Mailman 3 February 2021 - Python-ideas

zip(x, y, z, strict=True)
by Ram Rachum Dec. 2, 2022

Dec. 2, 2022

Here's something that would have saved me some debugging yesterday: >>> zipped = zip(x, y, z, strict=True) I suggest that `strict=True` would ensure that all the iterables have been exhausted, raising an exception otherwise. This is useful in cases where you're assuming that the iterables all have the same lengths. When your assumption is wrong, you currently just get a shorter result, and it could take you a while to figure out why it's happening. What do you think?

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Bringing the print statement back
by Guido van Rossum March 2, 2022

March 2, 2022

In Python 3.10 we will no longer be burdened by the old parser (though 3rd party tooling needs to catch up). One thing that the PEG parser makes possible in about 20 lines of code is something not entirely different from the old print statement. I have a prototype: Python 3.10.0a0 (heads/print-statement-dirty:5ed19fcc1a, Jun 9 2020, 16:31:17) [Clang 11.0.0 (clang-1100.0.33.8)] on darwin Type "help", "copyright", "credits" or "license" for more information. Cannot read termcap database; using dumb terminal settings. >>> print 2+2 4 >>> print "hello world" hello world >>> print "hello", input("Name:") Name:Guido hello Guido >>> print 1, 2, 3, sep=", " 1, 2, 3 >>> But wait, there's more! The same syntax will make it possible to call *any* function: >>> len "abc" 3 >>> Or any method: >>> import sys >>> sys.getrefcount "abc" 24 >>> Really, *any* method: >>> class C: ... def foo(self, arg): print arg ... >>> C().foo 2+2 4 >>> There are downsides too, though. For example, you can't call a method without arguments: >>> print <built-in function print> >>> Worse, the first argument cannot start with a parenthesis or bracket: >>> print (1, 2, 3) 1 2 3 >>> C().foo (1, 2, 3) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: C.foo() takes 2 positional arguments but 4 were given >>> print (2+2), 42 4 (None, 42) >>> C().foo [0] Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'method' object is not subscriptable >>> No, it's not April 1st. I am seriously proposing this (but I'll withdraw it if the response is a resounding "boo, hiss"). After all, we currently have a bunch of complexity in the parser just to give a helpful error message to people used to Python 2's print statement: >>> print 1, 2, 3 File "<stdin>", line 1 print 1, 2, 3 ^ SyntaxError: Missing parentheses in call to 'print'. Did you mean print(1, 2, 3)? >>> And IIRC there have been a number of aborted attempts at syntactic hacks to allow people to call functions (like print) without parentheses, although (I think) none of them made it into a PEP. The PEG parser makes this much simpler, because it can simply backtrack -- by placing the grammar rule for this syntax (tentatively called "call statement") last in the list of alternatives for "small statement" we ensure that everything that's a valid expression statement (including print() calls) is still an expression statement with exactly the same meaning, while still allowing parameter-less function calls, without lexical hacks. (There is no code in my prototype that checks for a space after 'print' -- it just checks that there's a name, number or string following a name, which is never legal syntax.) One possible extension I didn't pursue (yet -- dare me!) is to allow parameter-less calls inside other expressions. For example, my prototype does not support things like this: >>> a = (len "abc") File "<stdin>", line 1 a = (len "abc") ^ SyntaxError: invalid syntax >>> I think that strikes a reasonable balance between usability and reduced detection of common errors. I could also dial it back a bit, e.g. maybe it's too much to allow 'C().foo x' and we should only allow dotted names (sufficient to access functions in imported modules and method calls on variables). Or maybe we should only allow simple names (allowing 'len x' but disallowing 'sys.getrefcount x'. Or maybe we should really only bring back print statements. I believe there are some other languages that support a similar grammar (Ruby? R? Raku?) but I haven't investigated. Thoughts? -- --Guido van Rossum (python.org/~guido) *Pronouns: he/him **(why is my pronoun here?)* <http://feministing.com/2015/02/03/how-using-they-as-a-singular-pronoun-can-…>

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Adding a thin wrapper class around the functions in stdlib.heapq
by bunslow Feb. 18, 2022

Feb. 18, 2022

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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Allow syntax "func(arg=x if condition)"
by Peter O'Connor Jan. 19, 2022

Jan. 19, 2022

I often find that python lacks a nice way to say "only pass an argument under this condition". (See previous python-list email in "Idea: Deferred Default Arguments?") Example 1: Defining a list with conditional elements include_bd = True current_way = ['a'] + (['b'] if include_bd else [])+['c']+(['d'] if include_bd else []) new_way = ['a', 'b' if include_bd, 'c', 'd' if include_bd] also_new_way = list('a', 'b' if include_bd, 'c', 'd' if include_bd) Example 2: Deferring to defaults of called functions def is_close(a, b, precicion=1e-9): return abs(a-b) < precision def approach(pose, target, step=0.1, precision=None): # Defers to default precision if not otherwise specified: velocity = step*(target-pose) \ if not is_close(pose, target, precision if precision is not None) \ else 0 return velocity Not sure if this has been discussed, but I cannot see any clear downside to adding this, and it has some clear benefits (duplicated default arguments and **kwargs are the scourge of many real world code-bases)

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Barrier Object in asyncio lib
by Yves Duprat May 11, 2021

May 11, 2021

Hi,the list, I'm wondering why Barrier object does not exist in the synchronization primitives of the asyncio lib while it is present in threading and multiprocessing libs ? This may not be the right place to ask this question, but I never found an answer on the web. Thanks for your help. Yves

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Top Level Await in Python like in Deno
by redradist＠gmail.com April 16, 2021

April 16, 2021

Hi all, Deno is JavaScript runtime that has very nice feature like Top Level Await, I think it would be also nice to have such feature in Python, it will make using async/await more convenient What do you think ? Share your ideas lets discuss ...

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Access to source code when using exec()
by Alex Hall April 3, 2021

April 3, 2021

When using `exec(string)`, the standard Python REPL, or `python -c` in a terminal, the executed source code is not stored anywhere and cannot be retrieved (except in the `-c` case by looking at `sys.argv`). This means that: - The source line doesn't show up in tracebacks, e.g: >>> exec("1/0") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<string>", line 1, in <module> ZeroDivisionError: division by zero - Users cannot retrieve the source code using `inspect`, although many want to: - https://stackoverflow.com/questions/29850508/python-how-to-inspect-code-of-… - https://stackoverflow.com/questions/12072252/cant-get-source-code-for-a-met… - https://stackoverflow.com/questions/62160529/how-to-get-source-inside-exec-… - https://stackoverflow.com/questions/59244323/how-can-you-get-the-source-cod… - https://stackoverflow.com/questions/50822656/source-code-for-an-evaled-dyna… - https://stackoverflow.com/questions/50515651/why-does-the-python-linecache-… - Libraries such as traceback formatters and debuggers which depend on source code can't work. This often manifests when people try out a library for the first time in the REPL and it doesn't seem to work. This leads to many similar looking bug reports which sometimes take a while to resolve. Probably many more people think these libraries don't work without ever opening an issue. - https://github.com/cknd/stackprinter/issues/34 - https://github.com/Qix-/better-exceptions/issues/17 - https://github.com/Qix-/better-exceptions/issues/68 - https://github.com/aroberge/friendly-traceback/issues/23 - https://github.com/gruns/icecream/issues/17 - https://github.com/cool-RR/PySnooper/issues/135 - https://github.com/cool-RR/PySnooper/issues/164 - https://github.com/alexmojaki/sorcery/issues/3 - https://github.com/alexmojaki/sorcery/issues/5 - https://github.com/alexmojaki/sorcery/issues/6 I suggest that: 1. `exec(string)` assigns a unique generated filename to the compiled code object and stores the source code in the `linecache` module under that filename, similar to what the IPython REPL does. I imagine there might be complaints about memory usage, security implications of revealing source code, compatibility, etc. If that's an issue this could be off by default with a flag to turn it on. 2. The REPL, `-c`, and other similar mechanisms (IDLE?) either follow the same strategy as `exec` or defer to it. 3. The standard exception hook falls back to `linecache` for filenames that don't exist. I don't know why it doesn't already. Here is a question on that topic: https://stackoverflow.com/questions/50515651/why-does-the-python-linecache-…

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Make fnmatch.filter accept a tuple of patterns
by Andre Delfino March 26, 2021

March 26, 2021

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 March 12, 2021

March 12, 2021

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 lazy import statement to protect legitimate circular imports from failing
by Matt del Valle March 12, 2021

March 12, 2021

I have seen a lot of interest in this topic, and I think the sheer number of different lazy import implementations (generally using proxy objects for modules and then loading them when an attribute is accessed) should present pretty good evidence that this is a very desired feature, particularly for writers of library code seeking to optimize start-up times and similar. Unfortunately, all of these implementations have serious limitations in that often static code analysis tools cannot understand them, resulting in autocompletions and useful diagnostic errors being lost. Also, they are fundamentally incapable of dealing with 'from x import y' syntax, because this is *always *loaded eagerly by the Python interpreter. With the increasingly widespread adoption of type hints I think it is time to revive the proposal for lazy imports as an official language feature. It is very common to type hint the return type or argument type of a function as a type which cannot be imported at the top of the file inside the module namespace because it would trigger a circular import, and must therefore be imported inside the function. In these instances it is currently recommended to use the 'TYPE_CHECKING' value in the 'typing' module which is always 'False' at runtime, in order to be able to provide the return or argument type hint: some_module.py > from typing import TYPE_CHECKING > > if TYPE_CHECKING: > from module_that_depends_on_me import MyClass > > > def some_func() -> MyClass: > from module_that_depends_on_me import MyClass > > return MyClass() However, this is unnecessarily verbose and confusing to most users who are not familiar with this usage pattern. IDEs will also be unable to warn you that MyClass is undefined if you try to use it inside 'some_func' without importing it within 'some_func' first, because it appears to be defined even though it isn't. It also is fundamentally incapable of handling this other extremely common usage pattern: some_library/ __init__.py > __all__ = ["SomeClass", "AnotherClass"] > > from .extremely_expensive_module import SomeClass > from .another_extremely_expensive_module import AnotherClass some_module.py > from some_library import SomeClass Where a library author wants to make several classes in their library's public API available at package level in the __init__.py, but doesn't want to eagerly load all of them when many users will only ever use one in a given program. In this situation if I try to import SomeClass from some_library/__init__.py I will *always* trigger AnotherClass to be loaded as well, totally unnecessarily. My proposed syntax is pretty much the same as was suggested by Nicolas Cellier in 2017 here: https://mail.python.org/pipermail/python-ideas/2017-February/044894.html, but with the additional stipulation that 'from x import y' format imports should *also* be supported. The two new lazy import statements should therefore be: > lazy import some_module > from some_module lazy import SomeClass For the two examples given above the equivalent code should be. some_module.py > from module_that_depends_on_me lazy import MyClass # does not raise ImportError when module_that_depends_on_me tries to import me, because this import is deferred > > > def some_func() -> MyClass: > return MyClass() # MyClass is not imported until this line, in a function that isn't called until after module level imports have already been resolved some_library /__init__.py > __all__ = ["SomeClass", "AnotherClass"] > > from .extremely_expensive_module lazy import SomeClass # this import is deferred > from .another_extremely_expensive_module lazy import AnotherClass # this import is deferred some_module.py > from some_library import AnotherClass # this causes SomeClass to be imported from some_library/__init__.py which in turn causes the import statement on line 4 of some_library/__init__.py to finally happen for real. The import of AnotherClass on line 3 *still* hasn't actually happened, and may never happen if AnotherClass isn't imported (or lazy imported and then used) during the lifetime of this program. These are just some examples that I could think of off the top of my head. I am certain there are more. This would be insanely useful. I really hope this sparks some discussion :)

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