How to copy data from a numpy array to another
What is the fastest way to copy data from array b to array a, without modifying the address of array a. I need this because an external library (PyFFTW) uses a pointer to my
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To answer your question, I played with some variants and profiled them.
Conclusion: to copy data from a numpy array to another use one of the built-in numpy functions numpy.array(src) or numpy.copyto(dst, src) wherever possible.
(But always choose the later if
dst's memory is already allocated, to reuse the memory. See profiling at the end of the post.)profiling setup
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()profiling code
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)})results for Windows 7 on Intel i7 CPU, 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
Also, see results for a variant of the profiling where the destination's memory is already pre-allocated during value copying, since
y = np.empty_like(x)is part of the setup: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')
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