Mailman 3 March 2018 - Python-ideas

Adding a thin wrapper class around the functions in stdlib.heapq
by bunslow 18 Feb '22

18 Feb '22

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 12 Mar '21

12 Mar '21

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 16 Dec '20

16 Dec '20

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 07 Mar '20

07 Mar '20

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 22 Feb '20

22 Feb '20

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 17 Nov '19

17 Nov '19

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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Dart like multi line strings identation
by Marius Räsener 08 Feb '19

08 Feb '19

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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Asynchronous exception handling around with/try statement borders
by Erik Bray 24 Sep '18

24 Sep '18

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?

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Positional-only parameters
by Victor Stinner 11 Sep '18

11 Sep '18

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

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PEP 572: Statement-Local Name Bindings, take three!
by Chris Angelico 11 Apr '18

11 Apr '18

Apologies for letting this languish; life has an annoying habit of getting in the way now and then. Feedback from the previous rounds has been incorporated. From here, the most important concern and question is: Is there any other syntax or related proposal that ought to be mentioned here? If this proposal is rejected, it should be rejected with a full set of alternatives. Text of PEP is below; formatted version will be live shortly (if it isn't already) at: 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, 02-Mar-2018, 23-Mar-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 small scope. By permitting name bindings to exist within a single statement only, we make this both convenient and safe against name collisions. Rationale ========= When a subexpression is used multiple times in a list comprehension, there are currently several ways to spell this, none of which is universally accepted as ideal. A statement-local name allows any subexpression to be temporarily captured and then used multiple times. Additionally, this syntax can in places be used to remove the need to write an infinite loop with a ``break`` in it. Capturing part of a ``while`` loop's condition can improve the clarity of the loop header while still making the actual value available within the loop body. 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 technically also shadow each other, though actually doing this should not be encouraged.) Assignment to statement-local names is ONLY through this syntax. Regular assignment to the same name will remove the statement-local name and affect the name in the surrounding scope (function, class, or module). Statement-local names never appear in locals() or globals(), and cannot be closed over by nested functions. Execution order and its consequences ------------------------------------ Since the statement-local name binding lasts from its point of execution to the end of the current statement, this can potentially cause confusion when the actual order of execution does not match the programmer's expectations. Some examples:: # A simple statement ends at the newline or semicolon. a = (1 as y) print(y) # NameError # The assignment ignores the SLNB - this adds one to 'a' a = (a + 1 as a) # Compound statements usually enclose everything... if (re.match(...) as m): print(m.groups(0)) print(m) # NameError # ... except when function bodies are involved... if (input("> ") as cmd): def run_cmd(): print("Running command", cmd) # NameError # ... but function *headers* are executed immediately if (input("> ") as cmd): def run_cmd(cmd=cmd): # Capture the value in the default arg print("Running command", cmd) # Works Function bodies, in this respect, behave the same way they do in class scope; assigned names are not closed over by method definitions. Defining a function inside a loop already has potentially-confusing consequences, and SLNBs do not materially worsen the existing situation. Differences from regular assignment statements ---------------------------------------------- Using ``(EXPR as NAME)`` is similar to ``NAME = EXPR``, but has a number of important distinctions. * Assignment is a statement; an SLNB is an expression whose value is the same as the object bound to the new name. * SLNBs disappear at the end of their enclosing statement, at which point the name again refers to whatever it previously would have. SLNBs can thus shadow other names without conflict (although deliberately doing so will often be a sign of bad code). * SLNBs cannot be closed over by nested functions, and are completely ignored for this purpose. * SLNBs do not appear in ``locals()`` or ``globals()``. * An SLNB cannot be the target of any form of assignment, including augmented. Attempting to do so will remove the SLNB and assign to the fully-scoped name. In many respects, an SLNB is akin to a local variable in an imaginary nested function, except that the overhead of creating and calling a function is bypassed. As with names bound by ``for`` loops inside list comprehensions, SLNBs cannot "leak" into their surrounding scope. Example usage ============= These list comprehensions are all approximately equivalent:: # Calling the function twice stuff = [[f(x), x/f(x)] for x in range(5)] # External helper function def pair(x, value): return [value, x/value] stuff = [pair(x, f(x)) for x in range(5)] # Inline helper function stuff = [(lambda y: [y,x/y])(f(x)) for x in range(5)] # Extra 'for' loop - potentially could be optimized internally stuff = [[y, x/y] for x in range(5) for y in [f(x)]] # Iterating over a genexp stuff = [[y, x/y] for x, y in ((x, f(x)) for x in range(5))] # Expanding the comprehension into a loop stuff = [] for x in range(5): y = f(x) stuff.append([y, x/y]) # Wrapping the loop in a generator function def g(): for x in range(5): y = f(x) yield [y, x/y] stuff = list(g()) # Using a statement-local name stuff = [[(f(x) as y), x/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. As the name's scope extends to the full current statement, even a block statement, this can be used to good effect in the header of an ``if`` or ``while`` statement:: # Current Python, not caring about function return value while input("> ") != "quit": print("You entered a command.") # Current Python, capturing return value - four-line loop header while True: command = input("> "); if command == "quit": break print("You entered:", command) # Proposed alternative to the above while (input("> ") as command) != "quit": print("You entered:", command) # See, for instance, Lib/pydoc.py if (re.search(pat, text) as match): print("Found:", match.group(0)) while (sock.read() as data): print("Received data:", data) Particularly with the ``while`` loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value. Performance costs ================= The cost of SLNBs must be kept to a minimum, particularly when they are not used; the normal case MUST NOT be measurably penalized. SLNBs are expected to be uncommon, and using many of them in a single function should definitely be discouraged. Thus the current implementation uses a linked list of SLNB cells, with the absence of such a list being the normal case. This list is used for code compilation only; once a function's bytecode has been baked in, execution of that bytecode has no performance cost compared to regular assignment. Other Python implementations may choose to do things differently, but a zero run-time cost is strongly recommended, as is a minimal compile-time cost in the case where no SLNBs are used. Forbidden special cases ======================= In two situations, the use of SLNBs makes no sense, and could be confusing due to the ``as`` keyword already having a different meaning in the same context. 1. Exception catching:: try: ... except (Exception as e1) as e2: ... The expression ``(Exception as e1)`` has the value ``Exception``, and creates an SLNB ``e1 = Exception``. This is generally useless, and creates the potential confusion in that these two statements do quite different things: except (Exception as e1): except Exception as e2: The latter captures the exception **instance**, while the former captures the ``Exception`` **type** (not the type of the raised exception). 2. Context managers:: lock = threading.Lock() with (lock as l) as m: ... This captures the original Lock object as ``l``, and the result of calling its ``__enter__`` method as ``m``. As with ``except`` statements, this creates a situation in which parenthesizing an expression subtly changes its semantics, with the additional pitfall that this will frequently work (when ``x.__enter__()`` returns x, eg with file objects). Both of these are forbidden; creating SLNBs in the headers of these statements will result in a SyntaxError. Alternative proposals ===================== Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above. 1. ``where``, ``let``, ``given``:: stuff = [(y, x/y) where y = f(x) for x in range(5)] stuff = [(y, x/y) let y = f(x) for x in range(5)] stuff = [(y, x/y) given y = f(x) for x in range(5)] This brings the subexpression to a location in between the 'for' loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, ``where`` reads the most cleanly, but also has the greatest potential for conflict (eg SQLAlchemy and numpy have ``where`` methods, as does ``tkinter.dnd.Icon`` in the standard library). 2. ``with NAME = EXPR``:: stuff = [(y, x/y) with y = f(x) for x in range(5)] As above, but reusing the `with` keyword. Doesn't read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into "longhand" for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why "with NAME = EXPR:" cannot be used as a statement on its own. 3. ``with EXPR as NAME``:: stuff = [(y, x/y) with f(x) as y for x in range(5)] As per option 2, but using ``as`` rather than an equals sign. Aligns syntactically with other uses of ``as`` for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of ``with`` inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax. 4. ``EXPR as NAME`` without parentheses:: stuff = [[f(x) as y, x/y] for x in range(5)] Omitting the parentheses from this PEP's proposed syntax introduces many syntactic ambiguities. Requiring them in all contexts leaves open the option to make them optional in specific situations where the syntax is unambiguous (cf generator expressions as sole parameters in function calls), but there is no plausible way to make them optional everywhere. 5. Adorning statement-local names with a leading dot:: stuff = [[(f(x) as .y), x/.y] for x in range(5)] This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable's scope is encoded into its name, making refactoring harder. This syntax is quite viable, and could be promoted to become the current recommendation if its advantages are found to outweigh its cost. 6. Allowing ``(EXPR as NAME)`` to assign to any form of name. This is exactly the same as the promoted proposal, save that the name is bound in the same scope that it would otherwise have. Any expression can assign to any name, just as it would if the ``=`` operator had been used. Such variables would leak out of the statement into the enclosing function, subject to the regular behaviour of comprehensions (since they implicitly create a nested function, the name binding would be restricted to the comprehension itself, just as with the names bound by ``for`` loops). 7. Enhancing ``if`` and ``while`` syntax to permit the capture of their conditions:: if re.search(pat, text) as match: print("Found:", match.group(0)) This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return `''` when done), and completely useless in more complicated cases (eg where the condition is ``f(x) < 0`` and you want to capture the value of ``f(x)``). It also has no benefit to list comprehensions. Discrepancies in the current implementation =========================================== 1. SLNBs are implemented using a special (and mostly-invisible) name mangling. They may sometimes appear in globals() and/or locals() with their simple or mangled names (but buggily and unreliably). They should be suppressed as though they were guinea pigs. 2. The forbidden special cases do not yet raise SyntaxError. 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:

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Python-ideas March 2018