I just recently posted a blog about Annotating functions in Typescript. I just finished doing a bit of a study and understood more on how to annotate functions in Python and this blog is going to be all about annotating Python functions with similar examples to the last blog.
You can validate your type annotations in Visual Studio Code by setting python.analysis.typeCheckingMode to one of basic, standard, strict,. basic and standard options doesn't necessarily make sure you annotate your functions and variables but strict does.
Function Values
This might shock you but you can return functions and pass functions as values in Python. Callback functions are actually annotated using the Callable type which is written like this;
Callable[[argtype1, argtype2, argtype3], returnType]
For instance, a function length(text: str) -> int will be annotated as Callable[[str], int]
For example;
This function in JavaScript
function multiplier(factor){
return value => factor * value
}
const n = multiplier(6)
n(8) // 48
can be written like this in Python
def multiplier(factor): ? ? def inner(value): ? ? ? ? return value * factor ? ? return inner ? ? n = multiplier(6) n(8) #48
We can create a TypeAlias called number which is a Union (literally) of both an int and a float like;
from typing import TypeAlias, Union number: TypeAlias = Union[int, float]
To approach the parameters as JavaScript numbers.
So therefore, to annotate this function, we have;
def multiplier(factor: number) -> Callable[[number], number]:
def inner(value: number) -> inner:
return value * factor
return inner
a = multiplier(4.5)
a(3) #13.5
Generic functions
The classic generic function example is
def pick(array, index):
return array[index]
pick([1,2,3], 2) #3
Using TypeVar we can now create generic verbose (more verbose than typescript).
from typing import TypeVar
T = TypeVar("T") # the argument and the name of the variable should be the same
so that we have
from typing import TypeVar, Sequence
def pick(array: Sequence[T], index: int) -> T:
return array[index]
print(pick([1,2,3,4], 2))
So what about a custom myMap function that acts like map in JavaScript. such that we have;
Remember: map() in Python returns an Iterable type not a List type
def myMap(array, fn):
return map(fn, array)
def twice(n): return n * 2
print(myMap([1,2,3], twice))
We can use a mixture of Callable and TypeVar types to annotate this function. Observe...
from typing import TypeVar, Iterable, Callable
Input = TypeVar("Input") # Input and "Input" must be the same
Output = TypeVar("Output")
def myMap(array: Iterable[Input], fn: Callable[[Input], Output]) -> Iterable[Output]:
return map(fn, array)
def twice(n: int) -> int: return n * 2
print(myMap([1,2,3], twice))
or we can alias the Callable function
from typing import TypeVar, Iterable, Callable
Input = TypeVar("Input")
Output = TypeVar("Output")
MappableFunction = Callable[[Input], Output]
def myMap(array: Iterable[Input], fn: MappableFunction[Input, Output]) -> Iterable[Output]:
return map(fn, array)
Observe that MappableFunction takes those generic types Input and Output and applies them to the context of Callable[[Input], Output].
Take a minute to think of how the myFilter function will be annotated?
Well if you thought of this
from typing import Iterable, TypeVar, Callable
Input = TypeVar("Input")
def myFilter(array: Iterable[Input], fn: Callable[[Input], bool]) -> Iterable[Input]:
return filter(fn, array)
You're right
Generic Classes
I know I'm not supposed to be talking about class annotation but give me some time to explain generic classes.
If you came from the Typescript-verse, this was how you would define them
class GenericStore<Type>{
stores: Array<Type> = []
constructor(){
this.stores = []
}
add(item: Type){
this.stores.push(item)
}
}
const g1 = new GenericStore<string>(); //g1.stores: Array<string>
g1.add("Hello") //only string are allowed
But in Python they are rather different and awkward.
- First we import the Generic type, then we make them the child of the Generic class
So to recreate this GenericStore class in Python
Callable[[argtype1, argtype2, argtype3], returnType]
Why should I learn how to annotate functions in Python?
As I have said in the former blog, It helps in building a much smarter type system which in turn reduces your chances of bugs (especially when using static file checkers like mypy). Also when writing libraries (or SDKs) using a robust type system can improve the productivity of the developer using the library by a margin (mostly because of editor suggestions)
If you have any questions or there are mistakes in this writing, feel free to share them in the comments below ?
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