如何将数据从一个numpy数组复制到另一个数组
将数组b中的数据复制到数组a的最快方法是什么，而不需要修改数组a的地址。 我需要这个，因为外部库（PyFFTW）使用指向我的数组，不能改变的指针。
例如：
a = numpy.empty(n, dtype=complex) for i in xrange(a.size): a[i] = b[i] 有可能没有循环呢？
我相信
a = numpy.empty_like (b) a[:] = b
将迅速做出深层次的复制。 正如Funsi提到的，最近版本的numpy也有copytofunction。
numpy 1.7版有numpy.copyto函数，可以做你正在寻找的东西：
numpy.copyto（dst，src）
将数组中的值复制到另一个数组中，>根据需要进行广播。
参见： http : //docs.scipy.org/doc/numpy-dev/reference/generated/numpy.copyto.html
a = numpy.array(b)
甚至比build议的解决scheme更快到numpy v1.6，并且还创build了arrays的副本。 我可以不testingcopyto（a，b），因为我没有最新版本的numpy。
你可以轻松使用：
b = 1*a
这是最快的方法，但也有一些问题。 如果你不直接定义a的dtype ，也不检查b的dtype ，那么你可能会陷入困境。 例如：
a = np.arange(10) # dtype = int64 b = 1*a # dtype = int64 a = np.arange(10.) # dtype = float64 b = 1*a # dtype = float64 a = np.arange(10) # dtype = int64 b = 1. * a # dtype = float64
我希望我能说清楚一点。 有时候只需要一点操作就可以改变数据types。
为了回答你的问题，我玩了一些变体，并对它们进行了分析。
结论：将数据从一个numpy数组复制到另一个使用内置的numpy函数numpy.array(src)或numpy.copyto(dst, src) 。
（但是如果dst的内存已经被分配了，总是select后面的内存来重用内存。
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.max(timings) / timings pd.set_option('html', False) data = {'time (s)': timings, 'speedup': ['{0:0.2f}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()
分析代码
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, int(10**it), {'n': int(10**n)})
Windows 7在Intel i7 CPU，CPython v3.5.0，numpy v1.10.1上的结果。
globals: {'n': 100}, tested 1e+06 times time (s) speedup methods 0 0.386908 33.76xy = np.array(x) 1 0.496475 26.31xy = x.astype(x.dtype) 2 0.567027 23.03xy = np.empty_like(x); np.copyto(y, x) 3 0.666129 19.61xy = np.empty_like(x); y[:] = x 4 0.967086 13.51xy = 1*x 5 1.067240 12.24xy = np.empty_like(x); np.copyto(y, x, casting=... 6 1.235198 10.57xy = np.copy(x) 7 1.624535 8.04xy = np.zeros(n, dtype=x.dtype); y[:] = x 8 1.626120 8.03xy = np.empty(n, dtype=x.dtype); y[:] = x 9 3.569372 3.66xy = 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 6.10xy = x.astype(x.dtype) 1 0.740594 5.49xy = np.empty_like(x); np.copyto(y, x) 2 0.755246 5.39xy = np.array(x) 3 1.043631 3.90xy = np.empty_like(x); y[:] = x 4 1.398793 2.91xy = 1*x 5 1.434299 2.84xy = np.empty_like(x); np.copyto(y, x, casting=... 6 1.544769 2.63xy = np.copy(x) 7 1.873119 2.17xy = np.empty(n, dtype=x.dtype); y[:] = x 8 2.355593 1.73xy = 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 3.05xy = np.array(x) 1 2.466636 2.89xy = x.astype(x.dtype) 2 2.561535 2.78xy = np.empty_like(x); np.copyto(y, x) 3 2.603601 2.74xy = np.empty_like(x); y[:] = x 4 3.005610 2.37xy = np.empty_like(x); np.copyto(y, x, casting=... 5 3.215863 2.22xy = np.copy(x) 6 3.249763 2.19xy = 1*x 7 3.661599 1.95xy = np.empty(n, dtype=x.dtype); y[:] = x 8 6.344077 1.12xy = 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 2.82xy = np.array(x) 1 3.569501 2.71xy = x.astype(x.dtype) 2 3.618747 2.67xy = np.empty_like(x); np.copyto(y, x) 3 3.708604 2.61xy = np.empty_like(x); y[:] = x 4 4.150505 2.33xy = np.empty_like(x); np.copyto(y, x, casting=... 5 4.402126 2.19xy = np.copy(x) 6 4.917966 1.96xy = np.empty(n, dtype=x.dtype); y[:] = x 7 4.941269 1.96xy = 1*x 8 8.925884 1.08xy = 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 2.63xy = x.astype(x.dtype) 1 3.873989 2.62xy = np.array(x) 2 3.896584 2.60xy = np.empty_like(x); np.copyto(y, x) 3 3.919729 2.58xy = np.empty_like(x); np.copyto(y, x, casting=... 4 3.948563 2.57xy = np.empty_like(x); y[:] = x 5 4.000521 2.53xy = np.copy(x) 6 4.087255 2.48xy = np.empty(n, dtype=x.dtype); y[:] = x 7 4.803606 2.11xy = 1*x 8 6.723291 1.51xy = 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 1.24xy = np.empty_like(x); y[:] = x 1 85.693316 1.24xy = np.empty_like(x); np.copyto(y, x) 2 85.790064 1.24xy = np.empty_like(x); np.copyto(y, x, casting=... 3 86.342230 1.23xy = np.empty(n, dtype=x.dtype); y[:] = x 4 86.954862 1.22xy = np.zeros(n, dtype=x.dtype); y[:] = x 5 89.503368 1.18xy = np.array(x) 6 91.986177 1.15xy = 1*x 7 95.216021 1.11xy = np.copy(x) 8 100.524358 1.05xy = x.astype(x.dtype) 9 106.045746 y = np.zeros_like(x); y[:] = x
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)
事情不起作用
a=b a=b[:] # This have given my code bugs
