)
for n, it in ((2, 6), (3, 6), (3.8, 6), (4, 6), (5, 5), (6, 4.5)):
profile_this(methods[:-1:] if n > 2 else methods, setup,
niter=int(10 ** it), p_globals={'n': int(10 ** n)})
结果，CPython v3.5.0，numpy v1.10.1。
globals: {'n': 100}, tested 1e+06 times
time (s) speedup methods
0 0.386908 33.76x y = np.array(x)
1 0.496475 26.31x y = x.astype(x.dtype)
2 0.567027 23.03x y = np.empty_like(x); np.copyto(y, x)
3 0.666129 19.61x y = np.empty_like(x); y[:] = x
4 0.967086 13.51x y = 1*x
5 1.067240 12.24x y = np.empty_like(x); np.copyto(y, x, casting=...
6 1.235198 10.57x y = np.copy(x)
7 1.624535 8.04x y = np.zeros(n, dtype=x.dtype); y[:] = x
8 1.626120 8.03x y = np.empty(n, dtype=x.dtype); y[:] = x
9 3.569372 3.66x y = np.zeros_like(x); y[:] = x
10 13.061154 y = np.empty(n)\nfor i in range(x.size):\n\ty[...
globals: {'n': 1000}, tested 1e+06 times
time (s) speedup methods
0 0.666237 6.10x y = x.astype(x.dtype)
1 0.740594 5.49x y = np.empty_like(x); np.copyto(y, x)
2 0.755246 5.39x y = np.array(x)
3 1.043631 3.90x y = np.empty_like(x); y[:] = x
4 1.398793 2.91x y = 1*x
5 1.434299 2.84x y = np.empty_like(x); np.copyto(y, x, casting=...
6 1.544769 2.63x y = np.copy(x)
7 1.873119 2.17x y = np.empty(n, dtype=x.dtype); y[:] = x
8 2.355593 1.73x y = np.zeros(n, dtype=x.dtype); y[:] = x
9 4.067133 y = np.zeros_like(x); y[:] = x
globals: {'n': 6309}, tested 1e+06 times
time (s) speedup methods
0 2.338428 3.05x y = np.array(x)
1 2.466636 2.89x y = x.astype(x.dtype)
2 2.561535 2.78x y = np.empty_like(x); np.copyto(y, x)
3 2.603601 2.74x y = np.empty_like(x); y[:] = x
4 3.005610 2.37x y = np.empty_like(x); np.copyto(y, x, casting=...
5 3.215863 2.22x y = np.copy(x)
6 3.249763 2.19x y = 1*x
7 3.661599 1.95x y = np.empty(n, dtype=x.dtype); y[:] = x
8 6.344077 1.12x y = np.zeros(n, dtype=x.dtype); y[:] = x
9 7.133050 y = np.zeros_like(x); y[:] = x
globals: {'n': 10000}, tested 1e+06 times
time (s) speedup methods
0 3.421806 2.82x y = np.array(x)
1 3.569501 2.71x y = x.astype(x.dtype)
2 3.618747 2.67x y = np.empty_like(x); np.copyto(y, x)
3 3.708604 2.61x y = np.empty_like(x); y[:] = x
4 4.150505 2.33x y = np.empty_like(x); np.copyto(y, x, casting=...
5 4.402126 2.19x y = np.copy(x)
6 4.917966 1.96x y = np.empty(n, dtype=x.dtype); y[:] = x
7 4.941269 1.96x y = 1*x
8 8.925884 1.08x y = np.zeros(n, dtype=x.dtype); y[:] = x
9 9.661437 y = np.zeros_like(x); y[:] = x
globals: {'n': 100000}, tested 1e+05 times
time (s) speedup methods
0 3.858588 2.63x y = x.astype(x.dtype)
1 3.873989 2.62x y = np.array(x)
2 3.896584 2.60x y = np.empty_like(x); np.copyto(y, x)
3 3.919729 2.58x y = np.empty_like(x); np.copyto(y, x, casting=...
4 3.948563 2.57x y = np.empty_like(x); y[:] = x
5 4.000521 2.53x y = np.copy(x)
6 4.087255 2.48x y = np.empty(n, dtype=x.dtype); y[:] = x
7 4.803606 2.11x y = 1*x
8 6.723291 1.51x y = np.zeros_like(x); y[:] = x
9 10.131983 y = np.zeros(n, dtype=x.dtype); y[:] = x
globals: {'n': 1000000}, tested 3e+04 times
time (s) speedup methods
0 85.625484 1.24x y = np.empty_like(x); y[:] = x
1 85.693316 1.24x y = np.empty_like(x); np.copyto(y, x)
2 85.790064 1.24x y = np.empty_like(x); np.copyto(y, x, casting=...
3 86.342230 1.23x y = np.empty(n, dtype=x.dtype); y[:] = x
4 86.954862 1.22x y = np.zeros(n, dtype=x.dtype); y[:] = x
5 89.503368 1.18x y = np.array(x)
6 91.986177 1.15x y = 1*x
7 95.216021 1.11x y = np.copy(x)
8 100.524358 1.05x y = x.astype(x.dtype)
9 106.045746 y = np.zeros_like(x); y[:] = x
此外，请查看在值复制期间目标的内存已经预分配的分析变体的结果，因为y = np.empty_like(x)是设置的一部分：< / p>
globals: {'n': 100}, tested 1e+06 times
time (s) speedup methods
0 0.328492 2.33x np.copyto(y, x)
1 0.384043 1.99x y = np.array(x)
2 0.405529 1.89x y[:] = x
3 0.764625 np.copyto(y, x, casting='no')
globals: {'n': 1000}, tested 1e+06 times
time (s) speedup methods
0 0.453094 1.95x np.copyto(y, x)
1 0.537594 1.64x y[:] = x
2 0.770695 1.15x y = np.array(x)
3 0.884261 np.copyto(y, x, casting='no')
globals: {'n': 6309}, tested 1e+06 times
time (s) speedup methods
0 2.125426 1.20x np.copyto(y, x)
1 2.182111 1.17x y[:] = x
2 2.364018 1.08x y = np.array(x)
3 2.553323 np.copyto(y, x, casting='no')
globals: {'n': 10000}, tested 1e+06 times
time (s) speedup methods
0 3.196402 1.13x np.copyto(y, x)
1 3.523396 1.02x y[:] = x
2 3.531007 1.02x y = np.array(x)
3 3.597598 np.copyto(y, x, casting='no')
globals: {'n': 100000}, tested 1e+05 times
time (s) speedup methods
0 3.862123 1.01x np.copyto(y, x)
1 3.863693 1.01x y = np.array(x)
2 3.873194 1.01x y[:] = x
3 3.909018 np.copyto(y, x, casting='no')
答案 4 :(得分：9)
你可以轻松使用：
b = 1*a
dtype的{{1}}，也不会检查a dtype，则可能会遇到麻烦。例如：
b我希望，我能说清楚。有时候只需要一点操作就可以改变数据类型。
答案 5 :(得分：3)
你可以做很多不同的事情：
a=np.copy(b)
a=np.array(b) # Does exactly the same as np.copy
a[:]=b # a needs to be preallocated
a=b[np.arange(b.shape[0])]
a=copy.deepcopy(b)
不起作用的东西
a=b
a=b[:] # This have given my code bugs
答案 6 :(得分：1)
为什么不使用
a = 0 + b
我认为它类似于之前的乘法，但可能更简单。