I need to be able to store a numpy array in a dict for caching purposes. Hash speed is important. The array represents indicies, so while the actual identity of the object is not important, the value is. Mutabliity is not a concern, as I'm only interested in the current value. What should I hash in order to store it in a dict? My current approach is to use str(arr.data), which is faster than md5 in my testing. I've incorporated some examples from the answers to get an idea of relative times: In [121]: %timeit hash(str(y)) 10000 loops, best of 3: 68.7 us per loop In [122]: %timeit hash(y.tostring()) 1000000 loops, best of 3: 383 ns per loop In [123]: %timeit hash(str(y.data)) 1000000 loops, best of 3: 543 ns per loop In [124]: %timeit y.flags.writeable = False ; hash(y.data) 1000000 loops, best of 3: 1.15 us per loop In [125]: %timeit hash((b*y).sum()) 100000 loops, best of 3: 8.12 us per loop It would appear that for this particular use case (small arrays of indicies), arr.tostring offers the best performance. While hashing the read-only buffer is fast on its own, the overhead of setting the writeable flag actually makes it slower. Continue reading...
