[Python-ideas] Respectively and its unpacking sentence

[Andrew Barnert]

On Jan 27, 2016, at 09:55, Mirmojtaba Gharibi <mojtaba.gharibi at gmail.com> wrote:
> 
>> On Wed, Jan 27, 2016 at 2:29 AM, Andrew Barnert <abarnert at yahoo.com> wrote:
>>> On Jan 26, 2016, at 22:19, Mirmojtaba Gharibi <mojtaba.gharibi at gmail.com> wrote:
>>> 
>>> Yes, I'm aware sequence unpacking.
>>> There is an overlap like you mentioned, but there are things that can't be done with sequence unpacking, but can be done here.
>>> 
>>> For example, let's say you're given two lists that are not necessarily numbers, so you can't use numpy, but you want to apply some component-wise operator between each component. This is something you can't do with sequence unpacking or with numpy.
>> 
>> Yes, you can do it with numpy.
>> 
>> Obviously you don't get the performance benefits when you aren't using "native" types (like int32) and operations that have vectorizes implementations (like adding two arrays of int32 or taking the dot product of float64 matrices), but you do still get the same elementwise operators, and even a way to apply arbitrary callables over arrays, or even other collections:
>> 
>>     >>> firsts = ['John', 'Jane']
>>     >>> lasts = ['Smith', 'Doe']
>>     >>> np.vectorize('{1}, {0}'.format)(firsts, lasts)
>>     array(['Smith, John', 'Doe, Jane'], dtype='<U11)
> I think the form I am suggesting is simpler and more readable.

But the form you're suggesting doesn't work for vectorizing arbitrary functions, only for operator expressions (including simple function calls, but that doesn't help for more general function calls). The fact that numpy is a little harder to read for cases that your syntax can't handle at all is hardly a strike against numpy.

And, as I already explained, for the cases where your form _does_ work, numpy already does it, without all the sigils:

    c = a + b

    c = a*a + 2*a*b + b*b

    c = (a * b).sum()

It also works nicely over multiple dimensions. For example, if a and b are both arrays of N 3-vectors instead of just being 3-vectors, you can still elementwise-add them just with +; you can sum all of the results with sum(axis=1); etc. How would you write any of those things with your $-syntax?

> I'm happy you brought vectorize to my attention though. I think as soon you make the statement just a bit complex, it would become really complicated with vectorize. 
> 
> For example lets say you have 
> x=[1,2,3,4,5,...]
> y=['A','BB','CCC',...]
> p=[2,3,4,6,6,...]
> r=[]*n
> 
> $r = str(len($y*$p)+$x)

As a side note, []*n is always just []. Maybe you meant [None for _ in range(n)] or [None]*n? Also, where does n come from? It doesn't seem to have anything to do with the lengths of x, y, and p. So, what happens if it's shorter than them? Or longer? With numpy, of course, that isn't a problem--there's no magic being attempted on the = operator (which is good, because = isn't an operator in Python, and I'm not sure how you'd even properly define your design, much less implement it); the operators just create arrays of the right length.

Anyway, that's still mostly just operators. You _could_ wrap up an operator expression in a function to vectorize, but you almost never want to. Just use the operators directly on the arrays. 

So, let's try a case that has even some minimal amount of logic, where translating to operators would be clumsy at best:

    @np.vectorize
    def sillyslice(y, x, p):
        if x < p: return y[x:p]
        return y[p:x]

    r = sillyslice(y, x, p)

Being a separate function provides all the usual benefits: sillyslice is reusable, debuggable, unit-testable, usable as a first-class object, etc. But forget that; how would you do this at all with your $-syntax?

Since you didn't answer any of my other questions, I'll snip them and repost shorter versions:

* what's wrong with using numpy?
* what's wrong with APL or J or MATLAB?
* what's wrong with making the operators elementwise instead of wrapping the objects in some magic thing?
* what is the type of that magic thing anyway?

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