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Rules

byte-string-type-annotation

Default level: error · Related issues · View source

What it does

Checks for byte-strings in type annotation positions.

Why is this bad?

Static analysis tools like ty can't analyse type annotations that use byte-string notation.

Examples

def test(): -> b"int":
    ...

Use instead: 

def test(): -> "int":
    ...

call-non-callable

Default level: error · Related issues · View source

What it does

Checks for calls to non-callable objects.

Why is this bad?

Calling a non-callable object will raise a TypeError at runtime.

Examples

4()  # TypeError: 'int' object is not callable

conflicting-argument-forms

Default level: error · Related issues · View source

What it does

Checks whether an argument is used as both a value and a type form in a call.

Why is this bad?

Such calls have confusing semantics and often indicate a logic error.

Examples

from typing import reveal_type
from ty_extensions import is_singleton

if flag:
    f = repr  # Expects a value
else:
    f = is_singleton  # Expects a type form

f(int)  # error

conflicting-declarations

Default level: error · Related issues · View source

What it does

Checks whether a variable has been declared as two conflicting types.

Why is this bad

A variable with two conflicting declarations likely indicates a mistake. Moreover, it could lead to incorrect or ill-defined type inference for other code that relies on these variables.

Examples

if b:
    a: int
else:
    a: str

a = 1

conflicting-metaclass

Default level: error · Related issues · View source

What it does

Checks for class definitions where the metaclass of the class being created would not be a subclass of the metaclasses of all the class's bases.

Why is it bad?

Such a class definition raises a TypeError at runtime.

Examples

class M1(type): ...
class M2(type): ...
class A(metaclass=M1): ...
class B(metaclass=M2): ...

# TypeError: metaclass conflict
class C(A, B): ...

cyclic-class-definition

Default level: error · Related issues · View source

What it does

Checks for class definitions in stub files that inherit (directly or indirectly) from themselves.

Why is it bad?

Although forward references are natively supported in stub files, inheritance cycles are still disallowed, as it is impossible to resolve a consistent method resolution order for a class that inherits from itself.

Examples

# foo.pyi
class A(B): ...
class B(A): ...

duplicate-base

Default level: error · Related issues · View source

What it does

Checks for class definitions with duplicate bases.

Why is this bad?

Class definitions with duplicate bases raise TypeError at runtime.

Examples

class A: ...

# TypeError: duplicate base class
class B(A, A): ...

duplicate-kw-only

Default level: error · Related issues · View source

What it does

Checks for dataclass definitions with more than one field annotated with KW_ONLY.

Why is this bad?

dataclasses.KW_ONLY is a special marker used to emulate the * syntax in normal signatures. It can only be used once per dataclass.

Attempting to annotate two different fields with it will lead to a runtime error.

Examples

from dataclasses import dataclass, KW_ONLY

@dataclass
class A:  # Crash at runtime
    b: int
    _1: KW_ONLY
    c: str
    _2: KW_ONLY
    d: bytes

escape-character-in-forward-annotation

Default level: error · Related issues · View source

TODO #14889

fstring-type-annotation

Default level: error · Related issues · View source

What it does

Checks for f-strings in type annotation positions.

Why is this bad?

Static analysis tools like ty can't analyse type annotations that use f-string notation.

Examples

def test(): -> f"int":
    ...

Use instead: 

def test(): -> "int":
    ...

implicit-concatenated-string-type-annotation

Default level: error · Related issues · View source

What it does

Checks for implicit concatenated strings in type annotation positions.

Why is this bad?

Static analysis tools like ty can't analyse type annotations that use implicit concatenated strings.

Examples

def test(): -> "Literal[" "5" "]":
    ...

Use instead: 

def test(): -> "Literal[5]":
    ...

inconsistent-mro

Default level: error · Related issues · View source

What it does

Checks for classes with an inconsistent method resolution order (MRO).

Why is this bad?

Classes with an inconsistent MRO will raise a TypeError at runtime.

Examples

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

# TypeError: Cannot create a consistent method resolution order
class C(A, B): ...

index-out-of-bounds

Default level: error · Related issues · View source

What it does

Checks for attempts to use an out of bounds index to get an item from a container.

Why is this bad?

Using an out of bounds index will raise an IndexError at runtime.

Examples

t = (0, 1, 2)
t[3]  # IndexError: tuple index out of range

instance-layout-conflict

Default level: error · Related issues · View source

What it does

Checks for classes definitions which will fail at runtime due to "instance memory layout conflicts".

This error is usually caused by attempting to combine multiple classes that define non-empty __slots__ in a class's Method Resolution Order (MRO), or by attempting to combine multiple builtin classes in a class's MRO.

Why is this bad?

Inheriting from bases with conflicting instance memory layouts will lead to a TypeError at runtime.

An instance memory layout conflict occurs when CPython cannot determine the memory layout instances of a class should have, because the instance memory layout of one of its bases conflicts with the instance memory layout of one or more of its other bases.

For example, if a Python class defines non-empty __slots__, this will impact the memory layout of instances of that class. Multiple inheritance from more than one different class defining non-empty __slots__ is not allowed:

class A:
    __slots__ = ("a", "b")

class B:
    __slots__ = ("a", "b")  # Even if the values are the same

# TypeError: multiple bases have instance lay-out conflict
class C(A, B): ...

An instance layout conflict can also be caused by attempting to use multiple inheritance with two builtin classes, due to the way that these classes are implemented in a CPython C extension:

class A(int, float): ...  # TypeError: multiple bases have instance lay-out conflict

Note that pure-Python classes with no __slots__, or pure-Python classes with empty __slots__, are always compatible:

class A: ...
class B:
    __slots__ = ()
class C:
    __slots__ = ("a", "b")

# fine
class D(A, B, C): ...

Known problems

Classes that have "dynamic" definitions of __slots__ (definitions do not consist of string literals, or tuples of string literals) are not currently considered solid bases by ty.

Additionally, this check is not exhaustive: many C extensions (including several in the standard library) define classes that use extended memory layouts and thus cannot coexist in a single MRO. Since it is currently not possible to represent this fact in stub files, having a full knowledge of these classes is also impossible. When it comes to classes that do not define __slots__ at the Python level, therefore, ty, currently only hard-codes a number of cases where it knows that a class will produce instances with an atypical memory layout.

Further reading

invalid-argument-type

Default level: error · Related issues · View source

What it does

Detects call arguments whose type is not assignable to the corresponding typed parameter.

Why is this bad?

Passing an argument of a type the function (or callable object) does not accept violates the expectations of the function author and may cause unexpected runtime errors within the body of the function.

Examples

def func(x: int): ...
func("foo")  # error: [invalid-argument-type]

invalid-assignment

Default level: error · Related issues · View source

What it does

Checks for assignments where the type of the value is not assignable to the type of the assignee.

Why is this bad?

Such assignments break the rules of the type system and weaken a type checker's ability to accurately reason about your code.

Examples

a: int = ''

invalid-attribute-access

Default level: error · Related issues · View source

What it does

Checks for assignments to class variables from instances and assignments to instance variables from its class.

Why is this bad?

Incorrect assignments break the rules of the type system and weaken a type checker's ability to accurately reason about your code.

Examples

class C:
    class_var: ClassVar[int] = 1
    instance_var: int

C.class_var = 3  # okay
C().class_var = 3  # error: Cannot assign to class variable

C().instance_var = 3  # okay
C.instance_var = 3  # error: Cannot assign to instance variable

invalid-base

Default level: error · Related issues · View source

What it does

Checks for class definitions that have bases which are not instances of type.

Why is this bad?

Class definitions with bases like this will lead to TypeError being raised at runtime.

Examples

class A(42): ...  # error: [invalid-base]

invalid-context-manager

Default level: error · Related issues · View source

What it does

Checks for expressions used in with statements that do not implement the context manager protocol.

Why is this bad?

Such a statement will raise TypeError at runtime.

Examples

# TypeError: 'int' object does not support the context manager protocol
with 1:
    print(2)

invalid-declaration

Default level: error · Related issues · View source

What it does

Checks for declarations where the inferred type of an existing symbol is not assignable to its post-hoc declared type.

Why is this bad?

Such declarations break the rules of the type system and weaken a type checker's ability to accurately reason about your code.

Examples

a = 1
a: str

invalid-exception-caught

Default level: error · Related issues · View source

What it does

Checks for exception handlers that catch non-exception classes.

Why is this bad?

Catching classes that do not inherit from BaseException will raise a TypeError at runtime.

Example

try:
    1 / 0
except 1:
    ...

Use instead: 

try:
    1 / 0
except ZeroDivisionError:
    ...

References

Ruff rule

This rule corresponds to Ruff's except-with-non-exception-classes (B030)

invalid-generic-class

Default level: error · Related issues · View source

What it does

Checks for the creation of invalid generic classes

Why is this bad?

There are several requirements that you must follow when defining a generic class.

Examples

from typing import Generic, TypeVar

T = TypeVar("T")  # okay

# error: class uses both PEP-695 syntax and legacy syntax
class C[U](Generic[T]): ...

References

invalid-legacy-type-variable

Default level: error · Related issues · View source

What it does

Checks for the creation of invalid legacy TypeVars

Why is this bad?

There are several requirements that you must follow when creating a legacy TypeVar.

Examples

from typing import TypeVar

T = TypeVar("T")  # okay
Q = TypeVar("S")  # error: TypeVar name must match the variable it's assigned to
T = TypeVar("T")  # error: TypeVars should not be redefined

# error: TypeVar must be immediately assigned to a variable
def f(t: TypeVar("U")): ...

References

invalid-metaclass

Default level: error · Related issues · View source

What it does

Checks for arguments to metaclass= that are invalid.

Why is this bad?

Python allows arbitrary expressions to be used as the argument to metaclass=. These expressions, however, need to be callable and accept the same arguments as type.__new__.

Example

def f(): ...

# TypeError: f() takes 0 positional arguments but 3 were given
class B(metaclass=f): ...

References

invalid-overload

Default level: error · Related issues · View source

What it does

Checks for various invalid @overload usages.

Why is this bad?

The @overload decorator is used to define functions and methods that accepts different combinations of arguments and return different types based on the arguments passed. This is mainly beneficial for type checkers. But, if the @overload usage is invalid, the type checker may not be able to provide correct type information.

Example

Defining only one overload:

from typing import overload

@overload
def foo(x: int) -> int: ...
def foo(x: int | None) -> int | None:
    return x

Or, not providing an implementation for the overloaded definition:

from typing import overload

@overload
def foo() -> None: ...
@overload
def foo(x: int) -> int: ...

References

invalid-parameter-default

Default level: error · Related issues · View source

What it does

Checks for default values that can't be assigned to the parameter's annotated type.

Why is this bad?

This breaks the rules of the type system and weakens a type checker's ability to accurately reason about your code.

Examples

def f(a: int = ''): ...

invalid-protocol

Default level: error · Related issues · View source

What it does

Checks for invalidly defined protocol classes.

Why is this bad?

An invalidly defined protocol class may lead to the type checker inferring unexpected things. It may also lead to TypeErrors at runtime.

Examples

A Protocol class cannot inherit from a non-Protocol class; this raises a TypeError at runtime:

>>> from typing import Protocol
>>> class Foo(int, Protocol): ...
...
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    class Foo(int, Protocol): ...
TypeError: Protocols can only inherit from other protocols, got <class 'int'>

invalid-raise

Default level: error · Related issues · View source

Checks for raise statements that raise non-exceptions or use invalid causes for their raised exceptions.

Why is this bad?

Only subclasses or instances of BaseException can be raised. For an exception's cause, the same rules apply, except that None is also permitted. Violating these rules results in a TypeError at runtime.

Examples

def f():
    try:
        something()
    except NameError:
        raise "oops!" from f

def g():
    raise NotImplemented from 42

Use instead: 

def f():
    try:
        something()
    except NameError as e:
        raise RuntimeError("oops!") from e

def g():
    raise NotImplementedError from None

References

invalid-return-type

Default level: error · Related issues · View source

What it does

Detects returned values that can't be assigned to the function's annotated return type.

Why is this bad?

Returning an object of a type incompatible with the annotated return type may cause confusion to the user calling the function.

Examples

def func() -> int:
    return "a"  # error: [invalid-return-type]

invalid-super-argument

Default level: error · Related issues · View source

What it does

Detects super() calls where: - the first argument is not a valid class literal, or - the second argument is not an instance or subclass of the first argument.

Why is this bad?

super(type, obj) expects: - the first argument to be a class, - and the second argument to satisfy one of the following: - isinstance(obj, type) is True - issubclass(obj, type) is True

Violating this relationship will raise a TypeError at runtime.

Examples

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

super(A, B())  # it's okay! `A` satisfies `isinstance(B(), A)`

super(A(), B()) # error: `A()` is not a class

super(B, A())  # error: `A()` does not satisfy `isinstance(A(), B)`
super(B, A)  # error: `A` does not satisfy `issubclass(A, B)`

References

invalid-syntax-in-forward-annotation

Default level: error · Related issues · View source

TODO #14889

invalid-type-alias-type

Default level: error · Related issues · View source

What it does

Checks for the creation of invalid TypeAliasTypes

Why is this bad?

There are several requirements that you must follow when creating a TypeAliasType.

Examples

from typing import TypeAliasType

IntOrStr = TypeAliasType("IntOrStr", int | str)  # okay
NewAlias = TypeAliasType(get_name(), int)        # error: TypeAliasType name must be a string literal

invalid-type-checking-constant

Default level: error · Related issues · View source

What it does

Checks for a value other than False assigned to the TYPE_CHECKING variable, or an annotation not assignable from bool.

Why is this bad?

The name TYPE_CHECKING is reserved for a flag that can be used to provide conditional code seen only by the type checker, and not at runtime. Normally this flag is imported from typing or typing_extensions, but it can also be defined locally. If defined locally, it must be assigned the value False at runtime; the type checker will consider its value to be True. If annotated, it must be annotated as a type that can accept bool values.

Examples

TYPE_CHECKING: str
TYPE_CHECKING = ''

invalid-type-form

Default level: error · Related issues · View source

What it does

Checks for expressions that are used as type expressions but cannot validly be interpreted as such.

Why is this bad?

Such expressions cannot be understood by ty. In some cases, they might raise errors at runtime.

Examples

from typing import Annotated

a: type[1]  # `1` is not a type
b: Annotated[int]  # `Annotated` expects at least two arguments

invalid-type-guard-call

Default level: error · Related issues · View source

What it does

Checks for type guard function calls without a valid target.

Why is this bad?

The first non-keyword non-variadic argument to a type guard function is its target and must map to a symbol.

Starred (is_str(*a)), literal (is_str(42)) and other non-symbol-like expressions are invalid as narrowing targets.

Examples

from typing import TypeIs

def f(v: object) -> TypeIs[int]: ...

f()  # Error
f(*a)  # Error
f(10)  # Error

invalid-type-guard-definition

Default level: error · Related issues · View source

What it does

Checks for type guard functions without a first non-self-like non-keyword-only non-variadic parameter.

Why is this bad?

Type narrowing functions must accept at least one positional argument (non-static methods must accept another in addition to self/cls).

Extra parameters/arguments are allowed but do not affect narrowing.

Examples

from typing import TypeIs

def f() -> TypeIs[int]: ...  # Error, no parameter
def f(*, v: object) -> TypeIs[int]: ...  # Error, no positional arguments allowed
def f(*args: object) -> TypeIs[int]: ... # Error, expect variadic arguments
class C:
    def f(self) -> TypeIs[int]: ...  # Error, only positional argument expected is `self`

invalid-type-variable-constraints

Default level: error · Related issues · View source

What it does

Checks for constrained type variables with only one constraint.

Why is this bad?

A constrained type variable must have at least two constraints.

Examples

from typing import TypeVar

T = TypeVar('T', str)  # invalid constrained TypeVar

Use instead: 

T = TypeVar('T', str, int)  # valid constrained TypeVar
# or
T = TypeVar('T', bound=str)  # valid bound TypeVar

missing-argument

Default level: error · Related issues · View source

What it does

Checks for missing required arguments in a call.

Why is this bad?

Failing to provide a required argument will raise a TypeError at runtime.

Examples

def func(x: int): ...
func()  # TypeError: func() missing 1 required positional argument: 'x'

no-matching-overload

Default level: error · Related issues · View source

What it does

Checks for calls to an overloaded function that do not match any of the overloads.

Why is this bad?

Failing to provide the correct arguments to one of the overloads will raise a TypeError at runtime.

Examples

@overload
def func(x: int): ...
@overload
def func(x: bool): ...
func("string")  # error: [no-matching-overload]

non-subscriptable

Default level: error · Related issues · View source

What it does

Checks for subscripting objects that do not support subscripting.

Why is this bad?

Subscripting an object that does not support it will raise a TypeError at runtime.

Examples

4[1]  # TypeError: 'int' object is not subscriptable

not-iterable

Default level: error · Related issues · View source

What it does

Checks for objects that are not iterable but are used in a context that requires them to be.

Why is this bad?

Iterating over an object that is not iterable will raise a TypeError at runtime.

Examples

for i in 34:  # TypeError: 'int' object is not iterable
    pass

parameter-already-assigned

Default level: error · Related issues · View source

What it does

Checks for calls which provide more than one argument for a single parameter.

Why is this bad?

Providing multiple values for a single parameter will raise a TypeError at runtime.

Examples

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

f(1, x=2)  # Error raised here

raw-string-type-annotation

Default level: error · Related issues · View source

What it does

Checks for raw-strings in type annotation positions.

Why is this bad?

Static analysis tools like ty can't analyse type annotations that use raw-string notation.

Examples

def test(): -> r"int":
    ...

Use instead: 

def test(): -> "int":
    ...

static-assert-error

Default level: error · Related issues · View source

What it does

Makes sure that the argument of static_assert is statically known to be true.

Why is this bad?

A static_assert call represents an explicit request from the user for the type checker to emit an error if the argument cannot be verified to evaluate to True in a boolean context.

Examples

from ty_extensions import static_assert

static_assert(1 + 1 == 3)  # error: evaluates to `False`

static_assert(int(2.0 * 3.0) == 6)  # error: does not have a statically known truthiness

subclass-of-final-class

Default level: error · Related issues · View source

What it does

Checks for classes that subclass final classes.

Why is this bad?

Decorating a class with @final declares to the type checker that it should not be subclassed.

Example

from typing import final

@final
class A: ...
class B(A): ...  # Error raised here

too-many-positional-arguments

Default level: error · Related issues · View source

What it does

Checks for calls that pass more positional arguments than the callable can accept.

Why is this bad?

Passing too many positional arguments will raise TypeError at runtime.

Example

def f(): ...

f("foo")  # Error raised here

type-assertion-failure

Default level: error · Related issues · View source

What it does

Checks for assert_type() and assert_never() calls where the actual type is not the same as the asserted type.

Why is this bad?

assert_type() allows confirming the inferred type of a certain value.

Example

def _(x: int):
    assert_type(x, int)  # fine
    assert_type(x, str)  # error: Actual type does not match asserted type

unavailable-implicit-super-arguments

Default level: error · Related issues · View source

What it does

Detects invalid super() calls where implicit arguments like the enclosing class or first method argument are unavailable.

Why is this bad?

When super() is used without arguments, Python tries to find two things: the nearest enclosing class and the first argument of the immediately enclosing function (typically self or cls). If either of these is missing, the call will fail at runtime with a RuntimeError.

Examples

super()  # error: no enclosing class or function found

def func():
    super()  # error: no enclosing class or first argument exists

class A:
    f = super()  # error: no enclosing function to provide the first argument

    def method(self):
        def nested():
            super()  # error: first argument does not exist in this nested function

        lambda: super()  # error: first argument does not exist in this lambda

        (super() for _ in range(10))  # error: argument is not available in generator expression

        super()  # okay! both enclosing class and first argument are available

References

unknown-argument

Default level: error · Related issues · View source

What it does

Checks for keyword arguments in calls that don't match any parameter of the callable.

Why is this bad?

Providing an unknown keyword argument will raise TypeError at runtime.

Example

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

f(x=1, y=2)  # Error raised here

unresolved-attribute

Default level: error · Related issues · View source

What it does

Checks for unresolved attributes.

Why is this bad?

Accessing an unbound attribute will raise an AttributeError at runtime. An unresolved attribute is not guaranteed to exist from the type alone, so this could also indicate that the object is not of the type that the user expects.

Examples

class A: ...

A().foo  # AttributeError: 'A' object has no attribute 'foo'

unresolved-import

Default level: error · Related issues · View source

What it does

Checks for import statements for which the module cannot be resolved.

Why is this bad?

Importing a module that cannot be resolved will raise a ModuleNotFoundError at runtime.

Examples

import foo  # ModuleNotFoundError: No module named 'foo'

unresolved-reference

Default level: error · Related issues · View source

What it does

Checks for references to names that are not defined.

Why is this bad?

Using an undefined variable will raise a NameError at runtime.

Example

print(x)  # NameError: name 'x' is not defined

unsupported-bool-conversion

Default level: error · Related issues · View source

What it does

Checks for bool conversions where the object doesn't correctly implement __bool__.

Why is this bad?

If an exception is raised when you attempt to evaluate the truthiness of an object, using the object in a boolean context will fail at runtime.

Examples

class NotBoolable:
    __bool__ = None

b1 = NotBoolable()
b2 = NotBoolable()

if b1:  # exception raised here
    pass

b1 and b2  # exception raised here
not b1  # exception raised here
b1 < b2 < b1  # exception raised here

unsupported-operator

Default level: error · Related issues · View source

What it does

Checks for binary expressions, comparisons, and unary expressions where the operands don't support the operator.

Why is this bad?

Attempting to use an unsupported operator will raise a TypeError at runtime.

Examples

class A: ...

A() + A()  # TypeError: unsupported operand type(s) for +: 'A' and 'A'

zero-stepsize-in-slice

Default level: error · Related issues · View source

What it does

Checks for step size 0 in slices.

Why is this bad?

A slice with a step size of zero will raise a ValueError at runtime.

Examples

l = list(range(10))
l[1:10:0]  # ValueError: slice step cannot be zero

invalid-ignore-comment

Default level: warn · Related issues · View source

What it does

Checks for type: ignore and ty: ignore comments that are syntactically incorrect.

Why is this bad?

A syntactically incorrect ignore comment is probably a mistake and is useless.

Examples

a = 20 / 0  # type: ignoree

Use instead:

a = 20 / 0  # type: ignore

possibly-unbound-attribute

Default level: warn · Related issues · View source

What it does

Checks for possibly unbound attributes.

Why is this bad?

Attempting to access an unbound attribute will raise an AttributeError at runtime.

Examples

class A:
    if b:
        c = 0

A.c  # AttributeError: type object 'A' has no attribute 'c'

possibly-unbound-implicit-call

Default level: warn · Related issues · View source

What it does

Checks for implicit calls to possibly unbound methods.

Why is this bad?

Expressions such as x[y] and x * y call methods under the hood (__getitem__ and __mul__ respectively). Calling an unbound method will raise an AttributeError at runtime.

Examples

import datetime

class A:
    if datetime.date.today().weekday() != 6:
        def __getitem__(self, v): ...

A()[0]  # TypeError: 'A' object is not subscriptable

possibly-unbound-import

Default level: warn · Related issues · View source

What it does

Checks for imports of symbols that may be unbound.

Why is this bad?

Importing an unbound module or name will raise a ModuleNotFoundError or ImportError at runtime.

Examples

# module.py
import datetime

if datetime.date.today().weekday() != 6:
    a = 1

# main.py
from module import a  # ImportError: cannot import name 'a' from 'module'

redundant-cast

Default level: warn · Related issues · View source

What it does

Detects redundant cast calls where the value already has the target type.

Why is this bad?

These casts have no effect and can be removed.

Example

def f() -> int:
    return 10

cast(int, f())  # Redundant

undefined-reveal

Default level: warn · Related issues · View source

What it does

Checks for calls to reveal_type without importing it.

Why is this bad?

Using reveal_type without importing it will raise a NameError at runtime.

Examples

reveal_type(1)  # NameError: name 'reveal_type' is not defined

unknown-rule

Default level: warn · Related issues · View source

What it does

Checks for ty: ignore[code] where code isn't a known lint rule.

Why is this bad?

A ty: ignore[code] directive with a code that doesn't match any known rule will not suppress any type errors, and is probably a mistake.

Examples

a = 20 / 0  # ty: ignore[division-by-zer]

Use instead:

a = 20 / 0  # ty: ignore[division-by-zero]

unsupported-base

Default level: warn · Related issues · View source

What it does

Checks for class definitions that have bases which are unsupported by ty.

Why is this bad?

If a class has a base that is an instance of a complex type such as a union type, ty will not be able to resolve the method resolution order (MRO) for the class. This will lead to an inferior understanding of your codebase and unpredictable type-checking behavior.

Examples

import datetime

class A: ...
class B: ...

if datetime.date.today().weekday() != 6:
    C = A
else:
    C = B

class D(C): ...  # error: [unsupported-base]

division-by-zero

Default level: ignore · Related issues · View source

What it does

It detects division by zero.

Why is this bad?

Dividing by zero raises a ZeroDivisionError at runtime.

Examples

5 / 0

possibly-unresolved-reference

Default level: ignore · Related issues · View source

What it does

Checks for references to names that are possibly not defined.

Why is this bad?

Using an undefined variable will raise a NameError at runtime.

Example

for i in range(0):
    x = i

print(x)  # NameError: name 'x' is not defined

unused-ignore-comment

Default level: ignore · Related issues · View source

What it does

Checks for type: ignore or ty: ignore directives that are no longer applicable.

Why is this bad?

A type: ignore directive that no longer matches any diagnostic violations is likely included by mistake, and should be removed to avoid confusion.

Examples

a = 20 / 2  # ty: ignore[division-by-zero]

Use instead:

a = 20 / 2