As can be seen from the code below (or in the notebook linked beneath) fancy
indexing of a structured array is twice as slow as indexing both fields
independently - making it 4x slower?
I found that fancy indexing was a bottleneck in my application so I was
hoping to reduce the overhead by combining the arrays into a structured
array and only doing one indexing operation. Unfortunately that doubled the
time that it took!
Is there any reason for this? If not, I'm happy to open an enhancement issue
on GitHub - just let me know.
Thanks,
Dave
In [32]: nrows, ncols = 365, 10000
In [33]: items = np.rec.fromarrays(randn(2,nrows, ncols), names=
['widgets','gadgets'])
In [34]: row_idx = randint(0, nrows, ncols)
...: col_idx = np.arange(ncols)
In [35]: %timeit filtered_items = items[row_idx, col_idx]
100 loops, best of 3: 3.45 ms per loop
In [36]: %%timeit
...: widgets = items['widgets'][row_idx, col_idx]
...: gadgets = items['gadgets'][row_idx, col_idx]
...:
1000 loops, best of 3: 1.57 ms per loop
http://nbviewer.ipython.org/urls/gist.githubusercontent.com/dhirschfeld/98b9
970fb68adf23dfea/raw/10c0f968ea1489f0a24da80d3af30de7106848ac/Slow%20Structu
red%20Array%20Indexing.ipynb
https://gist.github.com/dhirschfeld/98b9970fb68adf23dfea
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