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