Python-ideas June 2015

python-ideas@python.org

85 participants
48 discussions

@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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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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Implicit string literal concatenation considered harmful?
by Guido van Rossum 14 Mar '18

14 Mar '18

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)

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solving multi-core Python
by Eric Snow 03 Jun '16

03 Jun '16

tl;dr Let's exploit multiple cores by fixing up subinterpreters, exposing them in Python, and adding a mechanism to safely share objects between them. This proposal is meant to be a shot over the bow, so to speak. I plan on putting together a more complete PEP some time in the future, with content that is more refined along with references to the appropriate online resources. Feedback appreciated! Offers to help even more so! :) -eric -------- Python's multi-core story is murky at best. Not only can we be more clear on the matter, we can improve Python's support. The result of any effort must make multi-core (i.e. parallelism) support in Python obvious, unmistakable, and undeniable (and keep it Pythonic). Currently we have several concurrency models represented via threading, multiprocessing, asyncio, concurrent.futures (plus others in the cheeseshop). However, in CPython the GIL means that we don't have parallelism, except through multiprocessing which requires trade-offs. (See Dave Beazley's talk at PyCon US 2015.) This is a situation I'd like us to solve once and for all for a couple of reasons. Firstly, it is a technical roadblock for some Python developers, though I don't see that as a huge factor. Regardless, secondly, it is especially a turnoff to folks looking into Python and ultimately a PR issue. The solution boils down to natively supporting multiple cores in Python code. This is not a new topic. For a long time many have clamored for death to the GIL. Several attempts have been made over the years and failed to do it without sacrificing single-threaded performance. Furthermore, removing the GIL is perhaps an obvious solution but not the only one. Others include Trent Nelson's PyParallels, STM, and other Python implementations.. Proposal ======= In some personal correspondence Nick Coghlan, he summarized my preferred approach as "the data storage separation of multiprocessing, with the low message passing overhead of threading". For Python 3.6: * expose subinterpreters to Python in a new stdlib module: "subinterpreters" * add a new SubinterpreterExecutor to concurrent.futures * add a queue.Queue-like type that will be used to explicitly share objects between subinterpreters This is less simple than it might sound, but presents what I consider the best option for getting a meaningful improvement into Python 3.6. Also, I'm not convinced that the word "subinterpreter" properly conveys the intent, for which subinterpreters is only part of the picture. So I'm open to a better name. Influences ======== Note that I'm drawing quite a bit of inspiration from elsewhere. The idea of using subinterpreters to get this (more) efficient isolated execution is not my own (I heard it from Nick). I have also spent quite a bit of time and effort researching for this proposal. As part of that, a number of people have provided invaluable insight and encouragement as I've prepared, including Guido, Nick, Brett Cannon, Barry Warsaw, and Larry Hastings. Additionally, Hoare's "Communicating Sequential Processes" (CSP) has been a big influence on this proposal. FYI, CSP is also the inspiration for Go's concurrency model (e.g. goroutines, channels, select). Dr. Sarah Mount, who has expertise in this area, has been kind enough to agree to collaborate and even co-author the PEP that I hope comes out of this proposal. My interest in this improvement has been building for several years. Recent events, including this year's language summit, have driven me to push for something concrete in Python 3.6. The subinterpreter Module ===================== The subinterpreters module would look something like this (a la threading/multiprocessing): settrace() setprofile() stack_size() active_count() enumerate() get_ident() current_subinterpreter() Subinterpreter(...) id is_alive() running() -> Task or None run(...) -> Task # wrapper around PyRun_*, auto-calls Task.start() destroy() Task(...) # analogous to a CSP process id exception() # other stuff? # for compatibility with threading.Thread: name ident is_alive() start() run() join() Channel(...) # shared by passing as an arg to the subinterpreter-running func # this API is a bit uncooked still... pop() push() poison() # maybe select() # maybe Note that Channel objects will necessarily be shared in common between subinterpreters (where bound). This sharing will happen when the one or more of the parameters to the function passed to Task() is a Channel. Thus the channel would be open to the (sub)interpreter calling Task() (or Subinterpreter.run()) and to the new subinterpreter. Also, other channels could be fed into such a shared channel, whereby those channels would then likewise be shared between the interpreters. I don't know yet if this module should include *all* the essential pieces to implement a complete CSP library. Given the inspiration that CSP is providing, it may make sense to support it fully. It would be interesting then if the implementation here allowed the (complete?) formalisms provided by CSP (thus, e.g. rigorous proofs of concurrent system models). I expect there will also be a _subinterpreters module with low-level implementation-specific details. Related Ideas and Details Under Consideration ==================================== Some of these are details that need to be sorted out. Some are secondary ideas that may be appropriate to address in this proposal or may need to be tabled. I have some others but these should be sufficient to demonstrate the range of points to consider. * further coalesce the (concurrency/parallelism) abstractions between threading, multiprocessing, asyncio, and this proposal * only allow one running Task at a time per subinterpreter * disallow threading within subinterpreters (with legacy support in C) + ignore/remove the GIL within subinterpreters (since they would be single-threaded) * use the GIL only in the main interpreter and for interaction between subinterpreters (and a "Local Interpreter Lock" for within a subinterpreter) * disallow forking within subinterpreters * only allow passing plain functions to Task() and Subinterpreter.run() (exclude closures, other callables) * object ownership model + read-only in all but 1 subinterpreter + RW in all subinterpreters + only allow 1 subinterpreter to have any refcounts to an object (except for channels) * only allow immutable objects to be shared between subinterpreters * for better immutability, move object ref counts into a separate table * freeze (new machinery or memcopy or something) objects to make them (at least temporarily) immutable * expose a more complete CSP implementation in the stdlib (or make the subinterpreters module more compliant) * treat the main interpreter differently than subinterpreters (or treat it exactly the same) * add subinterpreter support to asyncio (the interplay between them could be interesting) Key Dependencies ================ There are a few related tasks/projects that will likely need to be resolved before subinterpreters in CPython can be used in the proposed manner. The proposal could implemented either way, but it will help the multi-core effort if these are addressed first. * fixes to subinterpreter support (there are a couple individuals who should be able to provide the necessary insight) * PEP 432 (will simplify several key implementation details) * improvements to isolation between subinterpreters (file descriptors, env vars, others) Beyond those, the scale and technical scope of this project means that I am unlikely to be able to do all the work myself to land this in Python 3.6 (though I'd still give it my best shot). That will require the involvement of various experts. I expect that the project is divisible into multiple mostly independent pieces, so that will help. Python Implementations =================== They can correct me if I'm wrong, but from what I understand both Jython and IronPython already have subinterpreter support. I'll be soliciting feedback from the different Python implementors about subinterpreter support. C Extension Modules ================= Subinterpreters already isolate extension modules (and built-in modules, including sys). PEP 384 provides some help too. However, global state in C can easily leak data between subinterpreters, breaking the desired data isolation. This is something that will need to be addressed as part of the effort.

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Reprs of classes and functions
by Serhiy Storchaka 05 Aug '15

05 Aug '15

This idea is already casually mentioned, but sank deep into the threads of the discussion. Raise it up. Currently reprs of classes and functions look as: >>> int <class 'int'> >>> int.from_bytes <built-in method from_bytes of type object at 0x826cf60> >>> open <built-in function open> >>> import collections >>> collections.Counter <class 'collections.Counter'> >>> collections.Counter.fromkeys <bound method Counter.fromkeys of <class 'collections.Counter'>> >>> collections.namedtuple <function namedtuple at 0xb6fc4adc> What if change default reprs of classes and functions to just full qualified name __module__ + '.' + __qualname__ (or just __qualname__ if __module__ is builtins)? This will look more neatly. And such reprs are evaluable.

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Fwd: [Python-Dev] An yocto change proposal in logging module to simplify structured logs support
by Ludovic Gasc 06 Jul '15

06 Jul '15

Hi Python-Ideas ML, To resume quickly the idea: I wish to add "extra" attribute to LogMessage, to facilitate structured logs generation. For more details with use case and example, you can read message below. Before to push the patch on bugs.python.org, I'm interested in by your opinions: the patch seems to be too simple to be honest. Regards. -- Ludovic Gasc (GMLudo) http://www.gmludo.eu/ ---------- Forwarded message ---------- From: Guido van Rossum <guido(a)python.org> Date: 2015-05-24 23:44 GMT+02:00 Subject: Re: [Python-Dev] An yocto change proposal in logging module to simplify structured logs support To: Ludovic Gasc <gmludo(a)gmail.com> Ehh, python-ideas? On Sun, May 24, 2015 at 10:22 AM, Ludovic Gasc <gmludo(a)gmail.com> wrote: > Hi, > > 1. The problem > > For now, when you want to write a log message, you concatenate the data > from your context to generate a string: In fact, you convert your > structured data to a string. > When a sysadmin needs to debug your logs when something is wrong, he must > write regular expressions to extract interesting data. > > Often, he must find the beginning of the interesting log and follow the > path. Sometimes, you can have several requests in the same time in the log, > it's harder to find interesting log. > In fact, with regular expressions, the sysadmin tries to convert the log > lines strings to structured data. > > 2. A possible solution > > You should provide a set of regular expressions to your sysadmins to help > them to find the right logs, however, another approach is possible: > structured logs. > Instead of to break your data structure to push in the log message, the > idea is to keep the data structure, to attach that as metadata of the log > message. > For now, I know at least Logstash and Journald that can handle structured > logs and provide a query tool to extract easily logs. > > 3. A concrete example with structured logs > > As most Web developers, we build HTTP daemons used by several different > human clients in the same time. > In the Python source code, to support structured logs, you don't have a > big change, you can use "extra" parameter for that, example: > > [handle HTTP request] > LOG.debug('Receive a create_or_update request', extra={'request_id': > request.request_id, > > 'account_id': account_id, > > 'aiohttp_request': request, > > 'payload': str(payload)}) > [create data in database] > LOG.debug('Callflow created', extra={'account_id': account_id, > 'request_id': > request.request_id, > 'aiopg_cursor': cur, > 'results': row}) > > Now, if you want, you can enhance the structured log with a custom logging > Handler, because the standard journald handler doesn't know how to handle > aiohttp_request or aiopg_cursor. > My example is based on journald, but you can write an equivalent version > with python-logstash: > #### > from systemdream.journal.handler import JournalHandler > > class Handler(JournalHandler): > # Tip: on a system without journald, use socat to test: > # socat UNIX-RECV:/run/systemd/journal/socket STDIN > def emit(self, record): > if record.extra: > # import ipdb; ipdb.set_trace() > if 'aiohttp_request' in record.extra: > record.extra['http_method'] = > record.extra['aiohttp_request'].method > record.extra['http_path'] = > record.extra['aiohttp_request'].path > record.extra['http_headers'] = > str(record.extra['aiohttp_request'].headers) > del(record.extra['aiohttp_request']) > if 'aiopg_cursor' in record.extra: > record.extra['pg_query'] = > record.extra['aiopg_cursor'].query.decode('utf-8') > record.extra['pg_status_message'] = > record.extra['aiopg_cursor'].statusmessage > record.extra['pg_rows_count'] = > record.extra['aiopg_cursor'].rowcount > del(record.extra['aiopg_cursor']) > super().emit(record) > #### > > And you can enable this custom handler in your logging config file like > this: > [handler_journald] > class=XXXXXXXXXX.utils.logs.Handler > args=() > formatter=detailed > > And now, with journalctl, you can easily extract logs, some examples: > Logs messages from 'lg' account: > journalctl ACCOUNT_ID=lg > All HTTP requests that modify the 'lg' account (PUT, POST and DELETE): > journalctl ACCOUNT_ID=lg HTTP_METHOD=PUT > HTTP_METHOD=POST HTTP_METHOD=DELETE > Retrieve all logs from one specific HTTP request: > journalctl REQUEST_ID=130b8fa0-6576-43b6-a624-4a4265a2fbdd > All HTTP requests with a specific path: > journalctl HTTP_PATH=/v1/accounts/lg/callflows > All logs of "create" function in the file "example.py" > journalctl CODE_FUNC=create CODE_FILE=/path/example.py > > If you already do a troubleshooting on a production system, you should > understand the interest of this: > In fact, it's like to have SQL queries capabilities, but it's logging > oriented. > We use that since a small time on one of our critical daemon that handles > a lot of requests across several servers, it's already adopted from our > support team. > > 4. The yocto issue with the Python logging module > > I don't explain here a small part of my professional life for my pleasure, > but to help you to understand the context and the usages, because my patch > for logging is very small. > If you're an expert of Python logging, you already know that my Handler > class example I provided above can't run on a classical Python logging, > because LogRecord doesn't have an extra attribute. > > extra parameter exists in the Logger, but, in the LogRecord, it's merged > as attributes of LogRecord: > https://github.com/python/cpython/blob/master/Lib/logging/__init__.py#L1386 > > It means, that when the LogRecord is sent to the Handler, you can't > retrieve the dict from the extra parameter of logger. > The only way to do that without to patch Python logging, is to rebuild by > yourself the dict with a list of official attributes of LogRecord, as is > done in python-logstash: > > https://github.com/vklochan/python-logstash/blob/master/logstash/formatter.… > At least to me, it's a little bit dirty. > > My quick'n'dirty patch I use for now on our CPython on production: > > diff --git a/Lib/logging/__init__.py b/Lib/logging/__init__.py > index 104b0be..30fa6ef 100644 > --- a/Lib/logging/__init__.py > +++ b/Lib/logging/__init__.py > @@ -1382,6 +1382,7 @@ class Logger(Filterer): > """ > rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, > func, > sinfo) > + rv.extra = extra > if extra is not None: > for key in extra: > if (key in ["message", "asctime"]) or (key in > rv.__dict__): > > At least to me, it should be cleaner to add "extra" as parameter > of _logRecordFactory, but I've no idea of side effects, I understand that > logging module is critical, because it's used everywhere. > However, except with python-logstash, to my knowledge, extra parameter > isn't massively used. > The only backward incompatibility I see with a new extra attribute of > LogRecord, is that if you have a log like this: > LOG.debug('message', extra={'extra': 'example'}) > It will raise a KeyError("Attempt to overwrite 'extra' in LogRecord") > exception, but, at least to me, the probability of this use case is near to > 0. > > Instead of to "maintain" this yocto patch, even it's very small, I should > prefer to have a clean solution in Python directly. > > Thanks for your remarks. > > Regards. > -- > Ludovic Gasc (GMLudo) > http://www.gmludo.eu/ > > _______________________________________________ > Python-Dev mailing list > Python-Dev(a)python.org > https://mail.python.org/mailman/listinfo/python-dev > Unsubscribe: > https://mail.python.org/mailman/options/python-dev/guido%40python.org > > -- --Guido van Rossum (python.org/~guido)

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Enabling access to the AST for Python code
by Ben Hoyt 04 Jul '15

04 Jul '15

Hi Python Ideas folks, (I previously posted a similar message on Python-Dev, but it's a better fit for this list. See that thread here: https://mail.python.org/pipermail/python-dev/2015-May/140063.html) Enabling access to the AST for compiled code would make some cool things possible (C# LINQ-style ORMs, for example), and not knowing too much about this part of Python internals, I'm wondering how possible and practical this would be. Context: PonyORM (http://ponyorm.com/) allows you to write regular Python generator expressions like this: select(c for c in Customer if sum(c.orders.price) > 1000) which compile into and run SQL like this: SELECT "c"."id" FROM "Customer" "c" LEFT JOIN "Order" "order-1" ON "c"."id" = "order-1"."customer" GROUP BY "c"."id" HAVING coalesce(SUM("order-1"."total_price"), 0) > 1000 I think the Pythonic syntax here is beautiful. But the tricks PonyORM has to go to get it are ... not quite so beautiful. Because the AST is not available, PonyORM decompiles Python bytecode into an AST first, and then converts that to SQL. (More details on all that from author's EuroPython talk at http://pyvideo.org/video/2968) PonyORM needs the AST just for generator expressions and lambda functions, but obviously if this kind of AST access feature were in Python it'd probably be more general. I believe C#'s LINQ provides something similar, where if you're developing a LINQ converter library (say LINQ to SQL), you essentially get the AST of the code ("expression tree") and the library can do what it wants with that. (I know that there's the "ast" module and ast.parse(), which can give you an AST given a *source string*, but that's not very convenient here.) What would it take to enable this kind of AST access in Python? Is it possible? Is it a good idea? -Ben

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Are there asynchronous generators?
by Adam Bartoš 03 Jul '15

03 Jul '15

Hello, I had a generator producing pairs of values and wanted to feed all the first members of the pairs to one consumer and all the second members to another consumer. For example: def pairs(): for i in range(4): yield (i, i ** 2) biconsumer(sum, list)(pairs()) -> (6, [0, 1, 4, 9]) The point is I wanted the consumers to be suspended and resumed in a coordinated manner: The first producer is invoked, it wants the first element. The coordinator implemented by biconsumer function invokes pairs(), gets the first pair and yields its first member to the first consumer. Then it wants the next element, but now it's the second consumer's turn, so the first consumer is suspended and the second consumer is invoked and fed with the second member of the first pair. Then the second producer wants the next element, but it's the first consumer's turn… and so on. In the end, when the stream of pairs is exhausted, StopIteration is thrown to both consumers and their results are combined. The cooperative asynchronous nature of the execution reminded me asyncio and coroutines, so I thought that biconsumer may be implemented using them. However, it seems that it is imposible to write an "asynchronous generator" since the "yielding pipe" is already used for the communication with the scheduler. And even if it was possible to make an asynchronous generator, it is not clear how to feed it to a synchronous consumer like sum() or list() function. With PEP 492 the concepts of generators and coroutines were separated, so asyncronous generators may be possible in theory. An ordinary function has just the returning pipe – for returning the result to the caller. A generator has also a yielding pipe – used for yielding the values during iteration, and its return pipe is used to finish the iteration. A native coroutine has a returning pipe – to return the result to a caller just like an ordinary function, and also an async pipe – used for communication with a scheduler and execution suspension. An asynchronous generator would just have both yieling pipe and async pipe. So my question is: was the code like the following considered? Does it make sense? Or are there not enough uses cases for such code? I found only a short mention in https://www.python.org/dev/peps/pep-0492/#coroutine-generators, so possibly these coroutine-generators are the same idea. async def f(): number_string = await fetch_data() for n in number_string.split(): yield int(n) async def g(): result = async/await? sum(f()) return result async def h(): the_sum = await g() As for explanation about the execution of h() by an event loop: h is a native coroutine called by the event loop, having both returning pipe and async pipe. The returning pipe leads to the end of the task, the async pipe is used for cummunication with the scheduler. Then, g() is called asynchronously – using the await keyword means the the access to the async pipe is given to the callee. Then g() invokes the asyncronous generator f() and gives it the access to its async pipe, so when f() is yielding values to sum, it can also yield a future to the scheduler via the async pipe and suspend the whole task. Regards, Adam Bartoš

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Make os.pipe() return a namedtuple.
by Jonathan Slenders 30 Jun '15

30 Jun '15

Could we do that? Is there is reason it's not already a namedtuple? I always forget what the read-end and what the write-end of the pipe is, and I use it quite regularly. Jonathan

9 14

millisecond and microsecond times without floats
by Paul Sokolovsky 27 Jun '15

27 Jun '15

Hello from MicroPython, a lean Python implementation scaling down to run even on microcontrollers (https://github.com/micropython/micropython). Our target hardware base oftentimes lacks floating point support, and using software emulation is expensive. So, we would like to have versions of some timing functions, taking/returning millisecond and/or microsecond values as integers. The most functionality we're interested in: 1. Delays 2. Relative time (from an arbitrary starting point, expected to be wrapped) 3. Calculating time differences, with immunity to wrap-around. The first presented assumption is to use "time.sleep()" for delays, "time.monotonic()" for relative time as the base. Would somebody gave alternative/better suggestions? Second question is how to modify their names for millisecond/microsecond versions. For sleep(), "msleep" and "usleep" would be concise possibilities, but that doesn't map well to monotonic(), leading to "mmonotonic". So, better idea is to use "_ms" and "_us" suffixes: sleep_ms() sleep_us() monotonic_ms() monotonic_us() Point 3 above isn't currently addressed by time module at all. https://www.python.org/dev/peps/pep-0418/ mentions some internal workaround for overflows/wrap-arounds on some systems. Due to lean-ness of our hardware base, we'd like to make this matter explicit to the applications and avoid internal workarounds. Proposed solution is to have time.elapsed(time1, time2) function, which can take values as returned by monotonic_ms(), monotonic_us(). Assuming that results of both functions are encoded and wrap consistently (this is reasonable assumption), there's no need for 2 separate elapsed_ms(), elapsed_us() function. So, the above are rough ideas we (well, I) have. We'd like to get wider Python community feedback on them, see if there're better/alternative ideas, how Pythonic it is, etc. To clarify, this should not be construed as proposal to add the above functions to CPython. -- Best regards, Paul mailto:pmiscml@gmail.com

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Python-ideas June 2015