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.

Can’t install mypy using pip#

If installation fails, you’ve probably hit one of these issues:

Mypy needs Python 3.6 or later to run.
You may have to run pip like this: python3 -m pip install mypy.

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. Functions that do not have any annotations (neither for any argument nor for the return type) are not type-checked, and even the most blatant type errors (e.g. 2 + 'a') pass silently. The solution is to add annotations. Where that isn’t possible, functions without annotations can be checked using --check-untyped-defs.

Example:

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 no error:

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 Any.

If you’re having trouble debugging such situations, reveal_type() might come in handy.

Note that sometimes library stubs have imprecise type information, e.g. the pow() builtin returns Any (see typeshed issue 285 for the reason).

__init__ method has no annotated arguments or return type annotation. __init__ is considered fully-annotated if at least one argument is annotated, while mypy will infer the return type as None. The implication is that, for a __init__ method that has no argument, you’ll have to explicitly annotate the return type as None to type-check this __init__ method:

def foo(s: str) -> str:
    return s

class A():
    def __init__(self, value: str): # Return type inferred as None, considered as typed method
        self.value = value
        foo(1) # error: Argument 1 to "foo" has incompatible type "int"; expected "str"

class B():
    def __init__(self):  # No argument is annotated, considered as untyped method
        foo(1)  # No error!

class C():
    def __init__(self) -> None:  # Must specify return type to type-check
        foo(1) # error: Argument 1 to "foo" has incompatible type "int"; expected "str"

Some imports may be silently ignored. Another source of unexpected Any values are the --ignore-missing-imports and --follow-imports=skip flags. When you use --ignore-missing-imports, any imported module that cannot be found is silently replaced with Any. When using --follow-imports=skip the same is true for modules for which a .py file is found but that are not specified on the command line. (If a .pyi stub is found it is always processed normally, regardless of the value of --follow-imports.) To help debug the former situation (no module found at all) leave out --ignore-missing-imports; to get clarity about the latter use --follow-imports=error. You can read up about these and other useful flags in The mypy command line.

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 module:

import frobnicate  # Error: No module "frobnicate"
frobnicate.start()

You can add a # type: ignore comment to tell mypy to ignore this error:

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 Any type.

Note

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.

Note

The # type: ignore comment will only assign the implicit Any type if mypy cannot find information about that particular module. So, if we did have a stub available for frobnicate then mypy would ignore the # type: ignore comment and typecheck the stub as usual.

Another option is to explicitly annotate values with type Any – mypy will let you perform arbitrary operations on Any values. Sometimes there is no more precise type you can use for a particular value, especially if you use dynamic Python features such as __getattr__:

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 over .py files.

Ignoring a whole file#

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.

To only ignore errors, use a top-level # mypy: ignore-errors comment instead. To only ignore errors with a specific error code, use a top-level # mypy: disable-error-code=... comment. To replace the contents of the module with Any, use a per-module follow_imports = skip. See Following imports for details.

# type: ignore

import foo

foo.bar()

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:

ImportError from circular imports
NameError: name "X" is not defined from forward references
TypeError: 'type' object is not subscriptable from types that are not generic at runtime
ImportError or ModuleNotFoundError from 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 a.

You can use a simple empty list literal in a dynamically typed function (as the type of 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 declare it with the Any type.

def f() -> None:
    n = 1
    ...
    n = 'x'        # Type error: n has type int

Note

This limitation could be lifted in a future mypy release.

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 not support 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!

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[0]
f_bad(new_lst) # Fails

def f_good(x: Sequence[A]) -> A:
    return x[0]
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 (assume here that Shape is the base class of both Circle and Triangle):

shape = Circle()    # Infer shape to be Circle
...
shape = Triangle()  # Type error: Triangle is not a Circle

You can just give an explicit type for the variable in cases such the above example:

shape = Circle() # type: Shape   # 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 isinstance, issubclass, or 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

Note that the object type used in the above example is similar to Object in Java: it only supports operations defined for all objects, such as equality and isinstance(). The type Any, in contrast, supports all operations, even if they may fail at runtime. The cast above would have been unnecessary if the type of o was Any.

Note

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.

More specifically, mypy will understand the use of sys.version_info and sys.platform checks within if/elif/else statements. For example:

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 (unindented) assert; this makes mypy skip the rest of the file. Example:

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 --always-true or --always-false. (However, True and False are not treated specially!)

Note

Mypy currently does not support more complex checks, and does not assign any special meaning when assigning a sys.version_info or sys.platform check to a variable. This may change in future versions of mypy.

By default, mypy will use your current version of Python and your current operating system as default values for sys.version_info and sys.platform.

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 in --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 in --platform win32. See the documentation for sys.platform 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

Note

reveal_type and reveal_locals are only understood by mypy and don’t exist in Python. If you try to run your program, you’ll have to remove any reveal_type and reveal_locals calls before you can run your code. Both are always available and you don’t need to import them.

Silencing linters#

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 @property in 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
sudo python3 -m pip install --upgrade .

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 TypeAlias to 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

Incompatible overrides#

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 not necessary:

class NarrowerArgument(A):
    def test(self, t: List[int]) -> Sequence[str]:  # type: ignore[override]
        ...

Unreachable code#

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 foo.bar is 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 None.)

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 function.