However, the whole story began a little earlier by introducing arbitrary metadata annotations. In the previous section, we had a look at the basics of type annotations in Python. Otherwise, you need to stick to typing.List. However, if you are using at least Python 3.7, you can enable it by importing annotations from the _future_ module. The generic syntax list is only supported from Python 3.9 onwards. In essence, you can use the very same syntax to annotate plain variables in your code: greet: str = "Hello World"Īnnotating variables in loop-constructs is somewhat different.įor instance, if you need to annotate a variable in a for-loop, you need to do it before the for-loop: # loop.py So let us have a quick look at this, too. You may find yourself in a situation, where you need to annotate variables as well.įortunately, variable annotations were introduced in Python 3.6 and specified in PEP 526. The same way we annotated the division() function, methods of classes can be annotated. Let's define our own Number type alias, which stands for Union, and use it for our division() function: # division.pyĭef division(divident: Number, divisor: Number) -> float: This is especially true if you want to annotate dictionaries with a certain schema.įortunately, Python allows us to define type aliases. While the function's annotation is still pretty simple, it can massively grow in complexity. Success: no issues found in 1 source file Let's fix the function annotation first and check, whether mypy is satisfied: # division.pyĭef division(divident: Union, divisor: Union) -> float: To fix this, we use a typing.Union construct. To use mypy, you need to install it first via python -m pip install mypy. Instead, we want to be able to divide floating-point numbers, too.Īs a result of Python's dynamic-typed nature, we can still call division() with floating-point numbers and don't get an error even though we annotated it with integer types: # previous code in division.pyĮven though no runtime exception is raised, a static type checker like mypy will print an error: $ mypy division.pyĭivision.py:7: error: Argument 1 to "division" has incompatible type "float" expected "int"įound 1 error in 1 file (checked 1 source file) However, a division function only accepting integers is not very useful. The return type is specified by adding an arrow -> followed by the type after the parameter list. To annotate the function, we can use the following syntax: # division.pyĭef division(divident: int, divisor: int) -> float:Īfter the parameter's identifier, we add a colon followed by the type of the parameter. Suppose we have a function called division(), which takes two parameters (both integers) and returns a floating-point number.Ī possible implementation looks like this: # division.py Let's start with annotating functions as this is probably the use case most of you have already seen. To answer the question "What are annotated type hints and why do I need them?", we need to have a look at two things first: The core of the article is made up of having a closer look at annotated type hints - a feature introduced in Python 3.9 - and how to leverage the full potential of Python's type hints. The overall goal of the article at hand is to give you a general overview of type hints, first. The aim was not to turn Python into a static-typed language, but instead "opening up Python code to easier static analysis and refactoring, potential runtime type checking, and (perhaps, in some contexts) code generation utilizing type information." With Python 3.5 and PEP 484, Python got type hints or type annotations. Unlike Java or other static-typed languages, Python allows you to assign different value types to a single variable as follows: > x = 5 Python has been and will probably always be a dynamic-typed programming language.
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