My pleasure!
Cheers,
Miles
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So, this new method is in fact a hash table as discussed in that blog post.
However, because it assumes integer arrays, we can go even further than that
blog, and simply use `np.arange(ar_min, ar_max + 1)` is the "hash table". Thus,
you don't actually need to use a hashing function at all, you c
Regarding 2., did you have a particular approach in mind? This new lookup table
method is already O(n) scaling (similar to a counting sort), so I cannot fathom
a method that, as you suggest, would get significantly better performance for
integer arrays. The sorting here is "free" in some sense s
Ah, I did not clarify this: `kind="table"` will *also* return a sorted array.
It simply does not use a sorting algorithm to get to it. This is because the
table is generated using `np.arange` (i.e., already sorted) which is then
masked.
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Thanks for the comments Ralf!
> You cannot switch the default behavior, that will break backwards
> compatibility.
The default `kind=None` have no effect on input/output behavior of the
function. The only changes a user will see are in terms of speed and memory
usage. `unique` will select this
Dear all,
There is a PR that adds a lookup table approach to `unique`, shown below. You
can get up to ~16x speedup for large integer arrays, at the cost of potentially
greater memory usage.
https://github.com/numpy/numpy/pull/21843
This is controlled by a new `kind` parameter, which is describ
Hi,
I was wondering how I could have this PR merged (
https://github.com/numpy/numpy/pull/12065)? The discussion on the PR seems
to have gone well and all tests pass.
Cheers,
Miles
On Mon, Oct 1, 2018 at 2:36 PM Miles Cranmer
wrote:
> (Not sure what the right list is for this)
>
>
(Not sure what the right list is for this)
Hi,
I have started a PR for a "fast_integers" flag for np.isin and np.in1d
which greatly increases performance when both arrays are integral. It works
by creating a boolean array with elements set to 1 where the parent array
(ar2) has elements and 0 othe
