Mailman 3 January 2021 - Python-ideas

zip(x, y, z, strict=True)
by Ram Rachum Dec. 1, 2022

Dec. 1, 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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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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PEP 9999 (provisional): support for indexing with keyword arguments
by Stefano Borini March 10, 2021

March 10, 2021

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

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A shortcut to load a JSON file into a dict : json.loadf
by The Nomadic Coder March 10, 2021

March 10, 2021

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.

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Conditional with statements
by Jonathan Crall Feb. 16, 2021

Feb. 16, 2021

I'm not sure if this has been asked / suggested before. I'm wondering if there is any interest in conditional or loop-based `with` statements. I think it could be done without a syntax change. ### Napkin proposal Context managers could define `__if__` or `__for__`, and if those dunder methods were defined, then the `with` statement would either behave like a conditional or a loop. If `__if__` was defined then ``` with Ctx(): print('hi') ``` would only print `hi` if `__if__` returned True. This doesn't require a syntax change. The `__for__` variant would likely need a minor syntax change. ``` with item in Ctx(): print(item) ``` The `__for__` method is a generator that generates arguments of a loop. The item will be printed as many times as there are items generated by `__for__`. ### Use Cases This would simplify usage of my Timerit and ubelt library. The timerit module defines ``timerit.Timerit``, which is an object that is iterable. It has an ``__iter__`` method that generates ``timerit.TimerTimer`` objects, which are context managers. >>> import math >>> from timerit import Timerit >>> for timer in Timerit(num=200, verbose=2): >>> with timer: >>> math.factorial(10000) The timer context manager measures how much time the body of it takes by "tic"-ing ``__enter__`` and "toc"-ing on ``__exit__``. The underlying object has access to the context manager, so it is able to read its measurement. These measurements are stored and then we compute some statistics on them. Notably the minimum, mean, and standard-deviation of grouped (batched) running times. Unfortunately the syntax is one line and one indent bulker than I would prefer. However, a more consice version of the synax is available. >>> import math >>> from timerit import Timerit >>> for _ in Timerit(num=200, verbose=2): >>> math.factorial(10000) In this case the measurement is made in the `__iter__` method ``Timerit`` object itself, which I believe contains slightly more overhead than the with-statement version. (I should test to determine if this is the case). In the case where it does make a difference, a cool syntax might look like: >>> import math >>> from timerit import Timerit >>> with timer in Timerit(num=200, verbose=2): >>> math.factorial(10000) The other case is that my ``ubelt.Cacher`` library. Currently it requires 4 lines of boilerplate syntax. >>> import ubelt as ub >>> # Defines a cache name and dependencies, note the use of `ub.hash_data`. >>> cacher = ub.Cacher('name', cfgstr=ub.hash_data('dependencies')) # boilerplate:1 >>> # Calling tryload will return your data on a hit and None on a miss >>> data = cacher.tryload() # boilerplate:2 >>> # Check if you need to recompute your data >>> if data is None: # boilerplate:3 >>> # Your code to recompute data goes here (this is not boilerplate). >>> data = 'mydata' >>> # Cache the computation result (pickle is used by default) >>> cacher.save(data) # boilerplate:4 But a conditional ``with`` syntax would reduce boilerplate to 3 lines. >>> import ubelt as ub >>> with ub.Cacher('name', cfgstr=ub.hash_data('dependencies')) as cacher: >>> data = 'mydata' >>> cacher.save(data) >>> data = cacher.data I'm sure there are a lot of viable syntax variations, but does the idea of a conditional or loop aware "with" statement seem like a reasonable language proposal? -- -Dr. Jon Crall (him)

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