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In [1]: import numpy as np | |
In [2]: np.__version__ | |
Out[2]: '1.19.5' | |
In [3]: a = np.arange(1e6) | |
In [4]: %timeit x = np.array(a).copy() | |
1.12 ms ± 10.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) | |
In [5]: %timeit x = np.ascontiguousarray(a).copy() | |
527 µs ± 3.63 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) | |
In [6]: %timeit x = np.array([0,1,2]).copy() | |
1.48 µs ± 6.99 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) | |
In [7]: %timeit x = np.ascontiguousarray([0,1,2]).copy() | |
2.31 µs ± 29.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) |
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In [1]: import numpy as np | |
In [2]: np.__version__ | |
Out[2]: '1.20.1' | |
In [3]: a = np.arange(1e6) | |
In [4]: %timeit x = np.array(a).copy() | |
1.06 ms ± 22.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) | |
In [5]: %timeit x = np.ascontiguousarray(a).copy() | |
521 µs ± 4.37 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) | |
In [6]: %timeit x = np.array([0,1,2]).copy() | |
976 ns ± 12.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) | |
In [7]: %timeit x = np.ascontiguousarray([0,1,2]).copy() | |
1.59 µs ± 23.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) |
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