Suddenly some expressions that looked just fine and logical need restructuring and changes. Once you decide to use something like mypy, all hell gates are open. I should be honest to say that these changes are needed if you want a static type checker like mypy(which is the only one I've used so far) against your code, which seems the rational and right thing to do after you decide to add type hints(otherwise they'll turn out over time to be just another type of comments).
#Python function annotations code
Luckily this doesn't happen with user-defined types as they're supported to be used for both purposes.Īnd then consider the code changes. So you end up using two families of type names, one for constructing objects and another for annotating. Even the syntax for specifying a generic class with a type parameter is inconvenient, relying on the indexing operator instead of some other clearer one. You can't use the original types because they don't support the generic class syntax like list you have to instead rely on the equivalent ones from the typing module like List. Just consider the different types in the typing module the developer has to learn about to annotate variables instantiated from other popular types(built-in ones or otherwise) like list, tuple, re.Match. It looks like a completely new language was suddenly embedded inside your python code, and makes the developer to write what seems like two languages interspersed on the same line.įirst it has a steep learning curve(, and yes I mean the learning curve not the effort to apply the rules to an existing codebase, which is another story). While the idea seems promising at the first glance, I think the implementation could be much better, to say the least.Īdding type hints to python was performed in a way that got in the way of developers writing code instead of helping them to do so.
I got to learn about type hinting in python a couple months ago( I knew it existed long before that but didn't bother to learn about it until recently). I'm going to write my honest opinion about this. When annotating variables, it can be defined in the form With dynamically typed languages, its basically anyone's guess as to what the value of a variable is or should be. For instance, we know string variables can only be strings, ints can only be ints, and so on. This is accomplished by adding a given type declaration after initializing/declaring a variable or method.Ī helpful feature of statically typed languages is that the value of a variable can always be known within a specific domain. This hinting brings a sense of statically typed control to the dynamically typed Python. They are used to inform someone reading the code what the type of a variable should be expected. Type Annotating is a new feature added in PEP 484 that allows adding type hints to variables. With this said, I also enjoy the readability of statically typed languages for other programmers to know what type a specific variable should be! So, to get the best of both worlds, Python 3.5 introduced type annotations. This conversion works, but I really like the flexibility of Python, and I don't want to sacrifice its positive attributes as a dynamic, readable, and beginner-friendly language just because types are difficult to reason about in most cases. """Docstring comes after type comment.Enter fullscreen mode Exit fullscreen mode.When using multi-line comments, you do not need to prefix thetypes of your arg and **kwarg parameters with or **.For example, here is how you would annotate the first example usingmulti-line comments: Note that the return type annotation contains literalthree dots. You provide a return type annotation in the body of thefunction using the form # type: (…) -> rt, where rt is thereturn type. Ifyou leave out an annotation for an argument, it defaults to Any. Each annotation must be on a separate line. You write a separate annotation for each function argument on the sameline as the argument.
def send_email ( address, # type: Union].The alternative, multi-lineannotation syntax makes long annotations easier to read and write. When using the single-line annotation syntaxdescribed above, functions with long argument lists tend to result inoverly long type comments and it’s often tricky to see which argumenttype corresponds to which argument. Youcan provide a separate annotation for each argument using the variableannotation syntax. Mypy also supports a multi-line comment annotation syntax. Multi-line Python 2 function annotations.
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