![]() Very few libraries have done so, and even some very popular ones haven't put the effort to do(like the attrs library for example). Now consider the conformance and adoption of third-party libraries and packages for type hinting. Please don't get me wrong: I do appreciate the effort that has gone over the years to bring mypy and other type checkers to how they look now, but the deficiencies and shortcomings of these tools adversely affects the full picture of type hinting. I asked on stackoverflow about how to satisfy mypy without writing a lot of boilerplate code. Sometimes the type checker is completely unable to understand what's written and seems like a very clear code snippet, like this question /questions/600296. Another example is the need to convert many lambdas passed as parameters to functions to explicit standalone functions, just because the type checker can't sanitize the lambda syntax sufficiently(especially evident with third-party libraries, which I discuss later). Take for example containers or strings that we can use as boolean expressions this no longer works because the type checker needs to see that you really mean to test their truth value(especially evident in return statements with functions returning bool). 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). 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. Enter fullscreen mode Exit fullscreen mode
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