![]() ![]() A comparison of the Python 2.7 grammar and the Python 3.6 grammar shows that new parameters appear in only a few places: The corresponding new additions to the Python abstract syntax (a topic covered in more detail in another article) are actually quite few in number. More information on the built-in type specification library and the type annotation concrete syntax can be found in its documentation page. How can you organize your API to handle both of these issues in a way that keeps your API clean and allows users of your API to leverage features already built into Python? The second is more evidently difficult, and you would need to provide for the user a way to specify the input type. However, in the event that you need to determine this information in advance, you may not always be free to invoke the function ( e.g., if the function relies on some resource that is not yet available or running the function has a very high cost). The first of these cases is more straightforward to handle at runtime: inspect the type of the output and branch accordingly. your code needs to generate inputs to user-supplied functions that have the type the user-supplied function expects.user-supplied functions can return results of different types and you do not know in advance what the result type of any given user-supplied function will be, or.In many such use cases, you might find yourself dealing with two related scenarios: Perhaps you are allowing users to specify their own hooks or event handlers within a web application framework, or you are creating a unit testing library that generates test cases for a user-supplied function. Tuples of three floats and a bool.Suppose you are working on a project in Python that takes advantage of Python's support for higher-order functions. Tuple] might mean a dict mapping strings to list might mean a list of integers, and dict[str, I've toyed at various times with specific ways of combining The main goal was always to arrive at a notation to specify typeĬonstraints (and maybe other constraints) for arguments and return Pretty much all of the above to some extent. Like a command line parser, or runtime static checker? Was it designed to beĪn aid to IDEs, or static analysis tools that inspect source code? Somethingįor applications themselves to munge through to provide special behaviors, Some suggestions, but doesn't leave anything concrete. So, if I may ask, what was the original goal of annotations? The PEP gives The idea was also thatĮventually, from all those experiments, one would emerge that would be Syntax was settled people would be free to experiment with different Important or how to combine them, so we decided to take a two-stepĪpproach: in step 1, we designed the syntax, whereas in step 2, we Important, and we couldn't figure out which ones would be the most There were too many use cases that immediately looked Some goal in mind, and where the community was supposed to discover the goal It seemed like a feature that was designed and implemented without I've looked at annotations before, but I never understood the Here's a bit from one of Guido van Rossum's posts in that thread: The first post is from Thomas Kluyver on Dec 1.) The thread in question is near the beginning of December, and titled " Conventions for function annotations". (The link provided is just for the monthly archive I find that the URL for specific posts tends to change periodically. There is actually a recent thread in the python-ideas mailing list on the topic which may be helpful. Why are annotations better than decorators when it comes to adding metadata to functions?Īs you mentioned, the relevant PEP is 3107 (linked for easy reference in case others encountering this question haven't read it yet).įor now, annotations are kind of an experiment, and kind of a work in progress. Īt least my initial impression is that annotations are superfluous: decorators can do everything that annotations can (and more). Speed_mph="How fast to move the load (in miles per hour)."). Names, args_name, kwargs_name, defaults = inspect.getfuncspec(f)į.parameter_metadata = dict(zip(names, defaults)) You can add metadata to arguments too, and it can look pretty if you take advantage of default arguments, like so: import inspect def returns(return_type):į.return_type = return_type # <- adding metadata here It seems to me that you can add metadata to functions using decorators. I have gone through the first couple of sections in PEP 3107, but I still don't get what good they do for the language.
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