Note 4.7.1.
Although type annotations are ignored by the Python interpreter, there are tools such as mypy that can analyze code containing type annotations and flag potential problems.
1
http://mypy-lang.org/Section 4.7 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 we hinted at earlier, that can help reduce the amount of sleuthing that is needed.
Consider the following function definition:
def duplicate(msg):
"""Returns a string containing two copies of `msg`"""
return msg + msg
This function is intended to duplicate a string 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?)Python allows you to indicate the intended a parameter’s data type and the function’s return value type in a function definition using a special notation demonstrated in this example.Notice the syntax:
This definition of
duplicate makes use of type annotations to suggest the function’s expected parameter and return value types. A type annotation is an optional notation that specifies the data type for parameters and function result. It is an effort to tell 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.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.Here are some more examples of functions with type annotations:
You are already familiar with many of the possible data types one could include in an annotation. You will know
str, int, and float types. And later you will recognize and use bool and list, and Python has still more data types.It’s extremely 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')
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.Type annotations are an optional aspect of documenting functions, docstrings, and are an important tool to increase the readability of your code. You should use them in your programs.
Check your understanding
Checkpoint 4.7.2.
- 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.
What happens when the following is executed?
def display(msg: str):
"""Displays `msg` on the screen"""
print(msg + 2)
display(2.5)
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