Python-ideas February 2020

python-ideas@python.org

95 participants
65 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, 1 week

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

@classproperty, @abc.abstractclasspropery, etc.

by K. Richard Pixley

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

6 months

PYTHONLOGGING env variable

by Bar Harel

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

1 year

SerialExecutor for concurrent.futures + Convenience constructor

by Jonathan Crall

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

1 year, 2 months

Add the imath module

by Serhiy Storchaka

What are your thoughts about adding a new imath module for integer mathematics? It could contain the following functions: * factorial(n) Is just moved from the math module, but non-integer types are rejected. Currently math.factorial() accepts also integer floats like 3.0. It looks to me, the rationale was that at the time when math.factorial() was added, all function in the math module worked with floats. But now we can revise this decision. * gcd(n, m) Is just moved from the math module. * as_integer_ration(x) Equivalents to: def as_integer_ration(x): if hasattr(x, 'as_integer_ration'): return x.as_integer_ration() else: return (x.numerator, x.denominator) * binom(n, k) Returns factorial(n) // (factorial(k) * factorial(n-k)), but uses more efficient algorithm. * sqrt(n) Returns the largest integer r such that r**2 <= n and (r+1)**2 > n. * isprime(n) Tests if n is a prime number. * primes() Returns an iterator of prime numbers: 2, 3, 5, 7, 11, 13,... Are there more ideas?

1 year, 2 months

New explicit methods to trim strings

by Alex Grigoryev

Following the discussion here (https://link.getmailspring.com/link/7D84D131-65B6-4EF7-9C43-51957F9DFAA9@ge…) I propose to add 3 new string methods: str.trim, str.ltrim, str.rtrim Another option would be to change API for str.split method to work correctly with sequences. In [1]: def ltrim(s, seq): ...: return s[len(seq):] if s.startswith(seq) else s ...: In [2]: def rtrim(s, seq): ...: return s[:-len(seq)] if s.endswith(seq) else s ...: In [3]: def trim(s, seq): ...: return ltrim(rtrim(s, seq), seq) ...: In [4]: s = 'mailto:maria@gmail.com' In [5]: ltrim(s, 'mailto:') Out[5]: 'maria(a)gmail.com' In [6]: rtrim(s, 'com') Out[6]: 'mailto:maria@gmail.' In [7]: trim(s, 'm') Out[7]: 'ailto:maria@gmail.co'

1 year, 3 months

SQL string prefix idea

by minecraft2048＠gmail.com

The idea is to add a new string prefix 's' for SQL string. This string doesn't do anything in Python, unlike b"" or f"" strings, but interactive Python shells like IPython or Jupyter can parse the following characters as SQL syntax instead of Python syntax and give SQL syntax highlighting and autocompletion, and if they are configured correctly, they can do column name autocompletion. Unfortunately when I try to type s"select * from table" it gave me syntax error instead, so I think this need to be implemented in Python language itself instead of module

1 year, 3 months

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

by Neil Girdhar

I didn't think of this when we were discussing 448. I ran into this today, so I agree with you that it would be nice to have this. Best, Neil On Monday, December 4, 2017 at 1:02:09 AM UTC-5, Eric Wieser wrote: > > Hi, > > I've been thinking about the * unpacking operator while writing some numpy > code. PEP 448 allows the following: > > values = 1, *some_tuple, 2 > object[(1, *some_tuple, 2)] > > It seems reasonable to me that it should be extended to allow > > item = object[1, *some_tuple, 2] > item = object[1, *some_tuple, :] > > Was this overlooked in the original proposal, or deliberately rejected? > > Eric >

1 year, 3 months

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Python-ideas February 2020