Python-ideas November 2020

python-ideas@python.org

85 participants
49 discussions

Make fnmatch.filter accept a tuple of patterns

by Andre Delfino

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?

2 months, 3 weeks

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

3 months

PEP 9999 (provisional): support for indexing with keyword arguments

by Stefano Borini

PEP for support for indexing with keyword arguments is now submitted as PR. https://github.com/python/peps/pull/1612 Thanks to everybody involved in the development of the PEP and the interesting discussion. All your contributions have been received and often added to the document. If the PEP is approved, I would like to attempt an implementation, but I am not particularly skilled in the python internals. If a core developer is willing to teach me the ropes (especially in the new parser, I barely understood the syntax of the old one, but have no idea about the new one) and review my code I can give it a try. I would not mind refreshing my C a bit. -- Kind regards, Stefano Borini

3 months, 1 week

A shortcut to load a JSON file into a dict : json.loadf

by The Nomadic Coder

Hi All, This is the first time I'm posting to this mailing group, so forgive me if I'm making any mistakes. So one of the most common ways to load json, is via a file. This is used extensively in data science and the lines. We often write something like :- with open(filename.json, "r") as f: my_dict = json.load(f) or my_dict = json.load(open("filename.json", "r")) Since this is sooooo common, why doesn't python have something like :- json.loadf("filename.json") Is there an obvious issue by defining this in the cpython? I don't whipping up a PR if it gains traction.

3 months, 1 week

Add extend_const action to argparse

by python＠roganartu.com

Forgive me if this idea has been discussed before, I searched the mailing lists, the CPython repo, and the issue tracker and was unable to find anything. I have found myself a few times in a position where I have a repeated argument that uses the `append` action, along with some convenience arguments that append a specific const to that same dest (eg: `--filter-x` being made equivalent to `--filter x` via `append_const`). This is particularly useful in cli apps that expose some kind of powerful-but-verbose filtering capability, while also providing shorter aliases for common invocations. I'm sure there are other use cases, but this is the one I'm most familiar with. The natural extension to this filtering idea are convenience args that set two const values (eg: `--filter x --filter y` being equivalent to `--filter-x-y`), but there is no `extend_const` action to enable this. While this is possible (and rather straight forward) to add via a custom action, I feel like this should be a built-in action instead. `append` has `append_const`, it seems intuitive and reasonable to expect `extend` to have `extend_const` too (my anecdotal experience the first time I came across this need was that I simply tried using `extend_const` without checking the docs, assuming it already existed). Please see this gist for a working example that may help explain the idea and intended use case more clearly: https://gist.github.com/roganartu/7c2ec129d868ecda95acfbd655ef0ab2

4 months, 1 week

Re: [Python-Dev] reversed enumerate

by Steven D'Aprano

Hi Ilya, I'm not sure that this mailing list (Python-Dev) is the right place for this discussion, I think that Python-Ideas (CCed) is the correct place. For the benefit of Python-Ideas, I have left your entire post below, to establish context. [Ilya] > I needed reversed(enumerate(x: list)) in my code, and have discovered > that it wound't work. This is disappointing because operation is well > defined. It isn't really well-defined, since enumerate can operate on infinite iterators, and you cannot reverse an infinite stream. Consider: def values(): while True: yield random.random() a, b = reversed(enumerate(values()) What should the first pair of (a, b) be? However, having said that, I think that your idea is not unreasonable. `enumerate(it)` in the most general case isn't reversable, but if `it` is reversable and sized, there's no reason why `enumerate(it)` shouldn't be too. My personal opinion is that this is a fairly obvious and straightforward enhancement, one which (hopefully!) shouldn't require much, if any, debate. I don't think we need a new class for this, I think enhancing enumerate to be reversable if its underlying iterator is reversable makes good sense. But if you can show some concrete use-cases, especially one or two from the standard library, that would help your case. Or some other languages which offer this functionality as standard. On the other hand, I think that there is a fairly lightweight work around. Define a helper function: def countdown(n): while True: yield n n -= 1 then call it like this: # reversed(enumerate(seq)) zip(countdown(len(seq)-1), reversed(seq))) So it isn't terribly hard to work around this. But I agree that it would be nice if enumerate encapsulated this for the caller. One potentially serious question: what should `enumerate.__reversed__` do when given a starting value? reversed(enumerate('abc', 1)) Should that yield...? # treat the start value as a start value (1, 'c'), (0, 'b'), (-1, 'a') # treat the start value as an end value (3, 'c'), (2, 'b'), (1, 'a') Something else? My preference would be to treat the starting value as an ending value. Steven On Wed, Apr 01, 2020 at 08:45:34PM +0200, Ilya Kamenshchikov wrote: > Hi, > > I needed reversed(enumerate(x: list)) in my code, and have discovered that > it wound't work. This is disappointing because operation is well defined. > It is also well defined for str type, range, and - in principle, but not > yet in practice - on dictionary iterators - keys(), values(), items() as > dictionaries are ordered now. > It would also be well defined on any user type implementing __iter__, > __len__, __reversed__ - think numpy arrays, some pandas dataframes, tensors. > > That's plenty of usecases, therefore I guess it would be quite useful to > avoid hacky / inefficient solutions like described here: > https://code.activestate.com/lists/python-list/706205/. > > If deemed useful, I would be interested in implementing this, maybe > together with __reversed__ on dict keys, values, items. > > Best Regards, > -- > Ilya Kamen > > ----------- > p.s. > > *Sketch* of what I am proposing: > > class reversible_enumerate: > > def __init__(self, iterable): > self.iterable = iterable > self.ctr = 0 > > def __iter__(self): > for e in self.iterable: > yield self.ctr, e > self.ctr += 1 > > def __reversed__(self): > try: > ri = reversed(self.iterable) > except Exception as e: > raise Exception( > "enumerate can only be reversed if iterable to > enumerate can be reversed and has defined length." > ) from e > > try: > l = len(self.iterable) > except Exception as e: > raise Exception( > "enumerate can only be reversed if iterable to > enumerate can be reversed and has defined length." > ) from e > > indexes = range(l-1, -1, -1) > for i, e in zip(indexes, ri): > yield i, e > > for i, c in reversed(reversible_enumerate("Hello World")): > print(i, c) > > for i, c in reversed(reversible_enumerate([11, 22, 33])): > > print(i, c) > _______________________________________________ > Python-Dev mailing list -- python-dev(a)python.org > To unsubscribe send an email to python-dev-leave(a)python.org > https://mail.python.org/mailman3/lists/python-dev.python.org/ > Message archived at https://mail.python.org/archives/list/python-dev@python.org/message/NDDKDUD… > Code of Conduct: http://python.org/psf/codeofconduct/

4 months, 3 weeks

pathlib enhancements

by Todd

I know enhancements to pathlib gets brought up occasionally, but it doesn't look like anyone has been willing to take the initiative and see things through to completion. I am willing to keep the ball rolling here and even implement these myself. I have some suggestions and I would like to discuss them. I don't think any of them are significant enough to require a pep. These can be split it into independent threads if anyone prefers. 1. copy The big one people keep bringing up that I strongly agree on is a "copy" method. This is really the only common file manipulation task that currently isn't possible. You can make files, read them, move them, delete them, create directories, even do less common operations like change owners or create symlinks or hard links. A common objection is that pathlib doesn't work on multiple paths. But that isn't the case. There are a ton of methods that do that, including: * symlink_to * link_to * rename * replace * glob * rglob * iterdir * is_relative_to * relative_to * samefile I think this is really the only common file operation that someone would need to switch to a different module to do, and it seems pretty strange to me to be able to make symbolic or hard links to a file but not straight up copy one. 2. recursive remove This could be a "recursive" option to "rmdir" or a "rmtree" method (I prefer the option). The main reason for this is symmetry. It is possible to create a tree of folders (using "mkdir(parents=True)"), but once you do that you cannot remove it again in a straightforward way. 3. newLine for write_text This is the only relevant option that "Path.open" has but "Path.write_text" doesn't, and is a serious omission when dealing with multiple operating systems. 4. uid and gid You can get the owner and group name of a file (with the "owner" and "group" methods), but there is no easy way to get the corresponding number. 5. Stem with no suffixes The stem property only takes off the last suffix, but even in the example given ('my/library.tar.gz') it isn't really useful because the suffix has two parts ('.tar' and '.gz'). I suggest another property, probably called "rootstem" or "basestem", that takes off all the suffixes, using the same logic as the "suffixes" property. This is another symmetry issue: it is possible to extract all the suffixes, but not remove them. 6. with_suffixes Equivalent to with_suffix, but replacing all suffixes. Again, this is a symmetry issue. It is hard to manipulate all the suffixes right now, as the example show. You can add them or extract them, but not change them without doing several steps. 7. exist_ok for is_* methods Currently all the is_* methods (such as is_file) return False if the file doesn't exist or if it is a broken symlink. This can be dangerous, since it is not trivially easy to tell if you are dealing with the wrong type of file vs. a missing file. And it isn't obvious behavior just from the method name. I suggest adding an "exist_ok" argument to all of these, with the default being "True" for backwards-compatibility. This argument name is already in use elsewhere in pathlib. If this is False and the file is not present, a "FileNotFoundError" is raised.

4 months, 4 weeks

Add command-line option to unittest for enabling post-mortem debugging

by Dominik Vilsmeier

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.

5 months, 1 week

Python JIT Compilation Thoughts

by redradist＠gmail.com

Hi all, I do not know maybe it was already discussed ... but the toolchain like LLVM is very mature and it can provide the simpler JIT compilation to machine code functionality and it will improve performance of the Python a lot !!

5 months, 1 week

Add decorator_with_params function to functools module

by Yurii Karabas

In the current python codebases, there are a lot of decorators with parameters, and their code in most cases contains a lot of code boilerplates. The purpose of this feature is to add `decorator_with_params` (I think the name can be changed to smth better) function to `functools` module. This function will allow using a decorated function as a simple decorator and decorator with parameter. I believe the real example will bring more context, for instance, `typing._tp_cache` function can be rewritten from: ``` def _tp_cache(func=None, /, *, typed=False): """Internal wrapper caching __getitem__ of generic types with a fallback to original function for non-hashable arguments. """ def decorator(func): cached = functools.lru_cache(typed=typed)(func) _cleanups.append(cached.cache_clear) @functools.wraps(func) def inner(*args, **kwds): try: return cached(*args, **kwds) except TypeError: pass return func(*args, **kwds) return inner if func is not None: return decorator(func) return decorator ``` To ``` @decorator_with_params def _tp_cache(func, /, *, typed=False): """Internal wrapper caching __getitem__ of generic types with a fallback to original function for non-hashable arguments. """ cached = functools.lru_cache(typed=typed)(func) _cleanups.append(cached.cache_clear) @functools.wraps(func) def inner(*args, **kwds): try: return cached(*args, **kwds) except TypeError: pass # All real errors (not unhashable args) are raised below. return func(*args, **kwds) return inner ``` And `_tp_cache` can be used as: ``` @_tp_cache(typed=True) def __getitem__(self, parameters): return self._getitem(self, parameters) ... @_tp_cache def __getitem__(self, parameters): return self._getitem(self, parameters) ``` Proposed implementation is quite simple (I used it in a lot of projects): ``` def decorator_with_params(deco): @wraps(deco) def decorator(func=None, /, *args, **kwargs): if func is not None: return deco(func, *args, **kwargs) def wrapper(f): return deco(f, *args, **kwargs) return wrapper return decorator ``` I believe it will be a great enhancement for the Python standard library and will save such important indentations. I have already opened an issue https://bugs.python.org/issue42455 (Sorry about that I didn't know that smth like python-ideas exists).

5 months, 3 weeks

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Python-ideas November 2020