Mailman 3 March 2020 - 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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Make fnmatch.filter accept a tuple of patterns
by Andre Delfino 26 Mar '21

26 Mar '21

Frequently, while globbing, one needs to work with multiple extensions. I’d like to propose for fnmatch.filter to handle a tuple of patterns (while preserving the single str argument functionality, alas str.endswith), as a first step for glob.i?glob to accept multiple patterns as well. Here is the implementation I came up with: https://github.com/python/cpython/compare/master...andresdelfino:fnmatch-mu… If this is deemed reasonable, I’ll write tests and documentation updates. Any opinion?

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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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Add command-line option to unittest for enabling post-mortem debugging
by Dominik Vilsmeier 09 Jan '21

09 Jan '21

Consider the following example: import unittest def foo(): for x in [1, 2, 'oops', 4]: print(x + 100) class TestFoo(unittest.TestCase): def test_foo(self): self.assertIs(foo(), None) if __name__ == '__main__': unittest.main() If we were calling `foo` directly we could enter post-mortem debugging via `python -m pdb test.py`. However since `foo` is wrapped in a test case, `unittest` eats the exception and thus prevents post-mortem debugging. `--failfast` doesn't help, the exception is still swallowed. Since I am not aware of a solution that enables post-mortem debugging in such a case (without modifying the test scripts, please correct me if one exists), I propose adding a command-line option to `unittest` for [running test cases in debug mode](https://docs.python.org/3/library/unittest.html#unittest.TestCase.deb… so that post-mortem debugging can be used. P.S.: There is also [this SO question](https://stackoverflow.com/q/4398967/3767239) on a similar topic.

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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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Compound with .. else statement
by Jimmy Thrasibule 09 Sep '20

09 Sep '20

Hi, In Python, there are multiple [compound statements](https://docs.python.org/3/reference/compound_stmts.html) with the `else` keyword. For example: ``` for x in iterable: if x == sentinel: break else: print("Sentinel not found.") ``` or: ``` try: do_something_sensitive() except MyError: print("Oops!") else: print("We're all safe.") ``` In my situation, I would like to mix the `with` statement with `else`. In this case I would like that if no exception is raised within the `with` to run the `else` part. For example: ``` with my_context(): do_something_sensitive() else: print("We're all safe.") ``` Now imagine that in my `try .. except` block I have some heavy setup to do before `do_something_sensitive()` and some heavy cleanup when the exception occurs. I'd like my context manager to do the preparation work, execute the body, and cleanup. Or execute my else block only if there is no exception. Is there already a way to accomplish this in Python or can this be a nice to have? Regards, Jimmy

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Make `del x` an expression evaluating to `x`
by Eric Wieser 12 Jun '20

12 Jun '20

TL;DR: should we make `del x` an expression that returns the value of `x`. ## Motivation I noticed yesterday that `itertools.combinations` has an optimization for when the returned tuple has no remaining ref-counts, and reuses it - namely, the following code: >>> for v in itertools.combinations([1, 2, 3], 1): ... print(id(v)) ... del v # without this, the optimization can't take place 2500926199840 2500926199840 2500926199840 will print the same id three times. However, when used as a list comprehension, the optimization can't step in, and I have no way of using the `del` keyword >>> [id(v) for v in itertools.combinations([1, 2, 3], 1)] [2500926200992, 2500926199072, 2500926200992] `itertools.combinations` is not the only place to make this optimization - parts of numpy use it too, allowing a = (b * c) + d to elide the temporary `b*c`. This elision can't happen with the spelling bc = b * c a = bc + d My suggestion would be to make `del x` an expression, with semantics "unbind the name `x`, and evaluate to its value". This would allow: >>> [id(del v) for v in itertools.combinations([1, 2, 3], 1)] [2500926200992, 2500926200992, 2500926200992] and bc = b * c a = (del bc) + d # in C++, this would be `std::move(bc) + d` ## Why a keyword Unbinding a name is not something that a user would expect a function to do. Functions operate on values, not names, so `move(bc)` would be surprising. ## Why `del` `del` already has "unbind this name" semantics, and allow it to be used as an expression would break no existing code. `move x` might be more understandable, but adding new keywords is expensive ## Optional extension For consistency, `x = (del foo.attr)` and `x = (del foo[i])` could also become legal expressions, and `__delete__`, `__delattr__`, and `__delitem__` would now have return values. Existing types would be free to continue to return `None`.

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PYTHONLOGGING env variable
by Bar Harel 06 Jun '20

06 Jun '20

Another idea I've had that may be of use: PYTHONLOGGING environment variable. Setting PYTHONLOGGING to any log level or level name will initialize logging.basicConfig() with that appropriate level. Another option would be that -x dev or a different -x logging will initialize basic config. Will be useful mostly for debugging purposes instead of temporarily modifying the code. Kinda surprised it doesn't exist tbh. Bar Harel

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Explicitly defining a string buffer object (aka StringIO += operator)
by Paul Sokolovsky 12 Apr '20

12 Apr '20

Hello, 1. Intro -------- It is a well-known anti-pattern to use a string as a string buffer, to construct a long (perhaps very long) string piece-wise. A running example is: buf = "" for i in range(50000): buf += "foo" print(buf) An alternative is to use a buffer-like object explicitly designed for incremental updates, which for Python is io.StringIO: buf = io.StringIO() for i in range(50000): buf.write("foo") print(buf.getvalue()) As can be seen, this requires changing the way buffer is constructed (usually in one place), the way buffer value is taken (usually in one place), but more importantly, it requires changing each line which adds content to a buffer, and there can be many of those for more complex algorithms, leading to a code less clear than the original code, requiring noise-like changes, and complicating updates to 3rd-party code which needs optimization. To address this, this RFC proposes to add an __iadd__ method (i.e. implementing "+=" operator) to io.StringIO and io.BytesIO objects, making it the exact alias of .write() method. This will allow for the code very parallel to the original str-using code: buf = io.StringIO() for i in range(50000): buf += "foo" print(buf.getvalue()) This will still require updates for buffer construction/getting value, but that's usually 2 lines. But it will leave the rest of code intact, and not obfuscate the original content construction algorithm. 2. Performance Discussion ------------------------- The motivation for this change (of promoting usage of io.StringIO, by making it look&feel more like str) is performance. But is it really a problem? Turns out, it is such a pervasive anti-pattern, that recent versions on CPython3 have a special optimization for it. Let's use following script for testing: --------- import timeit import io def string(): sb = u"" for i in range(50000): sb += u"a" def strio(): sb = io.StringIO() for i in range(50000): sb.write(u"a") print(timeit.timeit(string, number=10)) print(timeit.timeit(strio, number=10)) --------- With CPython3.6 the result is: $ python3.6 str_iadd-vs-StringIO_write.py 0.03350826998939738 0.033480543992482126 In other words, there's no difference between usage of str vs StringIO. But it wasn't always like that, with CPython2.7.17: $ python2.7 str_iadd-vs-StringIO_write.py 2.10510993004 0.0399420261383 But Python2 is dead, right? Ok, let's see how Jython3 and IronPython3 fair. To my surprise, there're no (public releases of) such. Both projects sit firmly in the Python2 territory. So, let's try them: $ java -jar jython-standalone-2.7.2.jar str_iadd-vs-StringIO_write.py 10.8869998455 1.74700021744 Sadly, I wasn't able to get to run IronPython.2.7.9.zip on my Linux system, so I used the online version at https://tio.run/#python2-iron (after discovering that https://ironpython.net/try/ is dead). 2.7.9 (IronPython 2.7.9 (2.7.9.0) on Mono 4.0.30319.42000 (64-bit)) 26.2704391479 1.55628967285 So, it seems that rumors of Python2 being dead are somewhat exaggerated. Let's try a project which tries to provide "missing migration path" between Python2 and Python3 - https://github.com/naftaliharris/tauthon Tauthon 2.8.1+ (heads/master:7da5b76f5b, Mar 29 2020, 18:05:05) $ tauthon str_iadd-vs-StringIO_write.py 0.792158126831 0.0467159748077 Whoa, tauthon seems to be faithful to its promise of being half-way between CPython2 and CPython2. Anyway, let's get back to Python3. Fortunately, there's PyPy3, so let's try that: $ ./pypy3.6-v7.3.0-linux64/bin/pypy3 str_iadd-vs-StringIO_write.py 0.5423258490045555 0.01754526497097686 Let's not forget little Python brothers and continue with MicroPython 1.12 (https://github.com/micropython/micropython): $ micropython str_iadd-vs-StringIO_write.py 41.63419413566589 0.08073711395263672 Pycopy 3.0.6 (https://github.com/pfalcon/pycopy): $ pycopy str_iadd-vs-StringIO_write.py 25.03198313713074 0.0713810920715332 I also wanted to include TinyPy (http://tinypy.org/) and Snek (https://github.com/keith-packard/snek) in the shootout, but both (seem to) lack StringIO object. These results can be summarized as follows: of more than half-dozen Python implementations, CPython3 is the only implementation which optimizes for the dubious usage of an immutable string type as an accumulating character buffer. For all other implementations, unintended usage of str incurs overhead of about one order of magnitude, 2 order of magnitude for implementations optimized for particular usecases (this includes PyPy optimized for speed vs MicroPython/Pycopy optimized for small code size and memory usage). Consequently, other implementations have 2 choices: 1. Succumb to applying the same mis-optimization for string type as CPython3. (With the understanding that for speed-optimized projects, implementing mis-optimizations will eat into performance budget, and for memory-optimized projects, it likely will lead to noticeable memory bloat.) 2. Struggle against inefficient-by-concept usage, and promote usage of the correct object types for incremental construction of string content. This would require improving ergonomics of existing string buffer object, to make its usage less painful for both writing new code and refactoring existing. As you may imagine, the purpose of this RFC is to raise awareness and try to make headway with the choice 2. 3. Scope Creep, aka "Possible Future Work" ------------------------------------------ The purpose of this RFC is specifically to propose to apply *single* simple, obvious change. .__iadd__ is just an alias for .write, period. However, for completeness, it makes sense to consider both alternatives and where the path of adding "str-like functionality" may lead us. 1. One alternative to patching StringIO would be to introduce a completely different type, e.g. StringBuf. But that largely would be "creating more entities without necessity", given that StringIO already offers needed buffering functionality, and just needs a little touch of polish with interface. If 2 classes like StringIO and StringBuf existed, it would be extra quiz to explain difference between them and why they both exist. 2. On the other hand, this RFC fixates on the output buffering. But just image how much fun can be done re: input buffers! E.g., we can define "buf[0]" to have semantics of "tmp = buf.tell(); res = buf.read(1); buf.seek(tmp); return res". Ditto for .startswith(), etc., etc. So, well... This RFC is about making .__iadd__ to be an alias for .write, and cover with this output buffering usecase. Whoever may have interest in dealing with input buffer shortcuts would need to provide a separate RFC, with separate usecases and argumentation. 4. (Self-)Criticism and Risks. ------------------------------ 1. The biggest "criticism" I see is a response a-la "there's no problem with CPython3, so there's nothing to fix". This is related to a bigger questions "whether a life outside CPython exists", or put more formally, where's the border between Python-the-language and CPython-the-implementation. To address this point, I tried to collect performance stats for a pretty wide array of Python implementations. 2. Another potential criticism is that this may open a scope creep to add more str-like functionality to classes which classically expose a stream interface. Paragraph 3.2 is dedicated specifically to address this point, by invoking hopefully the best-practice approach: request to focus on the currently proposed feature, which requires very little changes for arguably noticeable improvements. (At the same time, for an abstract possibility of this change to be found positive, this may influence further proposals from interested parties). 3. Implementing the required functionality is pretty easy with a user subclass: class MyStringBuf(io.StringIO): def __iadd__(self, s): self.write(s) return self Voila. The problem is performance. Calling such .__iadd__() method implementing in Python is 3 times slower than calling .write() directly (with CPython3.6). But paradigmatic problem is even bigger: this RFC seeks to establish the best practice of using explicitly designed for the purpose type with ergonomic interface. Saying that "we lack such clearly designated type out of the box, but if you figure out that it's a problem (if you do figure that out), you can easily resolve that on your side, albeit with a performance hit when compared with it being provided out of the box" - that's not really welcoming or ergonomic. 5. Prior Art ------------ As many things related to Python, the idea is not new. I found thread from 2006 dedicated to it: https://mail.python.org/pipermail/python-list/2006-January/403480.html (strangely, there's some mixup in archives, and "thread view" shows another message as thread starter, though it's not: https://mail.python.org/pipermail/python-list/2006-January/357453.html) . The discussion there seemed to be without clear resolution and has been swamped into discussion of unicode handling complexities in Python2 and implementation details of "StringIO" vs "cStringIO" modules (both of which were deprecated in favor of "io"). -- Best regards, Paul mailto:pmiscml@gmail.com

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SerialExecutor for concurrent.futures + Convenience constructor
by Jonathan Crall 10 Apr '20

10 Apr '20

I'd like to propose an improvement to `concurrent.futures`. The library's ThreadPoolExecutor and ProcessPoolExecutor are excellent tools, but there is currently no mechanism for configuring which type of executor you want. Also, there is no duck-typed class that behaves like an executor, but does its processing in serial. Often times a develop will want to run a task in parallel, but depending on the environment they may want to disable threading or process execution. To address this I use a utility called a `SerialExecutor` which shares an API with ThreadPoolExecutor/ProcessPoolExecutor but executes processes sequentially in the same python thread: ```python import concurrent.futures class SerialFuture( concurrent.futures.Future): """ Non-threading / multiprocessing version of future for drop in compatibility with concurrent.futures. """ def __init__(self, func, *args, **kw): super(SerialFuture, self).__init__() self.func = func self.args = args self.kw = kw # self._condition = FakeCondition() self._run_count = 0 # fake being finished to cause __get_result to be called self._state = concurrent.futures._base.FINISHED def _run(self): result = self.func(*self.args, **self.kw) self.set_result(result) self._run_count += 1 def set_result(self, result): """ Overrides the implementation to revert to pre python3.8 behavior """ with self._condition: self._result = result self._state = concurrent.futures._base.FINISHED for waiter in self._waiters: waiter.add_result(self) self._condition.notify_all() self._invoke_callbacks() def _Future__get_result(self): # overrides private __getresult method if not self._run_count: self._run() return self._result class SerialExecutor(object): """ Implements the concurrent.futures API around a single-threaded backend Example: >>> with SerialExecutor() as executor: >>> futures = [] >>> for i in range(100): >>> f = executor.submit(lambda x: x + 1, i) >>> futures.append(f) >>> for f in concurrent.futures.as_completed(futures): >>> assert f.result() > 0 >>> for i, f in enumerate(futures): >>> assert i + 1 == f.result() """ def __enter__(self): return self def __exit__(self, ex_type, ex_value, tb): pass def submit(self, func, *args, **kw): return SerialFuture(func, *args, **kw) def shutdown(self): pass ``` In order to make it easy to choose the type of parallel (or serial) backend with minimal code changes I use the following "Executor" wrapper class (although if this was integrated into concurrent.futures the name would need to change to something better): ```python class Executor(object): """ Wrapper around a specific executor. Abstracts Serial, Thread, and Process Executor via arguments. Args: mode (str, default='thread'): either thread, serial, or process max_workers (int, default=0): number of workers. If 0, serial is forced. """ def __init__(self, mode='thread', max_workers=0): from concurrent import futures if mode == 'serial' or max_workers == 0: backend = SerialExecutor() elif mode == 'thread': backend = futures.ThreadPoolExecutor(max_workers=max_workers) elif mode == 'process': backend = futures.ProcessPoolExecutor(max_workers=max_workers) else: raise KeyError(mode) self.backend = backend def __enter__(self): return self.backend.__enter__() def __exit__(self, ex_type, ex_value, tb): return self.backend.__exit__(ex_type, ex_value, tb) def submit(self, func, *args, **kw): return self.backend.submit(func, *args, **kw) def shutdown(self): return self.backend.shutdown() ``` So in summary, I'm proposing to add a SerialExecutor and SerialFuture class as an alternative to the ThreadPool / ProcessPool executors, and I'm also advocating for some sort of "ParamatrizedExecutor", where the user can construct it in "thread", "process", or "serial" model. -- -Jon

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Python-ideas March 2020