Error codes enabled by default

This section documents various errors codes that mypy can generate with default options. See Error codes for general documentation about error codes. Error codes for optional checks documents additional error codes that you can enable.

Check that attribute exists [attr-defined]

Mypy checks that an attribute is defined in the target class or module when using the dot operator. This applies to both getting and setting an attribute. New attributes are defined by assignments in the class body, or assignments to self.x in methods. These assignments don’t generate attr-defined errors.


class Resource:
    def __init__(self, name: str) -> None: = name

r = Resource('x')
print(  # OK
print(  # Error: "Resource" has no attribute "id"  [attr-defined] = 5  # Error: "Resource" has no attribute "id"  [attr-defined]

This error code is also generated if an imported name is not defined in the module in a from ... import statement (as long as the target module can be found):

# Error: Module "os" has no attribute "non_existent"  [attr-defined]
from os import non_existent

A reference to a missing attribute is given the Any type. In the above example, the type of non_existent will be Any, which can be important if you silence the error.

Check that attribute exists in each union item [union-attr]

If you access the attribute of a value with a union type, mypy checks that the attribute is defined for every type in that union. Otherwise the operation can fail at runtime. This also applies to optional types.


from typing import Union

class Cat:
    def sleep(self) -> None: ...
    def miaow(self) -> None: ...

class Dog:
    def sleep(self) -> None: ...
    def follow_me(self) -> None: ...

def func(animal: Union[Cat, Dog]) -> None:
    # OK: 'sleep' is defined for both Cat and Dog
    # Error: Item "Cat" of "Union[Cat, Dog]" has no attribute "follow_me"  [union-attr]

You can often work around these errors by using assert isinstance(obj, ClassName) or assert obj is not None to tell mypy that you know that the type is more specific than what mypy thinks.

Check that name is defined [name-defined]

Mypy expects that all references to names have a corresponding definition in an active scope, such as an assignment, function definition or an import. This can catch missing definitions, missing imports, and typos.

This example accidentally calls sort() instead of sorted():

x = sort([3, 2, 4])  # Error: Name "sort" is not defined  [name-defined]

Check that a variable is not used before it’s defined [used-before-def]

Mypy will generate an error if a name is used before it’s defined. While the name-defined check will catch issues with names that are undefined, it will not flag if a variable is used and then defined later in the scope. used-before-def check will catch such cases.


print(x)  # Error: Name "x" is used before definition [used-before-def]
x = 123

Check arguments in calls [call-arg]

Mypy expects that the number and names of arguments match the called function. Note that argument type checks have a separate error code arg-type.


from typing import Sequence

def greet(name: str) -> None:
     print('hello', name)

greet('jack')  # OK
greet('jill', 'jack')  # Error: Too many arguments for "greet"  [call-arg]

Check argument types [arg-type]

Mypy checks that argument types in a call match the declared argument types in the signature of the called function (if one exists).


from typing import Optional

def first(x: list[int]) -> Optional[int]:
    return x[0] if x else 0

t = (5, 4)
# Error: Argument 1 to "first" has incompatible type "tuple[int, int]";
#        expected "list[int]"  [arg-type]

Check calls to overloaded functions [call-overload]

When you call an overloaded function, mypy checks that at least one of the signatures of the overload items match the argument types in the call.


from typing import overload, Optional

def inc_maybe(x: None) -> None: ...

def inc_maybe(x: int) -> int: ...

def inc_maybe(x: Optional[int]) -> Optional[int]:
     if x is None:
         return None
         return x + 1

inc_maybe(None)  # OK
inc_maybe(5)  # OK

# Error: No overload variant of "inc_maybe" matches argument type "float"  [call-overload]

Check validity of types [valid-type]

Mypy checks that each type annotation and any expression that represents a type is a valid type. Examples of valid types include classes, union types, callable types, type aliases, and literal types. Examples of invalid types include bare integer literals, functions, variables, and modules.

This example incorrectly uses the function log as a type:

def log(x: object) -> None:
    print('log:', repr(x))

# Error: Function "t.log" is not valid as a type  [valid-type]
def log_all(objs: list[object], f: log) -> None:
    for x in objs:

You can use Callable as the type for callable objects:

from typing import Callable

# OK
def log_all(objs: list[object], f: Callable[[object], None]) -> None:
    for x in objs:

Require annotation if variable type is unclear [var-annotated]

In some cases mypy can’t infer the type of a variable without an explicit annotation. Mypy treats this as an error. This typically happens when you initialize a variable with an empty collection or None. If mypy can’t infer the collection item type, mypy replaces any parts of the type it couldn’t infer with Any and generates an error.

Example with an error:

class Bundle:
    def __init__(self) -> None:
        # Error: Need type annotation for "items"
        #        (hint: "items: list[<type>] = ...")  [var-annotated]
        self.items = []

reveal_type(Bundle().items)  # list[Any]

To address this, we add an explicit annotation:

 class Bundle:
     def __init__(self) -> None:
         self.items: list[str] = []  # OK

reveal_type(Bundle().items)  # list[str]

Check validity of overrides [override]

Mypy checks that an overridden method or attribute is compatible with the base class. A method in a subclass must accept all arguments that the base class method accepts, and the return type must conform to the return type in the base class (Liskov substitution principle).

Argument types can be more general is a subclass (i.e., they can vary contravariantly). The return type can be narrowed in a subclass (i.e., it can vary covariantly). It’s okay to define additional arguments in a subclass method, as long all extra arguments have default values or can be left out (*args, for example).


from typing import Optional, Union

class Base:
    def method(self,
               arg: int) -> Optional[int]:

class Derived(Base):
    def method(self,
               arg: Union[int, str]) -> int:  # OK

class DerivedBad(Base):
    # Error: Argument 1 of "method" is incompatible with "Base"  [override]
    def method(self,
               arg: bool) -> int:

Check that function returns a value [return]

If a function has a non-None return type, mypy expects that the function always explicitly returns a value (or raises an exception). The function should not fall off the end of the function, since this is often a bug.


# Error: Missing return statement  [return]
def show(x: int) -> int:

# Error: Missing return statement  [return]
def pred1(x: int) -> int:
    if x > 0:
        return x - 1

# OK
def pred2(x: int) -> int:
    if x > 0:
        return x - 1
        raise ValueError('not defined for zero')

Check that functions don’t have empty bodies outside stubs [empty-body]

This error code is similar to the [return] code but is emitted specifically for functions and methods with empty bodies (if they are annotated with non-trivial return type). Such a distinction exists because in some contexts an empty body can be valid, for example for an abstract method or in a stub file. Also old versions of mypy used to unconditionally allow functions with empty bodies, so having a dedicated error code simplifies cross-version compatibility.

Note that empty bodies are allowed for methods in protocols, and such methods are considered implicitly abstract:

from abc import abstractmethod
from typing import Protocol

class RegularABC:
    def foo(self) -> int:
        pass  # OK
    def bar(self) -> int:
        pass  # Error: Missing return statement  [empty-body]

class Proto(Protocol):
    def bar(self) -> int:
        pass  # OK

Check that return value is compatible [return-value]

Mypy checks that the returned value is compatible with the type signature of the function.


def func(x: int) -> str:
    # Error: Incompatible return value type (got "int", expected "str")  [return-value]
    return x + 1

Check types in assignment statement [assignment]

Mypy checks that the assigned expression is compatible with the assignment target (or targets).


class Resource:
    def __init__(self, name: str) -> None: = name

r = Resource('A') = 'B'  # OK

# Error: Incompatible types in assignment (expression has type "int",
#        variable has type "str")  [assignment] = 5

Check that assignment target is not a method [method-assign]

In general, assigning to a method on class object or instance (a.k.a. monkey-patching) is ambiguous in terms of types, since Python’s static type system cannot express the difference between bound and unbound callable types. Consider this example:

class A:
    def f(self) -> None: pass
    def g(self) -> None: pass

def h(self: A) -> None: pass

A.f = h  # Type of h is Callable[[A], None]
A().f()  # This works
A.f = A().g  # Type of A().g is Callable[[], None]
A().f()  # ...but this also works at runtime

To prevent the ambiguity, mypy will flag both assignments by default. If this error code is disabled, mypy will treat the assigned value in all method assignments as unbound, so only the second assignment will still generate an error.


This error code is a subcode of the more general [assignment] code.

Check type variable values [type-var]

Mypy checks that value of a type variable is compatible with a value restriction or the upper bound type.


from typing import TypeVar

T1 = TypeVar('T1', int, float)

def add(x: T1, y: T1) -> T1:
    return x + y

add(4, 5.5)  # OK

# Error: Value of type variable "T1" of "add" cannot be "str"  [type-var]
add('x', 'y')

Check uses of various operators [operator]

Mypy checks that operands support a binary or unary operation, such as + or ~. Indexing operations are so common that they have their own error code index (see below).


# Error: Unsupported operand types for + ("int" and "str")  [operator]
1 + 'x'

Check indexing operations [index]

Mypy checks that the indexed value in indexing operation such as x[y] supports indexing, and that the index expression has a valid type.


a = {'x': 1, 'y': 2}

a['x']  # OK

# Error: Invalid index type "int" for "dict[str, int]"; expected type "str"  [index]

# Error: Invalid index type "bytes" for "dict[str, int]"; expected type "str"  [index]
a[b'x'] = 4

Check list items [list-item]

When constructing a list using [item, ...], mypy checks that each item is compatible with the list type that is inferred from the surrounding context.


# Error: List item 0 has incompatible type "int"; expected "str"  [list-item]
a: list[str] = [0]

Check dict items [dict-item]

When constructing a dictionary using {key: value, ...} or dict(key=value, ...), mypy checks that each key and value is compatible with the dictionary type that is inferred from the surrounding context.


# Error: Dict entry 0 has incompatible type "str": "str"; expected "str": "int"  [dict-item]
d: dict[str, int] = {'key': 'value'}

Check TypedDict items [typeddict-item]

When constructing a TypedDict object, mypy checks that each key and value is compatible with the TypedDict type that is inferred from the surrounding context.

When getting a TypedDict item, mypy checks that the key exists. When assigning to a TypedDict, mypy checks that both the key and the value are valid.


from typing import TypedDict

class Point(TypedDict):
    x: int
    y: int

# Error: Incompatible types (expression has type "float",
#        TypedDict item "x" has type "int")  [typeddict-item]
p: Point = {'x': 1.2, 'y': 4}

Check TypedDict Keys [typeddict-unknown-key]

When constructing a TypedDict object, mypy checks whether the definition contains unknown keys, to catch invalid keys and misspellings. On the other hand, mypy will not generate an error when a previously constructed TypedDict value with extra keys is passed to a function as an argument, since TypedDict values support structural subtyping (“static duck typing”) and the keys are assumed to have been validated at the point of construction. Example:

from typing import TypedDict

class Point(TypedDict):
    x: int
    y: int

class Point3D(Point):
    z: int

def add_x_coordinates(a: Point, b: Point) -> int:
    return a["x"] + b["x"]

a: Point = {"x": 1, "y": 4}
b: Point3D = {"x": 2, "y": 5, "z": 6}

add_x_coordinates(a, b)  # OK

# Error: Extra key "z" for TypedDict "Point"  [typeddict-unknown-key]
add_x_coordinates(a, {"x": 1, "y": 4, "z": 5})

Setting a TypedDict item using an unknown key will also generate this error, since it could be a misspelling:

a: Point = {"x": 1, "y": 2}
# Error: Extra key "z" for TypedDict "Point"  [typeddict-unknown-key]
a["z"] = 3

Reading an unknown key will generate the more general (and serious) typeddict-item error, which is likely to result in an exception at runtime:

a: Point = {"x": 1, "y": 2}
# Error: TypedDict "Point" has no key "z"  [typeddict-item]
_ = a["z"]


This error code is a subcode of the wider [typeddict-item] code.

Check that type of target is known [has-type]

Mypy sometimes generates an error when it hasn’t inferred any type for a variable being referenced. This can happen for references to variables that are initialized later in the source file, and for references across modules that form an import cycle. When this happens, the reference gets an implicit Any type.

In this example the definitions of x and y are circular:

class Problem:
    def set_x(self) -> None:
        # Error: Cannot determine type of "y"  [has-type]
        self.x = self.y

    def set_y(self) -> None:
        self.y = self.x

To work around this error, you can add an explicit type annotation to the target variable or attribute. Sometimes you can also reorganize the code so that the definition of the variable is placed earlier than the reference to the variable in a source file. Untangling cyclic imports may also help.

We add an explicit annotation to the y attribute to work around the issue:

class Problem:
    def set_x(self) -> None:
        self.x = self.y  # OK

    def set_y(self) -> None:
        self.y: int = self.x  # Added annotation here

Check for an issue with imports [import]

Mypy generates an error if it can’t resolve an import statement. This is a parent error code of import-not-found and import-untyped

See Missing imports for how to work around these errors.

Check that import target can be found [import-not-found]

Mypy generates an error if it can’t find the source code or a stub file for an imported module.


# Error: Cannot find implementation or library stub for module named "m0dule_with_typo"  [import-not-found]
import m0dule_with_typo

See Missing imports for how to work around these errors.

Check that import target can be found [import-untyped]

Mypy generates an error if it can find the source code for an imported module, but that module does not provide type annotations (via PEP 561).


# Error: Library stubs not installed for "bs4"  [import-untyped]
import bs4
# Error: Skipping analyzing "no_py_typed": module is installed, but missing library stubs or py.typed marker  [import-untyped]
import no_py_typed

In some cases, these errors can be fixed by installing an appropriate stub package. See Missing imports for more details.

Check that each name is defined once [no-redef]

Mypy may generate an error if you have multiple definitions for a name in the same namespace. The reason is that this is often an error, as the second definition may overwrite the first one. Also, mypy often can’t be able to determine whether references point to the first or the second definition, which would compromise type checking.

If you silence this error, all references to the defined name refer to the first definition.


class A:
    def __init__(self, x: int) -> None: ...

class A:  # Error: Name "A" already defined on line 1  [no-redef]
    def __init__(self, x: str) -> None: ...

# Error: Argument 1 to "A" has incompatible type "str"; expected "int"
#        (the first definition wins!)

Check that called function returns a value [func-returns-value]

Mypy reports an error if you call a function with a None return type and don’t ignore the return value, as this is usually (but not always) a programming error.

In this example, the if f() check is always false since f returns None:

def f() -> None:

# OK: we don't do anything with the return value

# Error: "f" does not return a value (it only ever returns None)  [func-returns-value]
if f():
     print("not false")

Check instantiation of abstract classes [abstract]

Mypy generates an error if you try to instantiate an abstract base class (ABC). An abstract base class is a class with at least one abstract method or attribute. (See also abc module documentation)

Sometimes a class is made accidentally abstract, often due to an unimplemented abstract method. In a case like this you need to provide an implementation for the method to make the class concrete (non-abstract).


from abc import ABCMeta, abstractmethod

class Persistent(metaclass=ABCMeta):
    def save(self) -> None: ...

class Thing(Persistent):
    def __init__(self) -> None:

    ...  # No "save" method

# Error: Cannot instantiate abstract class "Thing" with abstract attribute "save"  [abstract]
t = Thing()

Safe handling of abstract type object types [type-abstract]

Mypy always allows instantiating (calling) type objects typed as Type[t], even if it is not known that t is non-abstract, since it is a common pattern to create functions that act as object factories (custom constructors). Therefore, to prevent issues described in the above section, when an abstract type object is passed where Type[t] is expected, mypy will give an error. Example:

from abc import ABCMeta, abstractmethod
from typing import List, Type, TypeVar

class Config(metaclass=ABCMeta):
    def get_value(self, attr: str) -> str: ...

T = TypeVar("T")
def make_many(typ: Type[T], n: int) -> List[T]:
    return [typ() for _ in range(n)]  # This will raise if typ is abstract

# Error: Only concrete class can be given where "Type[Config]" is expected [type-abstract]
make_many(Config, 5)

Check that call to an abstract method via super is valid [safe-super]

Abstract methods often don’t have any default implementation, i.e. their bodies are just empty. Calling such methods in subclasses via super() will cause runtime errors, so mypy prevents you from doing so:

from abc import abstractmethod
class Base:
    def foo(self) -> int: ...
class Sub(Base):
    def foo(self) -> int:
        return super().foo() + 1  # error: Call to abstract method "foo" of "Base" with
                                  # trivial body via super() is unsafe  [safe-super]
Sub().foo()  # This will crash at runtime.

Mypy considers the following as trivial bodies: a pass statement, a literal ellipsis ..., a docstring, and a raise NotImplementedError statement.

Check the target of NewType [valid-newtype]

The target of a NewType definition must be a class type. It can’t be a union type, Any, or various other special types.

You can also get this error if the target has been imported from a module whose source mypy cannot find, since any such definitions are treated by mypy as values with Any types. Example:

from typing import NewType

# The source for "acme" is not available for mypy
from acme import Entity  # type: ignore

# Error: Argument 2 to NewType(...) must be subclassable (got "Any")  [valid-newtype]
UserEntity = NewType('UserEntity', Entity)

To work around the issue, you can either give mypy access to the sources for acme or create a stub file for the module. See Missing imports for more information.

Check the return type of __exit__ [exit-return]

If mypy can determine that __exit__ always returns False, mypy checks that the return type is not bool. The boolean value of the return type affects which lines mypy thinks are reachable after a with statement, since any __exit__ method that can return True may swallow exceptions. An imprecise return type can result in mysterious errors reported near with statements.

To fix this, use either typing.Literal[False] or None as the return type. Returning None is equivalent to returning False in this context, since both are treated as false values.


class MyContext:
    def __exit__(self, exc, value, tb) -> bool:  # Error
        return False

This produces the following output from mypy: error: "bool" is invalid as return type for "__exit__" that always returns False note: Use "typing_extensions.Literal[False]" as the return type or change it to
    "None" note: If return type of "__exit__" implies that it may return True, the context
    manager may swallow exceptions

You can use Literal[False] to fix the error:

from typing import Literal

class MyContext:
    def __exit__(self, exc, value, tb) -> Literal[False]:  # OK
        return False

You can also use None:

class MyContext:
    def __exit__(self, exc, value, tb) -> None:  # Also OK

Check that naming is consistent [name-match]

The definition of a named tuple or a TypedDict must be named consistently when using the call-based syntax. Example:

from typing import NamedTuple

# Error: First argument to namedtuple() should be "Point2D", not "Point"
Point2D = NamedTuple("Point", [("x", int), ("y", int)])

Check that literal is used where expected [literal-required]

There are some places where only a (string) literal value is expected for the purposes of static type checking, for example a TypedDict key, or a __match_args__ item. Providing a str-valued variable in such contexts will result in an error. Note that in many cases you can also use Final or Literal variables. Example:

from typing import Final, Literal, TypedDict

class Point(TypedDict):
    x: int
    y: int

def test(p: Point) -> None:
    X: Final = "x"
    p[X]  # OK

    Y: Literal["y"] = "y"
    p[Y]  # OK

    key = "x"  # Inferred type of key is `str`
    # Error: TypedDict key must be a string literal;
    #   expected one of ("x", "y")  [literal-required]

Check that overloaded functions have an implementation [no-overload-impl]

Overloaded functions outside of stub files must be followed by a non overloaded implementation.

from typing import overload

def func(value: int) -> int:

def func(value: str) -> str:

# presence of required function below is checked
def func(value):
    pass  # actual implementation

Check that coroutine return value is used [unused-coroutine]

Mypy ensures that return values of async def functions are not ignored, as this is usually a programming error, as the coroutine won’t be executed at the call site.

async def f() -> None:

async def g() -> None:
    f()  # Error: missing await
    await f()  # OK

You can work around this error by assigning the result to a temporary, otherwise unused variable:

_ = f()  # No error

Warn about top level await expressions [top-level-await]

This error code is separate from the general [syntax] errors, because in some environments (e.g. IPython) a top level await is allowed. In such environments a user may want to use --disable-error-code=top-level-await, that allows to still have errors for other improper uses of await, for example:

async def f() -> None:

top = await f()  # Error: "await" outside function  [top-level-await]

Warn about await expressions used outside of coroutines [await-not-async]

await must be used inside a coroutine.

async def f() -> None:

def g() -> None:
    await f()  # Error: "await" outside coroutine ("async def")  [await-not-async]

Check types in assert_type [assert-type]

The inferred type for an expression passed to assert_type must match the provided type.

from typing_extensions import assert_type

assert_type([1], list[int])  # OK

assert_type([1], list[str])  # Error

Check that function isn’t used in boolean context [truthy-function]

Functions will always evaluate to true in boolean contexts.

def f():

if f:  # Error: Function "Callable[[], Any]" could always be true in boolean context  [truthy-function]

Check that string formatting/interpolation is type-safe [str-format]

Mypy will check that f-strings, str.format() calls, and % interpolations are valid (when corresponding template is a literal string). This includes checking number and types of replacements, for example:

# Error: Cannot find replacement for positional format specifier 1 [str-format]
"{} and {}".format("spam")
"{} and {}".format("spam", "eggs")  # OK
# Error: Not all arguments converted during string formatting [str-format]
"{} and {}".format("spam", "eggs", "cheese")

# Error: Incompatible types in string interpolation
# (expression has type "float", placeholder has type "int") [str-format]

Check for implicit bytes coercions [str-bytes-safe]

Warn about cases where a bytes object may be converted to a string in an unexpected manner.

b = b"abc"

# Error: If x = b'abc' then f"{x}" or "{}".format(x) produces "b'abc'", not "abc".
# If this is desired behavior, use f"{x!r}" or "{!r}".format(x).
# Otherwise, decode the bytes [str-bytes-safe]
print(f"The alphabet starts with {b}")

# Okay
print(f"The alphabet starts with {b!r}")  # The alphabet starts with b'abc'
print(f"The alphabet starts with {b.decode('utf-8')}")  # The alphabet starts with abc

Check that overloaded functions don’t overlap [overload-overlap]

Warn if multiple @overload variants overlap in potentially unsafe ways. This guards against the following situation:

from typing import overload

class A: ...
class B(A): ...

def foo(x: B) -> int: ...  # Error: Overloaded function signatures 1 and 2 overlap with incompatible return types  [overload-overlap]
def foo(x: A) -> str: ...
def foo(x): ...

def takes_a(a: A) -> str:
    return foo(a)

a: A = B()
value = takes_a(a)
# mypy will think that value is a str, but it could actually be an int
reveal_type(value) # Revealed type is "builtins.str"

Note that in cases where you ignore this error, mypy will usually still infer the types you expect.

See overloading for more explanation.

Notify about an annotation in an unchecked function [annotation-unchecked]

Sometimes a user may accidentally omit an annotation for a function, and mypy will not check the body of this function (unless one uses --check-untyped-defs or --disallow-untyped-defs). To avoid such situations go unnoticed, mypy will show a note, if there are any type annotations in an unchecked function:

def test_assignment():  # "-> None" return annotation is missing
    # Note: By default the bodies of untyped functions are not checked,
    # consider using --check-untyped-defs [annotation-unchecked]
    x: int = "no way"

Note that mypy will still exit with return code 0, since such behaviour is specified by PEP 484.

Report syntax errors [syntax]

If the code being checked is not syntactically valid, mypy issues a syntax error. Most, but not all, syntax errors are blocking errors: they can’t be ignored with a # type: ignore comment.

Miscellaneous checks [misc]

Mypy performs numerous other, less commonly failing checks that don’t have specific error codes. These use the misc error code. Other than being used for multiple unrelated errors, the misc error code is not special. For example, you can ignore all errors in this category by using # type: ignore[misc] comment. Since these errors are not expected to be common, it’s unlikely that you’ll see two different errors with the misc code on a single line – though this can certainly happen once in a while.


Future mypy versions will likely add new error codes for some errors that currently use the misc error code.