Python-ideas February 2018

python-ideas@python.org

64 participants
34 discussions

Re: [Python-ideas] Allow star unpacking within an slice expression

by Neil Girdhar

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 >

7 months, 4 weeks

Specify number of items to allocate for array.array() constructor

by Sven Rahmann

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

8 months, 1 week

Dataclasses, keyword args, and inheritance

by George Leslie-Waksman

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

8 months, 3 weeks

Please consider adding of functions file system operations to pathlib

by George Fischhof

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

11 months, 2 weeks

Asynchronous exception handling around with/try statement borders

by Erik Bray

Hi folks, I normally wouldn't bring something like this up here, except I think that there is possibility of something to be done--a language documentation clarification if nothing else, though possibly an actual code change as well. I've been having an argument with a colleague over the last couple days over the proper way order of statements when setting up a try/finally to perform cleanup of some action. On some level we're both being stubborn I think, and I'm not looking for resolution as to who's right/wrong or I wouldn't bring it to this list in the first place. The original argument was over setting and later restoring os.environ, but we ended up arguing over threading.Lock.acquire/release which I think is a more interesting example of the problem, and he did raise a good point that I do want to bring up. </prologue> My colleague's contention is that given lock = threading.Lock() this is simply *wrong*: lock.acquire() try: do_something() finally: lock.release() whereas this is okay: with lock: do_something() Ignoring other details of how threading.Lock is actually implemented, assuming that Lock.__enter__ calls acquire() and Lock.__exit__ calls release() then as far as I've known ever since Python 2.5 first came out these two examples are semantically *equivalent*, and I can't find any way of reading PEP 343 or the Python language reference that would suggest otherwise. However, there *is* a difference, and has to do with how signals are handled, particularly w.r.t. context managers implemented in C (hence we are talking CPython specifically): If Lock.__enter__ is a pure Python method (even if it maybe calls some C methods), and a SIGINT is handled during execution of that method, then in almost all cases a KeyboardInterrupt exception will be raised from within Lock.__enter__--this means the suite under the with: statement is never evaluated, and Lock.__exit__ is never called. You can be fairly sure the KeyboardInterrupt will be raised from somewhere within a pure Python Lock.__enter__ because there will usually be at least one remaining opcode to be evaluated, such as RETURN_VALUE. Because of how delayed execution of signal handlers is implemented in the pyeval main loop, this means the signal handler for SIGINT will be called *before* RETURN_VALUE, resulting in the KeyboardInterrupt exception being raised. Standard stuff. However, if Lock.__enter__ is a PyCFunction things are quite different. If you look at how the SETUP_WITH opcode is implemented, it first calls the __enter__ method with _PyObjet_CallNoArg. If this returns NULL (i.e. an exception occurred in __enter__) then "goto error" is executed and the exception is raised. However if it returns non-NULL the finally block is set up with PyFrame_BlockSetup and execution proceeds to the next opcode. At this point a potentially waiting SIGINT is handled, resulting in KeyboardInterrupt being raised while inside the with statement's suite, and finally block, and hence Lock.__exit__ are entered. Long story short, because Lock.__enter__ is a C function, assuming that it succeeds normally then with lock: do_something() always guarantees that Lock.__exit__ will be called if a SIGINT was handled inside Lock.__enter__, whereas with lock.acquire() try: ... finally: lock.release() there is at last a small possibility that the SIGINT handler is called after the CALL_FUNCTION op but before the try/finally block is entered (e.g. before executing POP_TOP or SETUP_FINALLY). So the end result is that the lock is held and never released after the KeyboardInterrupt (whether or not it's handled somehow). Whereas, again, if Lock.__enter__ is a pure Python function there's less likely to be any difference (though I don't think the possibility can be ruled out entirely). At the very least I think this quirk of CPython should be mentioned somewhere (since in all other cases the semantic meaning of the "with:" statement is clear). However, I think it might be possible to gain more consistency between these cases if pending signals are checked/handled after any direct call to PyCFunction from within the ceval loop. Sorry for the tl;dr; any thoughts?

2 years, 1 month

Positional-only parameters

by Victor Stinner

Hi, For technical reasons, many functions of the Python standard libraries implemented in C have positional-only parameters. Example: ------- $ ./python Python 3.7.0a0 (default, Feb 25 2017, 04:30:32) >>> help(str.replace) replace(self, old, new, count=-1, /) # <== notice "/" at the end ... >>> "a".replace("x", "y") # ok 'a' >>> "a".replace(old="x", new="y") # ERR! TypeError: replace() takes at least 2 arguments (0 given) ------- When converting the methods of the builtin str type to the internal "Argument Clinic" tool (tool to generate the function signature, function docstring and the code to parse arguments in C), I asked if we should add support for keyword arguments in str.replace(). The answer was quick: no! It's a deliberate design choice. Quote of Yury Selivanov's message: """ I think Guido explicitly stated that he doesn't like the idea to always allow keyword arguments for all methods. I.e. `str.find('aaa')` just reads better than `str.find(needle='aaa')`. Essentially, the idea is that for most of the builtins that accept one or two arguments, positional-only parameters are better. """ http://bugs.python.org/issue29286#msg285578 I just noticed a module on PyPI to implement this behaviour on Python functions: https://pypi.python.org/pypi/positional My question is: would it make sense to implement this feature in Python directly? If yes, what should be the syntax? Use "/" marker? Use the @positional() decorator? Do you see concrete cases where it's a deliberate choice to deny passing arguments as keywords? Don't you like writing int(x="123") instead of int("123")? :-) (I know that Serhiy Storshake hates the name of the "x" parameter of the int constructor ;-)) By the way, I read that "/" marker is unknown by almost all Python developers, and [...] syntax should be preferred, but inspect.signature() doesn't support this syntax. Maybe we should fix signature() and use [...] format instead? Replace "replace(self, old, new, count=-1, /)" with "replace(self, old, new[, count=-1])" (or maybe even not document the default value?). Python 3.5 help (docstring) uses "S.replace(old, new[, count])". Victor

2 years, 1 month

Implicit string literal concatenation considered harmful?

by Guido van Rossum

I just spent a few minutes staring at a bug caused by a missing comma -- I got a mysterious argument count error because instead of foo('a', 'b') I had written foo('a' 'b'). This is a fairly common mistake, and IIRC at Google we even had a lint rule against this (there was also a Python dialect used for some specific purpose where this was explicitly forbidden). Now, with modern compiler technology, we can (and in fact do) evaluate compile-time string literal concatenation with the '+' operator, so there's really no reason to support 'a' 'b' any more. (The reason was always rather flimsy; I copied it from C but the reason why it's needed there doesn't really apply to Python, as it is mostly useful inside macros.) Would it be reasonable to start deprecating this and eventually remove it from the language? -- --Guido van Rossum (python.org/~guido)

2 years, 7 months

PEP 572: Statement-Local Name Bindings

by Chris Angelico

This is a suggestion that comes up periodically here or on python-dev. This proposal introduces a way to bind a temporary name to the value of an expression, which can then be used elsewhere in the current statement. The nicely-rendered version will be visible here shortly: https://www.python.org/dev/peps/pep-0572/ ChrisA PEP: 572 Title: Syntax for Statement-Local Name Bindings Author: Chris Angelico <rosuav(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 28-Feb-2018 Python-Version: 3.8 Post-History: 28-Feb-2018 Abstract ======== Programming is all about reusing code rather than duplicating it. When an expression needs to be used twice in quick succession but never again, it is convenient to assign it to a temporary name with very small scope. By permitting name bindings to exist within a single statement only, we make this both convenient and safe against collisions. Rationale ========= When an expression is used multiple times in a list comprehension, there are currently several suboptimal ways to spell this, and no truly good ways. A statement-local name allows any expression to be temporarily captured and then used multiple times. Syntax and semantics ==================== In any context where arbitrary Python expressions can be used, a named expression can appear. This must be parenthesized for clarity, and is of the form `(expr as NAME)` where `expr` is any valid Python expression, and `NAME` is a simple name. The value of such a named expression is the same as the incorporated expression, with the additional side-effect that NAME is bound to that value for the remainder of the current statement. Just as function-local names shadow global names for the scope of the function, statement-local names shadow other names for that statement. They can also shadow each other, though actually doing this should be strongly discouraged in style guides. Example usage ============= These list comprehensions are all approximately equivalent:: # Calling the function twice stuff = [[f(x), f(x)] for x in range(5)] # Helper function def pair(value): return [value, value] stuff = [pair(f(x)) for x in range(5)] # Inline helper function stuff = [(lambda v: [v,v])(f(x)) for x in range(5)] # Extra 'for' loop - see also Serhiy's optimization stuff = [[y, y] for x in range(5) for y in [f(x)]] # Expanding the comprehension into a loop stuff = [] for x in range(5): y = f(x) stuff.append([y, y]) # Using a statement-local name stuff = [[(f(x) as y), y] for x in range(5)] If calling `f(x)` is expensive or has side effects, the clean operation of the list comprehension gets muddled. Using a short-duration name binding retains the simplicity; while the extra `for` loop does achieve this, it does so at the cost of dividing the expression visually, putting the named part at the end of the comprehension instead of the beginning. Statement-local name bindings can be used in any context, but should be avoided where regular assignment can be used, just as `lambda` should be avoided when `def` is an option. Open questions ============== 1. What happens if the name has already been used? `(x, (1 as x), x)` Currently, prior usage functions as if the named expression did not exist (following the usual lookup rules); the new name binding will shadow the other name from the point where it is evaluated until the end of the statement. Is this acceptable? Should it raise a syntax error or warning? 2. The current implementation [1] implements statement-local names using a special (and mostly-invisible) name mangling. This works perfectly inside functions (including list comprehensions), but not at top level. Is this a serious limitation? Is it confusing? 3. The interaction with locals() is currently[1] slightly buggy. Should statement-local names appear in locals() while they are active (and shadow any other names from the same function), or should they simply not appear? 4. Syntactic confusion in `except` statements. While technically unambiguous, it is potentially confusing to humans. In Python 3.7, parenthesizing `except (Exception as e):` is illegal, and there is no reason to capture the exception type (as opposed to the exception instance, as is done by the regular syntax). Should this be made outright illegal, to prevent confusion? Can it be left to linters? 5. Similar confusion in `with` statements, with the difference that there is good reason to capture the result of an expression, and it is also very common for `__enter__` methods to return `self`. In many cases, `with expr as name:` will do the same thing as `with (expr as name):`, adding to the confusion. References ========== .. [1] Proof of concept / reference implementation (https://github.com/Rosuav/cpython/tree/statement-local-variables) Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

2 years, 8 months

Medium for discussion potential changes to python (was: PEP 572: Statement-Local Name Bindings)

by Alex Walters

That should probably be its own thread From: Python-ideas [mailto:python-ideas-bounces+tritium-list=sdamon.com@python.org] On Behalf Of Robert Vanden Eynde Sent: Wednesday, February 28, 2018 4:48 PM Cc: python-ideas <python-ideas(a)python.org> Subject: Re: [Python-ideas] PEP 572: Statement-Local Name Bindings We are currently like a dozen of people talking about multiple sections of a single subject. Isn't it easier to talk on a forum? Am I the only one who thinks mailing list isn't easy when lots of people talking about multiple subjects? Of course we would put the link in the mailing list so that everyone can join. A forum (or just few "issues" thread on github) is where we could have different thread in parallel, in my messages I end up with like 10 comments not all related, in a forum we could talk about everything and it would still be organized by subjects. Also, it's more interactive than email on a global list, people can talk to each other in parallel, if I want to answer about a mail that was 10 mail ago, it gets quickly messy. We could all discuss on a gist or some "Issues" thread on GitHub. 2018-02-28 22:38 GMT+01:00 Robert Vanden Eynde <robertve92(a)gmail.com <mailto:robertve92@gmail.com> >: Le 28 févr. 2018 11:43, "Chris Angelico" <rosuav(a)gmail.com <mailto:rosuav@gmail.com> > a écrit : > It's still right-to-left, which is as bad as middle-outward once you > combine it with normal left-to-right evaluation. Python has very > little of this [..] I agree [....] >> 2) talking about the implementation of thektulu in the "where =" part. > ? In the Alternate Syntax, I was talking about adding a link to the thektulu (branch where-expr) <https://github.com/thektulu/cpython/commits/where-expr> implementation as a basis of proof of concept (as you did with the other syntax). >> 3) "C problem that an equals sign in an expression can now create a name inding, rather than performing a comparison." As you agreed, with the "ch with ch = getch()" syntax we won't accidentally switch a "==" for a "=". I agree this syntax : ``` while (ch with ch = getch()): ... ``` doesn't read very well, but in the same way as in C or Java while(ch = getch()){} or worse ((ch = getch()) != null) syntax. Your syntax "while (getch() as ch):" may have a less words, but is still not clearer. As we spoke on Github, having this syntax in a while is only useful if the variable does leak. >> 5) Any expression vs "post for" only > I don't know what the benefit is here, but sure. As long as the > grammar is unambiguous, I don't see any particular reason to reject > this. I would like to see a discussion of pros and cons, some might think like me or disagree, that's a strong langage question. > 6) with your syntax, how does the simple case work (y+2 with y = x+1) ? What simple case? The case where you only use the variable once? I'd write it like this: (x + 1) + 2 >> The issue is not only about reusing variable. > If you aren't using the variable multiple times, there's no point > giving it a name. Unless I'm missing something here? Yes, variables are not there "just because we reuse them", but also to include temporary variables to better understand the code. Same for functions, you could inline functions when used only once, but you introduce them for clarity no ? ``` a = v ** 2 / R # the acceleration in a circular motion f = m * a # law of Newton ``` could be written as ``` f = m * (v ** 2 / R) # compute the force, trivial ``` But having temporary variables help a lot to understand the code, otherwise why would we create temporary variables ? I can give you an example where you do a process and each time the variable is used only one. >> 8) >> (lambda y: [y, y])(x+1) >> Vs >> (lambda y: [y, y])(y=x+1) Ewww. Remind me what the benefit is of writing the variable name that many times? "Explicit" doesn't mean "utterly verbose". Yep it's verbose, lambdas are verbose, that's why we created this PEP isn't it :) > 10) Chaining, in the case of the "with =", in thektulu, parenthesis were > mandatory: > > print((z+3 with z = y+2) with y = x+2) > > What happens when the parenthesis are dropped ? > > print(z+3 with y = x+2 with z = y+2) > > Vs > > print(z+3 with y = x+2 with z = y+2) > > I prefer the first one be cause it's in the same order as the "post for" > > [z + 3 for y in [ x+2 ] for z in [ y+2 ]] > With my proposal, the parens are simply mandatory. Extending this to > make them optional can come later. Indeed, but that's still questions that can be asked. >> 11) Scoping, in the case of the "with =" syntax, I think the parenthesis >> introduce a scope : >> >> print(y + (y+1 where y = 2)) >> >> Would raise a SyntaxError, it's probably better for the variable beeing >> local and not in the current function (that would be a mess). >> >> Remember that in list comp, the variable is not leaked : >> >> x = 5 >> stuff = [y+2 for y in [x+1] >> print(y) # SyntaxError > Scoping is a fundamental part of both my proposal and the others I've > seen here. (BTW, that would be a NameError, not a SyntaxError; it's > perfectly legal to ask for the name 'y', it just hasn't been given any > value.) By my definition, the variable is locked to the statement that > created it, even if that's a compound statement. By the definition of > a "(expr given var = expr)" proposal, it would be locked to that > single expression. Confer the discussion on scoping on github (https://github.com/python/peps/commit/2b4ca20963a24cf5faac054226857ea970547…) : """ In the current implementation it looks like it is like a regular assignment (function local then). Therefore in the expression usage, the usefulness would be debatable (just assign before). But in a list comprehension after the for (as I mentioned in my mail), aka. when used as a replacement for for y in [ x + 1 ] this would make sense. But I think that it would be much better to have a local scope, in the parenthesis. So that print(y+2 where y = x + 1) wouldn't leak y. And when there are no parenthesis like in a = y+2 where y = x+1, it would imply one, giving the same effect as a = (y+2 where y = x+1). Moreover, it would naturally shadow variables in the outermost scope. This would imply while data where data = sock.read(): does not leak data but as a comparison with C and Java, the syntax while((data = sock.read()) != null) is really really ugly and confusing. """

2 years, 8 months

List assignment - extended slicing inconsistency

by Alexander Heger

What little documentation I could find, providing a stride on the assignment target for a list is supposed to trigger 'advanced slicing' causing element-wise replacement - and hence requiring that the source iterable has the appropriate number of elements. >>> a = [0,1,2,3] >>> a[::2] = [4,5] >>> a [4, 1, 5, 3] >>> a[::2] = [4,5,6] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: attempt to assign sequence of size 3 to extended slice of size 2 This is in contrast to regular slicing (*without* a stride), allowing to replace a *range* by another sequence of arbitrary length. >>> a = [0,1,2,3] >>> a[:3] = [4] >>> a [4, 3] Issue ===== When, however, a stride of `1` is specified, advanced slicing is not triggered. >>> a = [0,1,2,3] >>> a[:3:1] = [4] >>> a [4, 3] If advanced slicing had been triggered, there should have been a ValueError instead. Expected behaviour: >>> a = [0,1,2,3] >>> a[:3:1] = [4] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: attempt to assign sequence of size 1 to extended slice of size 3 I think that is an inconsistency in the language that should be fixed. Why do we need this? ==================== One may want this as extra check as well so that list does not change size. Depending on implementation, it may come with performance benefits as well. One could, though, argue that you still get the same result if you do all correctly >>> a = [0,1,2,3] >>> a[:3:1] = [4,5,6] >>> a [4, 5, 6, 3] But I disagree that there should be no error when it is wrong. *Strides that are not None should always trigger advanced slicing.* Other Data Types ================ This change should also be applied to bytearray, etc., though see below. Concerns ======== It may break some code that uses advanced slicing and expects regular slicing to occur? These cases should be rare, and the error message should be clear enough to allow fixes? I assume these cases should be exceptionally rare. If the implementation relies on `slice.indices(len(seq))[2] == 1` to determine about advance slicing or not, that would require some refactoring. If it is only `slice.stride in (1, None)` then this could easily replaced by checking against None. Will there be issues with syntax consistency with other data types, in particular outside the core library? - I always found that the dynamic behaviour of lists w/r non-advanced slicing to be somewhat peculiar in the first place, though, undeniably, it can be immensely useful. - Most external data types with fixed memory such as numpy do not have this dynamic flexibility, and the behavior of regular slicing on assignment is the same as regular slicing. The proposed change would increase consistency with these other data types. More surprises ============== >>> import array >>> a[1::2] = a[3:3] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: attempt to assign sequence of size 0 to extended slice of size 2 whereas >>> a = [1,2,3,4,5] >>> a[1::2] = a[3:3] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: attempt to assign sequence of size 0 to extended slice of size 2 >>> a = bytearray(b'12345') >>> a[1::2] = a[3:3] >>> a bytearray(b'135') but numpy >>> import numpy as np >>> a = np.array([1,2,3,4,5]) >>> a[1::2] = a[3:3] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: could not broadcast input array from shape (0) into shape (2) and >>> import numpy as np >>> a[1:2] = a[3:3] Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: could not broadcast input array from shape (0) into shape (1) The latter two as expected. memoryview behaves the same. Issue 2 ======= Whereas NumPy is know to behave differently as a data type with fixed memory layout, and is not part of the standard library anyway, the difference in behaviour between lists and arrays I find disconcerting. This should be resolved to a consistent behaviour. Proposal 2 ========== Arrays and bytearrays should should adopt the same advanced slicing behaviour I suggest for lists. Concerns 2 ========== This has the potential for a lot more side effects in existing code, but as before in most cases error message should be triggered. Summary ======= I find it it not acceptable as a good language design that there is a large range of behaviour on slicing in assignment target for the different native (and standard library) data type of seemingly similar kind, and that users have to figure out for each data type by testing - or at the very least remember if documented - how it behaves on slicing in assignment targets. There should be a consistent behaviour at the very least, ideally even one with a clear user interface as suggested for lists. -Alexander

2 years, 8 months

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Python-ideas February 2018