# Type inference and type annotations

## Type inference

The initial assignment defines a variable. If you do not explicitly specify the type of the variable, mypy infers the type based on the static type of the value expression:

    i = 1           # Infer type int for i
    l = [1, 2]      # Infer type List[int] for l

Type inference is bidirectional and takes context into account. For example, the following is valid:

    def f(l: List[object]) -> None:
        l = [1, 2]  # Infer type List[object] for [1, 2]

In an assignment, the type context is determined by the assignment target. In this case this is `l`, which has the type `List[object]`. The value expression `[1, 2]` is type checked in this context and given the type `List[object]`. In the previous example we introduced a new variable `l`, and here the type context was empty.

Note that the following is not valid, since `List[int]` is not compatible with `List[object]`:

    def f(l: List[object], k: List[int]) -> None:
        l = k       # Type check error: incompatible types in assignment

The reason why the above assignment is disallowed is that allowing the assignment could result in non-int values stored in a list of `int`:

    def f(l: List[object], k: List[int]) -> None:
        l = k
        l.append('x')
        print(k[-1])  # Ouch; a string in List[int]

You can still run the above program; it prints `x`. This illustrates the fact that static types are used during type checking, but they do not affect the runtime behavior of programs. You can run programs with type check failures, which is often very handy when performing a large refactoring. Thus you can always ‘work around’ the type system, and it doesn’t really limit what you can do in your program.

Type inference is not used in dynamically typed functions (those without an explicit return type) — every local variable type defaults to `Any`, which is discussed later.

## Explicit types for variables

You can override the inferred type of a variable by using a special type comment after an assignment statement:

    x = 1  # type: Union[int, str]

Without the type comment, the type of `x` would be just `int`. We use an annotation to give it a more general type `Union[int, str]`. Mypy checks that the type of the initializer is compatible with the declared type. The following example is not valid, since the initializer is a floating point number, and this is incompatible with the declared type:

    x = 1.1  # type: Union[int, str]  # Error!

Python 3.6 introduced a new syntax for variable annotations, which resembles function annotations:

    x: Union[int, str] = 1

We’ll use both syntax variants in examples. The syntax variants are mostly interchangeable, but the Python 3.6 syntax allows defining the type of a variable without initialization, which is not possible with the comment-based syntax:

    x: str  # Declare type of 'x' without initialization

Note

The best way to think about this is that the type comment sets the type of the variable, not the type of the expression. To force the type of an expression you can use `cast(<type>, <expression>)`.

## Explicit types for collections

The type checker cannot always infer the type of a list or a dictionary. This often arises when creating an empty list or dictionary and assigning it to a new variable that doesn’t have an explicit variable type. In these cases you can give the type explicitly using a type annotation comment:

    l = []  # type: List[int]       # Create empty list with type List[int]
    d = {}  # type: Dict[str, int]  # Create empty dictionary (str -> int)

Similarly, you can also give an explicit type when creating an empty set:

    s = set()  # type: Set[int]

## Declaring multiple variable types at a time

You can declare more than a single variable at a time. In order to nicely work with multiple assignment, you must give each variable a type separately:

    i, found = 0, False # type: int, bool

You can optionally use parentheses around the types, assignment targets and assigned expression:

    i, found = 0, False # type: (int, bool)      # OK
    (i, found) = 0, False # type: int, bool      # OK
    i, found = (0, False) # type: int, bool      # OK
    (i, found) = (0, False) # type: (int, bool)  # OK

## Starred expressions

In most cases, mypy can infer the type of starred expressions from the right-hand side of an assignment, but not always:

    a, *bs = 1, 2, 3   # OK
    p, q, *rs = 1, 2   # Error: Type of rs cannot be inferred

On first line, the type of `bs` is inferred to be `List[int]`. However, on the second line, mypy cannot infer the type of `rs`, because there is no right-hand side value for `rs` to infer the type from. In cases like these, the starred expression needs to be annotated with a starred type:

    p, q, *rs = 1, 2  # type: int, int, *List[int]

Here, the type of `rs` is set to `List[int]`.

## Types in stub files

Stub files are written in normal Python 3 syntax, but generally leaving out runtime logic like variable initializers, function bodies, and default arguments, replacing them with ellipses.

In this example, each ellipsis `...` is literally written in the stub file as three dots:

    x = ...  # type: int
    def afunc(code: str) -> int: ...
    def afunc(a: int, b: int=...) -> int: ...

Note

The ellipsis `...` is also used with a different meaning in callable types and tuple types.