Type inference and type annotations
===================================

Type inference
**************

Mypy considers the initial assignment as the definition of 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:

.. code-block:: python

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

Type inference is not used in dynamically typed functions (those
without a function type annotation) — every local variable type defaults
to ``Any`` in such functions. ``Any`` is discussed later in more detail.

.. _explicit-var-types:

Explicit types for variables
****************************

You can override the inferred type of a variable by using a
variable type annotation:

.. code-block:: python

   from typing import Union

   x: Union[int, str] = 1

Without the type annotation, the type of ``x`` would be just ``int``. We
use an annotation to give it a more general type ``Union[int, str]`` (this
type means that the value can be either an ``int`` or a ``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:

.. code-block:: python

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

The variable annotation syntax is available starting from Python 3.6.
In earlier Python versions, you can use a special comment after an
assignment statement to declare the type of a variable:

.. code-block:: python

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

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

.. code-block:: python

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

.. note::

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

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. Here is an example where mypy can't infer the type
without some help:

.. code-block:: python

   l = []  # Error: Need type annotation for 'l'

In these cases you can give the type explicitly using a type annotation:

.. code-block:: python

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

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

.. code-block:: python

   s: Set[int] = set()

Compatibility of container types
********************************

The following program generates a mypy error, since ``List[int]``
is not compatible with ``List[object]``:

.. code-block:: python

   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``:

.. code-block:: python

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

Other container types like :py:class:`~typing.Dict` and :py:class:`~typing.Set` behave similarly. We
will discuss how you can work around this in :ref:`variance`.

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.

Context in type inference
*************************

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

.. code-block:: python

   def f(l: List[object]) -> None:
       l = [1, 2]  # Infer type List[object] for [1, 2], not List[int]

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.

Declared argument types are also used for type context. In this program
mypy knows that the empty list ``[]`` should have type ``List[int]`` based
on the declared type of ``arg`` in ``foo``:

.. code-block:: python

    def foo(arg: List[int]) -> None:
        print('Items:', ''.join(str(a) for a in arg))

    foo([])  # OK

However, context only works within a single statement. Here mypy requires
an annotation for the empty list, since the context would only be available
in the following statement:

.. code-block:: python

    def foo(arg: List[int]) -> None:
        print('Items:', ', '.join(arg))

    a = []  # Error: Need type annotation for 'a'
    foo(a)

Working around the issue is easy by adding a type annotation:

.. code-block:: Python

    ...
    a: List[int] = []  # OK
    foo(a)

Declaring multiple variable types at a time
*******************************************

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

.. code-block:: python

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

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

.. code-block:: python

   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:

.. code-block:: python

    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:

.. code-block:: python

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

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