I am quite excited about this new feature in
python: simple dependent types.
“Dependent types” might sound complex, but it is not. Instead, it is a useful feature and I am going to show how it works and when you should rely on it.
We are not going to dive deep into the theory and I am not going to provide any kind of math formulas here. As Steven Hawking once said:
Someone told me that each equation I included in the book would halve the sales. I, therefore, resolved not to have any equations at all. In the end, however, I did put in one equation, Einstein’s famous equation, E=mc^2. I hope that this will not scare off half of my potential readers
What is dependent typing? It is a concept when you rely on values of some types, not just raw types.
Consider this example:
from typing import Union def return_int_or_str(flag: bool) -> Union[str, int]: if flag: return 'I am a string!' return 0
We can clearly see that depending on the value of
flag we can get
int values. The result type will be
Union[str, int]. And every time we call this function with mixed-up-return-type we have to check what type we actually got and what to do with it. This is inconvenient and makes your code more complex.
You might say that this function is just bad, and it should not behave the way it does now. Correct, but there are some real-world use-cases where this is required by design.
open function from the standard library. How often did you get runtime errors because you mixed up
bytes? It happened a thousand times to me. And I do not want this to happen again! So, we will write type-safe code for this time.
def open_file(filename: str, mode: str): return open(filename, mode)
What return type do we expect here?
str? Wait for a second! We can call it like so:
open_file('some.txt', 'rb') and it will return
bytes! So, the return type is
Union[IO[str], IO[bytes]]. And it really hard to work with it. We will end up with a lot of conditions, unneeded casts, and guards.
Dependent types solve this problem. But, before we will move any further - we have to know some primitives that we are going to use later.
Literal and @overload
If you don’t have
typing_extensions installed, you need to install the latest version of these packages.
» pip install mypy typing_extensions
And now we are ready to rewrite our code with the power of
from typing import overload from typing_extension import Literal
A quick side note:
typing is a builtin
python module where all possible types are defined. And the development speed of this module is limited to the new
python version releases. And
typing_extensions is an official package for new types that will be available in the future releases of
python. So, it does solve all issues with the release speed and frequency of regular
Literal type represents a specific value of the specific type.
from typing_extensions import Literal def function(x: Literal) -> Literal: return x function(1) # => OK! function(2) # => Argument has incompatible type "Literal"; expected "Literal"
To run this code use:
mypy --python-version=3.6 --strict test.py. It will remain the same for all examples in this article.
That’s awesome! But, what is the difference between
from typing_extensions import Literal def function(x: int = 0, y: Literal = 0) -> int: reveal_type(x) # => Revealed type is 'builtins.int' reveal_type(y) # => Revealed type is 'Literal' return x
Revealed types differ. The only way to get
Literal type is to annotate is as
Literal. It is done to save backward compatibility with older versions of
mypy and not to detect
x: int = 0 as a
Literal type. Because the value of
x can later be changed.
You can use
Literal everywhere where a regular
int can be used, but not the other way around.
from typing_extensions import Literal def function(x: int, y: Literal) -> int: return x x1: int = 0 y1: Literal = 0 function(y1, y1) function(x1, x1) # => Argument 2 has incompatible type "int"; expected "Literal"
x1 is a variable - it cannot be used where we expect
In the first part of this series, I wrote an article about using real constants in
python. Read it if you do not know the difference between variables and constants in
Will constants help in this case? Yes, they will!
from typing_extensions import Literal, Final def function(x: int = 0, y: Literal = 0) -> int: return x x: Final = 0 y: Literal = 0 function(y, y) function(x, x)
As you can see, when declaring some value
Final - we create a constant. That cannot be changed. And it matches what
Literal is. Source code implementation of these two types is also quite similar.
Why do I constantly call dependent types in
python simple? Because it is currently limited to simple values:
None. It can not currently work with tuples, lists, dicts, custom types and classes, etc. But, you can track the development progress in this thread.
Do not forget about the official docs.
The next thing we will need is
@overload decorator. It is required to define multiple function declarations with different input types and results.
Imagine, we have a situation when we need to write a function that decreases a value. It should work with both
int inputs. When given
str it should return all the input characters except the last one, but when given
int it should return the previous number.
from typing import Union def decrease(first: Union[str, int]) -> Union[str, int]: if isinstance(first, int): return first - 1 return first[:-1] reveal_type(decrease(1)) # => Revealed type is 'Union[builtins.str, builtins.int]' reveal_type(decrease('abc')) # => Revealed type is 'Union[builtins.str, builtins.int]'
Not too practical, isn’t it?
mypy still does not know what specific type was returned. We can enhance the typing with
from typing import Union, overload @overload def decrease(first: str) -> str: """Decreases a string.""" @overload def decrease(first: int) -> int: """Decreases a number.""" def decrease(first: Union[str, int]) -> Union[str, int]: if isinstance(first, int): return first - 1 return first[:-1] reveal_type(decrease(1)) # => Revealed type is 'builtins.int' reveal_type(decrease('abc')) # => Revealed type is 'builtins.str'
In this case, we define several function heads to give
mypy enough information about what is going on. And these head functions are only used during the type checking this module. As you can see only one function definition actually contains some logic. You can create as many function heads as you need.
The idea is: whenever
mypy finds a function with multiple
@overload heads it tries to match input values to these declarations. When it finds the first match - it returns the result type.
Official documentation might also help you to understand how to use it in your projects.
Now, we are going to combine our new knowledge about
@overload together to solve our problem with
open. At last!
Remember, we need to return
'rb' mode and
And we need to know the exact return type.
An algorithm will be:
- Write several
@overloaddecorators to match all possible cases
Literaltypes when we expect to get
- Write function logic in a general case
from typing import IO, Any, Union, overload from typing_extensions import Literal @overload def open_file(filename: str, mode: Literal['r']) -> IO[str]: """When 'r' is supplied we return 'str'.""" @overload def open_file(filename: str, mode: Literal['rb']) -> IO[bytes]: """When 'rb' is supplied we return 'bytes' instead of a 'str'.""" @overload def open_file(filename: str, mode: str) -> IO[Any]: """Any other options might return Any-thing!.""" def open_file(filename: str, mode: str) -> IO[Any]: return open(filename, mode) reveal_type(open_file('some.txt', 'r')) # => Revealed type is 'typing.IO[builtins.str]' reveal_type(open_file('some.txt', 'rb')) # => Revealed type is 'typing.IO[builtins.bytes]' reveal_type(open_file('some.txt', 'other')) # => Revealed type is 'typing.IO[AnyStr]'
What do we have here? Three
@overload decorators and a function body with logic. First
@overload decorator declares to return
Literal parameter, the second one tells to return
bytes when we use
'rb' parameter. And the third one is fallback. Whenever we provide another any other mode - we can get both
Now, our problem is solved. We supply some specific values into the function, we receive some specific type in return. It makes our code easier to read and safer to execute.
Thanks how dependent types work in
I hope this little tutorial helped you to understand typing in
python a little bit better. In the future articles, I will cover more complex topics about
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