[Numpy-discussion] Consider improving numpy.outer's behavior with zero-dimensional vectors

[josef.pktd at gmail.com]

On Wed, Apr 15, 2015 at 6:40 PM, Nathaniel Smith <njs at pobox.com> wrote:
> On Wed, Apr 15, 2015 at 6:08 PM,  <josef.pktd at gmail.com> wrote:
>> On Wed, Apr 15, 2015 at 5:31 PM, Neil Girdhar <mistersheik at gmail.com> wrote:
>>> Does it work for you to set
>>>
>>> outer = np.multiply.outer
>>>
>>> ?
>>>
>>> It's actually faster on my machine.
>>
>> I assume it does because np.corrcoeff uses it, and it's the same type
>> of use cases.
>> However, I'm not using it very often (I prefer broadcasting), but I've
>> seen it often enough when reviewing code.
>>
>> This is mainly to point out that it could be a popular function (that
>> maybe shouldn't be deprecated)
>>
>> https://github.com/search?utf8=%E2%9C%93&q=np.outer
>> 416914
>
> For future reference, that's not the number -- you have to click
> through to "Code" and then look at a single-language result to get
> anything remotely meaningful. In this case b/c they're different by an
> order of magnitude, and in general because sometimes the "top line"
> number is completely made up (like it has no relation to the
> per-language numbers on the left and then changes around randomly if
> you simply reload the page).
>
> (So 29,397 is what you want in this case.)
>
> Also that count then tends to have tons of duplicates (e.g. b/c there
> are hundreds of copies of numpy itself on github), so you need a big
> grain of salt when looking at the absolute number, but it can be
> useful, esp. for relative comparisons.

My mistake, rushing too much.
github show only 25 code references in numpy itself.

in quotes, python only  (namespace conscious packages on github)
(I think github counts modules not instances)

"np.cumsum" 11,022
"np.cumprod" 1,290
"np.outer" 6,838

statsmodels
"np.cumsum" 21
"np.cumprod"  2
"np.outer" 15

Josef

>
> -n
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