Mailman 3 April 2018 - Python-ideas

Adding a thin wrapper class around the functions in stdlib.heapq
by bunslow Feb. 18, 2022

Feb. 18, 2022

Nothing so bombastic this time. The heapq functions are basically all named "heapsomething", and basically all take a "heap" for their first argument, with supplementary args coming after. It's a textbook example of the (hypothetical) Object Oriented Manifesto™ where defining a class increases type safety and programmers' conceptual clarity. There're practically no drawbacks, and the code to be added would be very simple. Updating the tests and docs would probably be harder. In pure Python, such a class would look like this: class Heap(list): def __init__(self, iterable=None): if iterable: super().__init__(iterable) else: super().__init__() self.heapify() push = heapq.heappush pop = heapq.heappop pushpop = heapq.heappushpop replace = heapq.heapreplace heapify = heapq.heapify # This could be a simple wrapper as well, but I had the following thoughts anyways, so here they are def nsmallest(self, n, key=None): # heapq.nsmallest makes a *max* heap of the first n elements, # while we know that self is already a min heap, so we can # make the max heap construction faster self[:n] = reversed(self[:n]) return heapq.nsmallest(n, self, key) # do we define nlargest on a minheap?? Wrapping around the C builtin functions (which aren't descriptors) would be a bit harder, but not much so: from functools import partial class Heap(list): def __init__(self, iterable=None): if iterable: super().__init__(iterable) else: super().__init__() self.heapify = partial(heapq.heapify, self) self.push = partial(heapq.heappush, self) ... self.heapify() Thoughts?

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Dataclasses, keyword args, and inheritance
by George Leslie-Waksman March 12, 2021

March 12, 2021

The proposed implementation of dataclasses prevents defining fields with defaults before fields without defaults. This can create limitations on logical grouping of fields and on inheritance. Take, for example, the case: @dataclass class Foo: some_default: dict = field(default_factory=dict) @dataclass class Bar(Foo): other_field: int this results in the error: 5 @dataclass ----> 6 class Bar(Foo): 7 other_field: int 8 ~/.pyenv/versions/3.6.2/envs/clover_pipeline/lib/python3.6/site-packages/dataclasses.py in dataclass(_cls, init, repr, eq, order, hash, frozen) 751 752 # We're called as @dataclass, with a class. --> 753 return wrap(_cls) 754 755 ~/.pyenv/versions/3.6.2/envs/clover_pipeline/lib/python3.6/site-packages/dataclasses.py in wrap(cls) 743 744 def wrap(cls): --> 745 return _process_class(cls, repr, eq, order, hash, init, frozen) 746 747 # See if we're being called as @dataclass or @dataclass(). ~/.pyenv/versions/3.6.2/envs/clover_pipeline/lib/python3.6/site-packages/dataclasses.py in _process_class(cls, repr, eq, order, hash, init, frozen) 675 # in __init__. Use "self" if possible. 676 '__dataclass_self__' if 'self' in fields --> 677 else 'self', 678 )) 679 if repr: ~/.pyenv/versions/3.6.2/envs/clover_pipeline/lib/python3.6/site-packages/dataclasses.py in _init_fn(fields, frozen, has_post_init, self_name) 422 seen_default = True 423 elif seen_default: --> 424 raise TypeError(f'non-default argument {f.name!r} ' 425 'follows default argument') 426 TypeError: non-default argument 'other_field' follows default argument I understand that this is a limitation of positional arguments because the effective __init__ signature is: def __init__(self, some_default: dict = <something>, other_field: int): However, keyword only arguments allow an entirely reasonable solution to this problem: def __init__(self, *, some_default: dict = <something>, other_field: int): And have the added benefit of making the fields in the __init__ call entirely explicit. So, I propose the addition of a keyword_only flag to the @dataclass decorator that renders the __init__ method using keyword only arguments: @dataclass(keyword_only=True) class Bar(Foo): other_field: int --George Leslie-Waksman

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@classproperty, @abc.abstractclasspropery, etc.
by K. Richard Pixley Dec. 16, 2020

Dec. 16, 2020

There's a whole matrix of these and I'm wondering why the matrix is currently sparse rather than implementing them all. Or rather, why we can't stack them as: class foo(object): @classmethod @property def bar(cls, ...): ... Essentially the permutation are, I think: {'unadorned'|abc.abstract}{'normal'|static|class}{method|property|non-callable attribute}. concreteness implicit first arg type name comments {unadorned} {unadorned} method def foo(): exists now {unadorned} {unadorned} property @property exists now {unadorned} {unadorned} non-callable attribute x = 2 exists now {unadorned} static method @staticmethod exists now {unadorned} static property @staticproperty proposing {unadorned} static non-callable attribute {degenerate case - variables don't have arguments} unnecessary {unadorned} class method @classmethod exists now {unadorned} class property @classproperty or @classmethod;@property proposing {unadorned} class non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract {unadorned} method @abc.abstractmethod exists now abc.abstract {unadorned} property @abc.abstractproperty exists now abc.abstract {unadorned} non-callable attribute @abc.abstractattribute or @abc.abstract;@attribute proposing abc.abstract static method @abc.abstractstaticmethod exists now abc.abstract static property @abc.staticproperty proposing abc.abstract static non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract class method @abc.abstractclassmethod exists now abc.abstract class property @abc.abstractclassproperty proposing abc.abstract class non-callable attribute {degenerate case - variables don't have arguments} unnecessary I think the meanings of the new ones are pretty straightforward, but in case they are not... @staticproperty - like @property only without an implicit first argument. Allows the property to be called directly from the class without requiring a throw-away instance. @classproperty - like @property, only the implicit first argument to the method is the class. Allows the property to be called directly from the class without requiring a throw-away instance. @abc.abstractattribute - a simple, non-callable variable that must be overridden in subclasses @abc.abstractstaticproperty - like @abc.abstractproperty only for @staticproperty @abc.abstractclassproperty - like @abc.abstractproperty only for @classproperty --rich

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Re: [Python-ideas] Allow star unpacking within an slice expression
by Neil Girdhar March 7, 2020

March 7, 2020

I didn't think of this when we were discussing 448. I ran into this today, so I agree with you that it would be nice to have this. Best, Neil On Monday, December 4, 2017 at 1:02:09 AM UTC-5, Eric Wieser wrote: > > Hi, > > I've been thinking about the * unpacking operator while writing some numpy > code. PEP 448 allows the following: > > values = 1, *some_tuple, 2 > object[(1, *some_tuple, 2)] > > It seems reasonable to me that it should be extended to allow > > item = object[1, *some_tuple, 2] > item = object[1, *some_tuple, :] > > Was this overlooked in the original proposal, or deliberately rejected? > > Eric >

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Specify number of items to allocate for array.array() constructor
by Sven Rahmann Feb. 22, 2020

Feb. 22, 2020

At the moment, the array module of the standard library allows to create arrays of different numeric types and to initialize them from an iterable (eg, another array). What's missing is the possiblity to specify the final size of the array (number of items), especially for large arrays. I'm thinking of suffix arrays (a text indexing data structure) for large texts, eg the human genome and its reverse complement (about 6 billion characters from the alphabet ACGT). The suffix array is a long int array of the same size (8 bytes per number, so it occupies about 48 GB memory). At the moment I am extending an array in chunks of several million items at a time at a time, which is slow and not elegant. The function below also initializes each item in the array to a given value (0 by default). Is there a reason why there the array.array constructor does not allow to simply specify the number of items that should be allocated? (I do not really care about the contents.) Would this be a worthwhile addition to / modification of the array module? My suggestions is to modify array generation in such a way that you could pass an iterator (as now) as second argument, but if you pass a single integer value, it should be treated as the number of items to allocate. Here is my current workaround (which is slow): def filled_array(typecode, n, value=0, bsize=(1<<22)): """returns a new array with given typecode (eg, "l" for long int, as in the array module) with n entries, initialized to the given value (default 0) """ a = array.array(typecode, [value]*bsize) x = array.array(typecode) r = n while r >= bsize: x.extend(a) r -= bsize x.extend([value]*r) return x

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Please consider adding of functions file system operations to pathlib
by George Fischhof Nov. 16, 2019

Nov. 16, 2019

Good day all, as a continuation of thread "OS related file operations (copy, move, delete, rename...) should be placed into one module" https://mail.python.org/pipermail/python-ideas/2017-January/044217.html please consider making pathlib to a central file system module with putting file operations (copy, move, delete, rmtree etc) into pathlib. BR, George

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Start argument for itertools.accumulate() [Was: Proposal: A Reduce-Map Comprehension and a "last" builtin]
by Raymond Hettinger Oct. 23, 2019

Oct. 23, 2019

> On Friday, April 6, 2018 at 8:14:30 AM UTC-7, Guido van Rossum wrote: > On Fri, Apr 6, 2018 at 7:47 AM, Peter O'Connor <peter.ed...(a)gmail.com> wrote: >> So some more humble proposals would be: >> >> 1) An initializer to itertools.accumulate >> functools.reduce already has an initializer, I can't see any controversy to adding an initializer to itertools.accumulate > > See if that's accepted in the bug tracker. It did come-up once but was closed for a number reasons including lack of use cases. However, Peter's signal processing example does sound interesting, so we could re-open the discussion. For those who want to think through the pluses and minuses, I've put together a Q&A as food for thought (see below). Everybody's design instincts are different -- I'm curious what you all think think about the proposal. Raymond --------------------------------------------- Q. Can it be done? A. Yes, it wouldn't be hard. _sentinel = object() def accumulate(iterable, func=operator.add, start=_sentinel): it = iter(iterable) if start is _sentinel: try: total = next(it) except StopIteration: return else: total = start yield total for element in it: total = func(total, element) yield total Q. Do other languages do it? A. Numpy, no. R, no. APL, no. Mathematica, no. Haskell, yes. * http://docs.scipy.org/doc/numpy/reference/generated/numpy.ufunc.accumulate.… * https://stat.ethz.ch/R-manual/R-devel/library/base/html/cumsum.html * http://microapl.com/apl/apl_concepts_chapter5.html \+ 1 2 3 4 5 1 3 6 10 15 * https://reference.wolfram.com/language/ref/Accumulate.html * https://www.haskell.org/hoogle/?hoogle=mapAccumL Q. How much work for a person to do it currently? A. Almost zero effort to write a simple helper function: myaccum = lambda it, func, start: accumulate(chain([start], it), func) Q. How common is the need? A. Rare. Q. Which would be better, a simple for-loop or a customized itertool? A. The itertool is shorter but more opaque (especially with respect to the argument order for the function call): result = [start] for x in iterable: y = func(result[-1], x) result.append(y) versus: result = list(accumulate(iterable, func, start=start)) Q. How readable is the proposed code? A. Look at the following code and ask yourself what it does: accumulate(range(4, 6), operator.mul, start=6) Now test your understanding: How many values are emitted? What is the first value emitted? Are the two sixes related? What is this code trying to accomplish? Q. Are there potential surprises or oddities? A. Is it readily apparent which of assertions will succeed? a1 = sum(range(10)) a2 = sum(range(10), 0) assert a1 == a2 a3 = functools.reduce(operator.add, range(10)) a4 = functools.reduce(operator.add, range(10), 0) assert a3 == a4 a4 = list(accumulate(range(10), operator.add)) a5 = list(accumulate(range(10), operator.add, start=0)) assert a5 == a6 Q. What did the Python 3.0 Whatsnew document have to say about reduce()? A. "Removed reduce(). Use functools.reduce() if you really need it; however, 99 percent of the time an explicit for loop is more readable." Q. What would this look like in real code? A. We have almost no real-world examples, but here is one from a StackExchange post: def wsieve(): # wheel-sieve, by Will Ness. ideone.com/mqO25A->0hIE89 wh11 = [ 2,4,2,4,6,2,6,4,2,4,6,6, 2,6,4,2,6,4,6,8,4,2,4,2, 4,8,6,4,6,2,4,6,2,6,6,4, 2,4,6,2,6,4,2,4,2,10,2,10] cs = accumulate(cycle(wh11), start=11) yield( next( cs)) # cf. ideone.com/WFv4f ps = wsieve() # codereview.stackexchange.com/q/92365/9064 p = next(ps) # 11 psq = p*p # 121 D = dict( zip( accumulate(wh11, start=0), count(0))) # start from sieve = {} for c in cs: if c in sieve: wheel = sieve.pop(c) for m in wheel: if not m in sieve: break sieve[m] = wheel # sieve[143] = wheel@187 elif c < psq: yield c else: # (c==psq) # map (p*) (roll wh from p) = roll (wh*p) from (p*p) x = [p*d for d in wh11] i = D[ (p-11) % 210] wheel = accumulate(cycle(x[i:] + x[:i]), start=psq) p = next(ps) ; psq = p*p next(wheel) ; m = next(wheel) sieve[m] = wheel

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Dart like multi line strings identation
by Marius Räsener Feb. 8, 2019

Feb. 8, 2019

Hey List, this is my very first approach to suggest a Python improvement I'd think worth discussing. At some point, maybe with Dart 2.0 or a little earlier, Dart is now supporting multiline strings with "proper" identation (tried, but I can't find the according docs at the moment. probably due to the rather large changes related to dart 2.0 and outdated docs.) What I have in mind is probably best described with an Example: print(""" I am a multiline String. """) the closing quote defines the "margin indentation" - so in this example all lines would get reduces by their leading 4 spaces, resulting in a "clean" and unintended string. anyways, if dart or not, doesn't matter - I like the Idea and I think python3.x could benefit from it. If that's possible at all :) I could also imagine that this "indentation cleanup" only is applied if the last quotes are on their own line? Might be too complicated though, I can't estimated or understand this... thx for reading, Marius

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Pypi private repo's
by Nick Humrich Oct. 29, 2018

Oct. 29, 2018

I am sure this has been discussed before, and this might not even be the best place for this discussion, but I just wanted to make sure this has been thought about. What if pypi.org supported private repos at a cost, similar to npm? This would be able to help support the cost of pypi, and hopefully make it better/more reliable, thus in turn improving the python community. If this discussion should happen somewhere else, let me know. Nick

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Re: [Python-ideas] [Distutils] Pypi private repo's
by Nick Coghlan Oct. 28, 2018

Oct. 28, 2018

On 5 April 2018 at 07:58, Jannis Gebauer <ja.geb(a)me.com> wrote: > What if there was some kind of “blessed” entity that runs these services and puts the majority of the revenue into a fund that funds development on PyPi (maybe trough the PSF)? Having a wholly owned for-profit subsidiary that provides commercial services as a revenue raising mechanism is certainly one way to approach something like this without alienating sponsors or tax authorities (although it may still alienate the vendors of now competing services). It would require a big time commitment on the PSF side to get everything set up though, as well as interest from key folks in joining what would essentially be a single-language-focused start up in an already crowded cross-language developer tools marketplace. When the PSF as a whole is still operating with only a handful of full or part time employees, it's far from clear that setting something like that up would be the most effective possible use of their time and energy. At a more basic level, that kind of arrangement technically doesn't require anyone's blessing, it could be as straightforward as downstream tooling vendors signing up as PSF sponsors and saying "please allocate our sponsorship contribution to the Packaging WG's budget so that PyPI keeps operating well and the PyPA tooling keeps improving, increasing the level of demand for our commercial Python repository management services". Historically that wouldn't have helped much, since the PSF itself has struggled with effective project management (for a variety of reasons), but one of the things I think the success of the MOSS grant has shown is the significant strides that the PSF has made in budget management in recent years, such that if funding is made available, it can and will be spent effectively. Cheers, Nick. P.S. PyPA contributors are also free agents in their own right, so folks offering Python-centric developer workflow management tools or features may decide that it's worth their while to invest more directly in smoothing out some of the rough edges that currently still exist. It's a mercenary way of looking at things, but in many cases, it is *absolutely* possible to pay for the time and attention of existing contributors, and if you can persuade them that your proposals are reasonable, they'll often have an easier time than most convincing other community contributors that it's a good way to go :) -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

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