Common issues and solutions#
This section has examples of cases when you need to update your code to use static typing, and ideas for working around issues if mypy doesn’t work as expected. Statically typed code is often identical to normal Python code (except for type annotations), but sometimes you need to do things slightly differently.
No errors reported for obviously wrong code#
There are several common reasons why obviously wrong code is not flagged as an error.
The function containing the error is not annotated.
do not have any annotations (neither for any argument nor for the
return type) are not type-checked, and even the most blatant type
2 + 'a') pass silently. The solution is to add
annotations. Where that isn’t possible, functions without annotations
can be checked using
def foo(a): return '(' + a.split() + ')' # No error!
This gives no error even though
a.split() is “obviously” a list
(the author probably meant
a.strip()). The error is reported
once you add annotations:
def foo(a: str) -> str: return '(' + a.split() + ')' # error: Unsupported operand types for + ("str" and List[str])
If you don’t know what types to add, you can use
Any, but beware:
One of the values involved has type ‘Any’.
Extending the above
example, if we were to leave out the annotation for
a, we’d get
def foo(a) -> str: return '(' + a.split() + ')' # No error!
The reason is that if the type of
a is unknown, the type of
a.split() is also unknown, so it is inferred as having type
Any, and it is no error to add a string to an
If you’re having trouble debugging such situations, reveal_type() might come in handy.
Note that sometimes library stubs with imprecise type information
can be a source of
__init__ method has no annotated
arguments and no return type annotation.
This is basically a combination of the two cases above, in that
without annotations can cause
Any types leak into instance variables:
class Bad: def __init__(self): self.value = "asdf" 1 + "asdf" # No error! bad = Bad() bad.value + 1 # No error! reveal_type(bad) # Revealed type is "__main__.Bad" reveal_type(bad.value) # Revealed type is "Any" class Good: def __init__(self) -> None: # Explicitly return None self.value = value
Some imports may be silently ignored.
A common source of unexpected
Any values is the
When you use
any imported module that cannot be found is silently replaced with
To help debug this, simply leave out
As mentioned in Missing imports, setting
on a per-module basis will make bad surprises less likely and is highly encouraged.
Use of the
--follow-imports=skip flags can also
cause problems. Use of these flags is strongly discouraged and only required in
relatively niche situations. See Following imports for more information.
mypy considers some of your code unreachable.
See Unreachable code for more information.
A function annotated as returning a non-optional type returns ‘None’ and mypy doesn’t complain.
def foo() -> str: return None # No error!
You may have disabled strict optional checking (see Disabling strict optional checking for more).
Spurious errors and locally silencing the checker#
You can use a
# type: ignore comment to silence the type checker
on a particular line. For example, let’s say our code is using
the C extension module
frobnicate, and there’s no stub available.
Mypy will complain about this, as it has no information about the
import frobnicate # Error: No module "frobnicate" frobnicate.start()
You can add a
# type: ignore comment to tell mypy to ignore this
import frobnicate # type: ignore frobnicate.start() # Okay!
The second line is now fine, since the ignore comment causes the name
frobnicate to get an implicit
You can use the form
# type: ignore[<code>] to only ignore
specific errors on the line. This way you are less likely to
silence unexpected errors that are not safe to ignore, and this
will also document what the purpose of the comment is. See
Error codes for more information.
# type: ignore comment will only assign the implicit
type if mypy cannot find information about that particular module. So,
if we did have a stub available for
frobnicate then mypy would
# type: ignore comment and typecheck the stub as usual.
Another option is to explicitly annotate values with type
mypy will let you perform arbitrary operations on
values. Sometimes there is no more precise type you can use for a
particular value, especially if you use dynamic Python features
class Wrapper: ... def __getattr__(self, a: str) -> Any: return getattr(self._wrapped, a)
Finally, you can create a stub file (
.pyi) for a file that
generates spurious errors. Mypy will only look at the stub file
and ignore the implementation, since stub files take precedence
Ignoring a whole file#
To only ignore errors, use a top-level
# mypy: ignore-errorscomment instead.
To only ignore errors with a specific error code, use a top-level
# mypy: disable-error-code="..."comment. Example:
# mypy: disable-error-code="truthy-bool, ignore-without-code"
To replace the contents of a module with
Any, use a per-module
follow_imports = skip. See Following imports for details.
Note that a
# type: ignore comment at the top of a module (before any statements,
including imports or docstrings) has the effect of ignoring the entire contents of the module.
This behaviour can be surprising and result in
“Module … has no attribute … [attr-defined]” errors.
Issues with code at runtime#
Idiomatic use of type annotations can sometimes run up against what a given version of Python considers legal code. These can result in some of the following errors when trying to run your code:
ImportErrorfrom circular imports
NameError: name "X" is not definedfrom forward references
TypeError: 'type' object is not subscriptablefrom types that are not generic at runtime
ModuleNotFoundErrorfrom use of stub definitions not available at runtime
TypeError: unsupported operand type(s) for |: 'type' and 'type'from use of new syntax
For dealing with these, see Annotation issues at runtime.
Mypy runs are slow#
If your mypy runs feel slow, you should probably use the mypy daemon, which can speed up incremental mypy runtimes by a factor of 10 or more. Remote caching can make cold mypy runs several times faster.
Types of empty collections#
You often need to specify the type when you assign an empty list or dict to a new variable, as mentioned earlier:
a: List[int] = 
Without the annotation mypy can’t always figure out the
precise type of
You can use a simple empty list literal in a dynamically typed function (as the
a would be implicitly
Any and need not be inferred), if type
of the variable has been declared or inferred before, or if you perform a simple
modification operation in the same scope (such as
append for a list):
a =  # Okay because followed by append, inferred type List[int] for i in range(n): a.append(i * i)
However, in more complex cases an explicit type annotation can be required (mypy will tell you this). Often the annotation can make your code easier to understand, so it doesn’t only help mypy but everybody who is reading the code!
Redefinitions with incompatible types#
Each name within a function only has a single ‘declared’ type. You can
reuse for loop indices etc., but if you want to use a variable with
multiple types within a single function, you may need to instead use
multiple variables (or maybe declare the variable with an
def f() -> None: n = 1 ... n = 'x' # error: Incompatible types in assignment (expression has type "str", variable has type "int")
flag can suppress this error in several cases.
Note that you can redefine a variable with a more precise or a more
concrete type. For example, you can redefine a sequence (which does
sort()) as a list and sort it in-place:
def f(x: Sequence[int]) -> None: # Type of x is Sequence[int] here; we don't know the concrete type. x = list(x) # Type of x is List[int] here. x.sort() # Okay!
See Type narrowing for more information.
Invariance vs covariance#
Most mutable generic collections are invariant, and mypy considers all user-defined generic classes invariant by default (see Variance of generic types for motivation). This could lead to some unexpected errors when combined with type inference. For example:
class A: ... class B(A): ... lst = [A(), A()] # Inferred type is List[A] new_lst = [B(), B()] # inferred type is List[B] lst = new_lst # mypy will complain about this, because List is invariant
Possible strategies in such situations are:
Use an explicit type annotation:
new_lst: List[A] = [B(), B()] lst = new_lst # OK
Make a copy of the right hand side:
lst = list(new_lst) # Also OK
Use immutable collections as annotations whenever possible:
def f_bad(x: List[A]) -> A: return x f_bad(new_lst) # Fails def f_good(x: Sequence[A]) -> A: return x f_good(new_lst) # OK
Declaring a supertype as variable type#
Sometimes the inferred type is a subtype (subclass) of the desired type. The type inference uses the first assignment to infer the type of a name:
class Shape: ... class Circle(Shape): ... class Triangle(Shape): ... shape = Circle() # mypy infers the type of shape to be Circle shape = Triangle() # error: Incompatible types in assignment (expression has type "Triangle", variable has type "Circle")
You can just give an explicit type for the variable in cases such the above example:
shape: Shape = Circle() # The variable s can be any Shape, not just Circle shape = Triangle() # OK
Complex type tests#
Mypy can usually infer the types correctly when using
type(obj) is some_class type tests,
and even user-defined type guards,
but for other kinds of checks you may need to add an
explicit type cast:
from typing import Sequence, cast def find_first_str(a: Sequence[object]) -> str: index = next((i for i, s in enumerate(a) if isinstance(s, str)), -1) if index < 0: raise ValueError('No str found') found = a[index] # Has type "object", despite the fact that we know it is "str" return cast(str, found) # We need an explicit cast to make mypy happy
Alternatively, you can use an
assert statement together with some
of the supported type inference techniques:
def find_first_str(a: Sequence[object]) -> str: index = next((i for i, s in enumerate(a) if isinstance(s, str)), -1) if index < 0: raise ValueError('No str found') found = a[index] # Has type "object", despite the fact that we know it is "str" assert isinstance(found, str) # Now, "found" will be narrowed to "str" return found # No need for the explicit "cast()" anymore
Note that the
object type used in the above example is similar
Object in Java: it only supports operations defined for all
objects, such as equality and
isinstance(). The type
in contrast, supports all operations, even if they may fail at
runtime. The cast above would have been unnecessary if the type of
You can read more about type narrowing techniques here.
Type inference in Mypy is designed to work well in common cases, to be predictable and to let the type checker give useful error messages. More powerful type inference strategies often have complex and difficult-to-predict failure modes and could result in very confusing error messages. The tradeoff is that you as a programmer sometimes have to give the type checker a little help.
Python version and system platform checks#
Mypy supports the ability to perform Python version checks and platform checks (e.g. Windows vs Posix), ignoring code paths that won’t be run on the targeted Python version or platform. This allows you to more effectively typecheck code that supports multiple versions of Python or multiple operating systems.
import sys # Distinguishing between different versions of Python: if sys.version_info >= (3, 8): # Python 3.8+ specific definitions and imports else: # Other definitions and imports # Distinguishing between different operating systems: if sys.platform.startswith("linux"): # Linux-specific code elif sys.platform == "darwin": # Mac-specific code elif sys.platform == "win32": # Windows-specific code else: # Other systems
As a special case, you can also use one of these checks in a top-level
assert; this makes mypy skip the rest of the file.
import sys assert sys.platform != 'win32' # The rest of this file doesn't apply to Windows.
Some other expressions exhibit similar behavior; in particular,
TYPE_CHECKING, variables named
MYPY, and any variable
whose name is passed to
False are not treated specially!)
Mypy currently does not support more complex checks, and does not assign
any special meaning when assigning a
check to a variable. This may change in future versions of mypy.
To target a different Python version, use the
--python-version X.Y flag.
For example, to verify your code typechecks if were run using Python 3.8, pass
--python-version 3.8 from the command line. Note that you do not need
to have Python 3.8 installed to perform this check.
To target a different operating system, use the
--platform PLATFORM flag.
For example, to verify your code typechecks if it were run in Windows, pass
--platform win32. See the documentation for
for examples of valid platform parameters.
Displaying the type of an expression#
You can use
reveal_type(expr) to ask mypy to display the inferred
static type of an expression. This can be useful when you don’t quite
understand how mypy handles a particular piece of code. Example:
reveal_type((1, 'hello')) # Revealed type is "Tuple[builtins.int, builtins.str]"
You can also use
reveal_locals() at any line in a file
to see the types of all local variables at once. Example:
a = 1 b = 'one' reveal_locals() # Revealed local types are: # a: builtins.int # b: builtins.str
reveal_locals are only understood by mypy and
don’t exist in Python. If you try to run your program, you’ll have to
reveal_locals calls before you can
run your code. Both are always available and you don’t need to import
In some cases, linters will complain about unused imports or code. In these cases, you can silence them with a comment after type comments, or on the same line as the import:
# to silence complaints about unused imports from typing import List # noqa a = None # type: List[int]
To silence the linter on the same line as a type comment put the linter comment after the type comment:
a = some_complex_thing() # type: ignore # noqa
Covariant subtyping of mutable protocol members is rejected#
Mypy rejects this because this is potentially unsafe. Consider this example:
from typing_extensions import Protocol class P(Protocol): x: float def fun(arg: P) -> None: arg.x = 3.14 class C: x = 42 c = C() fun(c) # This is not safe c.x << 5 # Since this will fail!
To work around this problem consider whether “mutating” is actually part
of a protocol. If not, then one can use a
the protocol definition:
from typing_extensions import Protocol class P(Protocol): @property def x(self) -> float: pass def fun(arg: P) -> None: ... class C: x = 42 fun(C()) # OK
Dealing with conflicting names#
Suppose you have a class with a method whose name is the same as an imported (or built-in) type, and you want to use the type in another method signature. E.g.:
class Message: def bytes(self): ... def register(self, path: bytes): # error: Invalid type "mod.Message.bytes" ...
The third line elicits an error because mypy sees the argument type
bytes as a reference to the method by that name. Other than
renaming the method, a workaround is to use an alias:
bytes_ = bytes class Message: def bytes(self): ... def register(self, path: bytes_): ...
Using a development mypy build#
You can install the latest development version of mypy from source. Clone the
mypy repository on GitHub, and then run
pip install locally:
git clone https://github.com/python/mypy.git cd mypy python3 -m pip install --upgrade .
To install a development version of mypy that is mypyc-compiled, see the instructions at the mypyc wheels repo.
Variables vs type aliases#
Mypy has both type aliases and variables with types like
Type[...]. These are
subtly different, and it’s important to understand how they differ to avoid pitfalls.
A variable with type
Type[...]is defined using an assignment with an explicit type annotation:
class A: ... tp: Type[A] = A
You can define a type alias using an assignment without an explicit type annotation at the top level of a module:
class A: ... Alias = A
You can also use
TypeAlias(PEP 613) to define an explicit type alias:
from typing import TypeAlias # "from typing_extensions" in Python 3.9 and earlier class A: ... Alias: TypeAlias = A
You should always use
TypeAliasto define a type alias in a class body or inside a function.
The main difference is that the target of an alias is precisely known statically, and this means that they can be used in type annotations and other type contexts. Type aliases can’t be defined conditionally (unless using supported Python version and platform checks):
class A: ... class B: ... if random() > 0.5: Alias = A else: # error: Cannot assign multiple types to name "Alias" without an # explicit "Type[...]" annotation Alias = B tp: Type[object] # "tp" is a variable with a type object value if random() > 0.5: tp = A else: tp = B # This is OK def fun1(x: Alias) -> None: ... # OK def fun2(x: tp) -> None: ... # Error: "tp" is not valid as a type
It’s unsafe to override a method with a more specific argument type, as it violates the Liskov substitution principle. For return types, it’s unsafe to override a method with a more general return type.
Other incompatible signature changes in method overrides, such as adding an extra required parameter, or removing an optional parameter, will also generate errors. The signature of a method in a subclass should accept all valid calls to the base class method. Mypy treats a subclass as a subtype of the base class. An instance of a subclass is valid everywhere where an instance of the base class is valid.
This example demonstrates both safe and unsafe overrides:
from typing import Sequence, List, Iterable class A: def test(self, t: Sequence[int]) -> Sequence[str]: ... class GeneralizedArgument(A): # A more general argument type is okay def test(self, t: Iterable[int]) -> Sequence[str]: # OK ... class NarrowerArgument(A): # A more specific argument type isn't accepted def test(self, t: List[int]) -> Sequence[str]: # Error ... class NarrowerReturn(A): # A more specific return type is fine def test(self, t: Sequence[int]) -> List[str]: # OK ... class GeneralizedReturn(A): # A more general return type is an error def test(self, t: Sequence[int]) -> Iterable[str]: # Error ...
You can use
# type: ignore[override] to silence the error. Add it
to the line that generates the error, if you decide that type safety is
class NarrowerArgument(A): def test(self, t: List[int]) -> Sequence[str]: # type: ignore[override] ...
Mypy may consider some code as unreachable, even if it might not be immediately obvious why. It’s important to note that mypy will not type check such code. Consider this example:
class Foo: bar: str = '' def bar() -> None: foo: Foo = Foo() return x: int = 'abc' # Unreachable -- no error
It’s easy to see that any statement after
return is unreachable,
and hence mypy will not complain about the mis-typed code below
it. For a more subtle example, consider this code:
class Foo: bar: str = '' def bar() -> None: foo: Foo = Foo() assert foo.bar is None x: int = 'abc' # Unreachable -- no error
Again, mypy will not report any errors. The type of
str, and mypy reasons that it can never be
None. Hence the
assert statement will always fail and the statement below will
never be executed. (Note that in Python,
None is not an empty
reference but an object of type
In this example mypy will go on to check the last line and report an error, since mypy thinks that the condition could be either True or False:
class Foo: bar: str = '' def bar() -> None: foo: Foo = Foo() if not foo.bar: return x: int = 'abc' # Reachable -- error
If you use the
--warn-unreachable flag, mypy will generate
an error about each unreachable code block.
Narrowing and inner functions#
Because closures in Python are late-binding (https://docs.python-guide.org/writing/gotchas/#late-binding-closures), mypy will not narrow the type of a captured variable in an inner function. This is best understood via an example:
def foo(x: Optional[int]) -> Callable[, int]: if x is None: x = 5 print(x + 1) # mypy correctly deduces x must be an int here def inner() -> int: return x + 1 # but (correctly) complains about this line x = None # because x could later be assigned None return inner inner = foo(5) inner() # this will raise an error when called
To get this code to type check, you could assign
y = x after
x has been
narrowed, and use
y in the inner function, or add an assert in the inner