Type inference and type annotations¶

Type inference¶

For most variables, if you do not explicitly specify its type, mypy will infer the correct type based on what is initially assigned to the variable.

# Mypy will infer the type of these variables, despite no annotations
i = 1
reveal_type(i)  # Revealed type is "builtins.int"
l = [1, 2]
reveal_type(l)  # Revealed type is "builtins.list[builtins.int]"

Note

Note that mypy will not use type inference in dynamically typed functions (those without a function type annotation) — every local variable type defaults to Any in such functions. For more details, see Dynamically typed code.

def untyped_function():
    i = 1
    reveal_type(i) # Revealed type is "Any"
                   # 'reveal_type' always outputs 'Any' in unchecked functions

Explicit types for variables¶

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

x: 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 int | str (this type means that the value can be either an int or a str).

The best way to think about this is that the type annotation sets the type of the variable, not the type of the expression. For instance, mypy will complain about the following code:

x: int | str = 1.1  # error: Incompatible types in assignment
                    # (expression has type "float", variable has type "int | str")

Note

To explicitly override the type of an expression you can use cast(<type>, <expression>). See Casts for details.

Note that you can explicitly declare the type of a variable without giving it an initial value:

# We only unpack two values, so there's no right-hand side value
# for mypy to infer the type of "cs" from:
a, b, *cs = 1, 2  # error: Need type annotation for "cs"

rs: list[int]  # no assignment!
p, q, *rs = 1, 2  # OK

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:

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

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

l: list[int] = []       # Create empty list of int
d: dict[str, int] = {}  # Create empty dictionary (str -> int)

Note

Using type arguments (e.g. list[int]) on builtin collections like list, dict, tuple, and set only works in Python 3.9 and later. For Python 3.8 and earlier, you must use List (e.g. List[int]), Dict, and so on.

Compatibility of container types¶

A quick note: container types can sometimes be unintuitive. We’ll discuss this more in Invariance vs covariance. For example, the following program generates a mypy error, because mypy treats list[int] as incompatible with list[object]:

def f(l: list[object], k: list[int]) -> None:
    l = k  # 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]

Other container types like dict and set behave similarly.

You can still run the above program; it prints x. This illustrates the fact that static types 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.

Mypy will take into account the type of the variable on the left-hand side of an assignment when inferring the type of the expression on the right-hand side. For example, the following will type check:

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

The value expression [1, 2] is type checked with the additional context that it is being assigned to a variable of type list[object]. This is used to infer the type of the expression as list[object].

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:

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:

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:

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

Silencing type errors¶

You might want to disable type checking on specific lines, or within specific files in your codebase. To do that, you can use a # type: ignore comment.

For example, say in its latest update, the web framework you use can now take an integer argument to run(), which starts it on localhost on that port. Like so:

# Starting app on http://localhost:8000
app.run(8000)

However, the devs forgot to update their type annotations for run, so mypy still thinks run only expects str types. This would give you the following error:

error: Argument 1 to "run" of "A" has incompatible type "int"; expected "str"

If you cannot directly fix the web framework yourself, you can temporarily disable type checking on that line, by adding a # type: ignore:

# Starting app on http://localhost:8000
app.run(8000)  # type: ignore

This will suppress any mypy errors that would have raised on that specific line.

You should probably add some more information on the # type: ignore comment, to explain why the ignore was added in the first place. This could be a link to an issue on the repository responsible for the type stubs, or it could be a short explanation of the bug. To do that, use this format:

# Starting app on http://localhost:8000
app.run(8000)  # type: ignore  # `run()` in v2.0 accepts an `int`, as a port

Type ignore error codes¶

By default, mypy displays an error code for each error:

error: "str" has no attribute "trim"  [attr-defined]

It is possible to add a specific error-code in your ignore comment (e.g. # type: ignore[attr-defined]) to clarify what’s being silenced. You can find more information about error codes here.

Other ways to silence errors¶

You can get mypy to silence errors about a specific variable by dynamically typing it with Any. See Dynamically typed code for more information.

from typing import Any

def f(x: Any, y: str) -> None:
    x = 'hello'
    x += 1  # OK

You can ignore all mypy errors in a file by adding a # mypy: ignore-errors at the top of the file:

# mypy: ignore-errors
# This is a test file, skipping type checking in it.
import unittest
...

You can also specify per-module configuration options in your The mypy configuration file. For example:

# Don't report errors in the 'package_to_fix_later' package
[mypy-package_to_fix_later.*]
ignore_errors = True

# Disable specific error codes in the 'tests' package
# Also don't require type annotations
[mypy-tests.*]
disable_error_code = var-annotated, has-type
allow_untyped_defs = True

# Silence import errors from the 'library_missing_types' package
[mypy-library_missing_types.*]
ignore_missing_imports = True

Finally, adding a @typing.no_type_check decorator to a class, method or function causes mypy to avoid type checking that class, method or function and to treat it as not having any type annotations.

@typing.no_type_check
def foo() -> str:
   return 12345  # No error!