Type Annotations

Section 8.5 Type Annotations

In the previous section, we discussed the decoding work that is required when you look at a function and are trying to determine the types of its parameters. In this section, we’ll introduce a feature that can help reduce the amount of sleuthing that is needed.

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Consider the following function definition:

def duplicate(msg):
    """Returns a string containing two copies of `msg`"""

    return msg + msg

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This function is intended to duplicate a message; if called with the value ‘Hello’, it returns the value ‘HelloHello’. If called with other types of data, however, it will not work properly. (What will the function do if given an int or a float value?)

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Python allows you to indicate the intended type of the function parameters and the type of the function return value in a function definition using a special notation demonstrated in this example:

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This definition of duplicate makes use of type annotations that indicate the function’s parameter type and return type. A type annotation is an optional notation that specifies the type of a parameter or function result. It tells the programmer using the function what kind of data to pass to the function, and what kind of data to expect when the function returns a value.

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In the definition above, the annotation : str in msg: str indicates that the caller should pass a str value as an argument. The annotation -> str indicates that the function will produce a str result.

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Here are some more examples of functions with type annotations:

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It’s important to understand that adding type annotations to a function definition does not cause the Python interpreter to check that the values passed to a function are the expected types or cause the returned value to be converted to the expected type. For example, if the function add in the example above is called like this:

result = add('5', '15')

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the function will receive two string values, concatenate them, and return the resulting string ‘515’. The int annotations are completely ignored by the Python interpreter. Think of type annotations as a kind of function documentation and remember that they have no effect on the program’s behavior.

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Type annotations are an optional aspect of documenting functions. Still, type annotations are an important tool to increase the readability of your code, and you should use them in your programs.

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Note 8.5.1.

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Although type annotations are ignored by the Python interpreter, there are tools such as mypy

http://mypy-lang.org/

that can analyze your code containing type annotations and flag potential problems.

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Check your understanding

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Checkpoint 8.5.2.

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What happens when the following is executed?

def display(msg: str):
    """Displays `msg` on the screen"""
    print(msg + 2)

display(2.5)

The value 4.5 is displayed on the screen.
Correct! Python ignores the ’: str’ annotation and returns the sum of msg (the float 2.5) + 2.
The value 2.52 is displayed on the screen.
Incorrect. In this call, msg contains the float value 2.5; the ’: str’ annotation serves only as documentation.
A crash occurs because 2.5 is not a string
Incorrect. Python ignores the ’: str’ annotation and allows the float value 2.5 to be passed to msg.
A crash occurs because the expression ’msg + 2’ illegally attempts to concatenate a str and an int
Incorrect. In this call, msg contains the float value 2.5, not a str, so the + operation is legal.

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