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| """ | |
| snippet to time different indexing strategies | |
| based on: | |
| http://wesmckinney.com/blog/?p=215 | |
| http://stackoverflow.com/questions/11800075/faster-numpy-fancy-indexing-and-reduction/11813040#11813040 | |
| http://stackoverflow.com/questions/14386822/fast-numpy-fancy-indexing?rq=1 | |
| """ | |
| import numpy as np | |
| import random | |
| indexer = np.arange(5000) | |
| random.shuffle(indexer) | |
| arr = np.array(np.random.randn(10000, 10000), order="F") | |
| %timeit arr[:,indexer] | |
| %timeit arr.take(indexer, axis=1) | |
| arr = np.array(np.random.randn(10000, 10000), order="C") | |
| %timeit arr[:,indexer] | |
| %timeit arr.take(indexer, axis=1) | |
| """ | |
| In [46]: indexer = np.arange(5000) | |
| In [47]: random.shuffle(indexer) | |
| In [48]: | |
| In [48]: arr = np.array(np.random.randn(10000, 10000), order="F") | |
| In [49]: %timeit arr[:,indexer] | |
| 1 loops, best of 3: 1.41 s per loop | |
| In [50]: %timeit arr.take(indexer, axis=1) | |
| 1 loops, best of 3: 2.35 s per loop | |
| In [51]: | |
| In [51]: arr = np.array(np.random.randn(10000, 10000), order="C") | |
| In [52]: %timeit arr[:,indexer] | |
| 1 loops, best of 3: 3.08 s per loop | |
| In [53]: %timeit arr.take(indexer, axis=1) | |
| 1 loops, best of 3: 438 ms per loop | |
| """ | |
| # verdict: use C-order with take |
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