[Numpy-discussion] New function `count_unique` to generate contingency tables.

[Warren Weckesser]

On Tue, Aug 12, 2014 at 12:51 PM, Eelco Hoogendoorn <
hoogendoorn.eelco at gmail.com> wrote:

> ah yes, that's also an issue I was trying to deal with. the semantics I
> prefer in these type of operators, is (as a default), to have every array
> be treated as a sequence of keys, so if calling unique(arr_2d), youd get
> unique rows, unless you pass axis=None, in which case the array is
> flattened.
>
> I also agree that the extension you propose here is useful; but ideally,
> with a little more discussion on these subjects we can converge on an
> even more comprehensive overhaul
>
>
> On Tue, Aug 12, 2014 at 6:33 PM, Joe Kington <joferkington at gmail.com>
> wrote:
>
>>
>>
>>
>> On Tue, Aug 12, 2014 at 11:17 AM, Eelco Hoogendoorn <
>> hoogendoorn.eelco at gmail.com> wrote:
>>
>>> Thanks. Prompted by that stackoverflow question, and similar problems I
>>> had to deal with myself, I started working on a much more general extension
>>> to numpy's functionality in this space. Like you noted, things get a little
>>> panda-y, but I think there is a lot of panda's functionality that could or
>>> should be part of the numpy core, a robust set of grouping operations in
>>> particular.
>>>
>>> see pastebin here:
>>> http://pastebin.com/c5WLWPbp
>>>
>>
>> On a side note, this is related to a pull request of mine from awhile
>> back: https://github.com/numpy/numpy/pull/3584
>>
>> There was a lot of disagreement on the mailing list about what to call a
>> "unique slices along a given axis" function, so I wound up closing the pull
>> request pending more discussion.
>>
>> At any rate, I think it's a useful thing to have in "base" numpy.
>>
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>
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Update: I renamed the function to `table` in the pull request:
https://github.com/numpy/numpy/pull/4958


Warren
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