.. _common_issues: 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. .. _annotations_needed: 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 :option:`--check-untyped-defs `. Example: .. code-block:: python 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: .. code-block:: python 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: .. code-block:: python 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, :ref:`reveal_type() ` might come in handy. Note that sometimes library stubs with imprecise type information can be a source of ``Any`` values. :py:meth:`__init__ ` **method has no annotated arguments and no return type annotation.** This is basically a combination of the two cases above, in that ``__init__`` without annotations can cause ``Any`` types leak into instance variables: .. code-block:: python 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 :option:`--ignore-missing-imports ` flag. When you use :option:`--ignore-missing-imports `, any imported module that cannot be found is silently replaced with ``Any``. To help debug this, simply leave out :option:`--ignore-missing-imports `. As mentioned in :ref:`fix-missing-imports`, setting ``ignore_missing_imports=True`` on a per-module basis will make bad surprises less likely and is highly encouraged. Use of the :option:`--follow-imports=skip ` flags can also cause problems. Use of these flags is strongly discouraged and only required in relatively niche situations. See :ref:`follow-imports` for more information. **mypy considers some of your code unreachable**. See :ref:`unreachable` for more information. **A function annotated as returning a non-optional type returns 'None' and mypy doesn't complain**. .. code-block:: python def foo() -> str: return None # No error! You may have disabled strict optional checking (see :ref:`--no-strict-optional ` for more). .. _silencing_checker: 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: .. code-block:: python import frobnicate # Error: No module "frobnicate" frobnicate.start() You can add a ``# type: ignore`` comment to tell mypy to ignore this error: .. code-block:: python 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[]`` 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 :ref:`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 :py:meth:`__getattr__ `: .. code-block:: python 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 --------------------- * 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. 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 :ref:`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: * ``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 :ref:`runtime_troubles`. Mypy runs are slow ------------------ If your mypy runs feel slow, you should probably use the :ref:`mypy daemon `, which can speed up incremental mypy runtimes by a factor of 10 or more. :ref:`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: .. code-block:: python 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): .. code-block:: python 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 ``Any`` type). .. code-block:: python def f() -> None: n = 1 ... n = 'x' # error: Incompatible types in assignment (expression has type "str", variable has type "int") .. note:: Using the :option:`--allow-redefinition ` 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 not support ``sort()``) as a list and sort it in-place: .. code-block:: python 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 :ref:`type-narrowing` for more information. .. _variance: Invariance vs covariance ------------------------ Most mutable generic collections are invariant, and mypy considers all user-defined generic classes invariant by default (see :ref:`variance-of-generics` for motivation). This could lead to some unexpected errors when combined with type inference. For example: .. code-block:: python 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: .. code-block:: python new_lst: list[A] = [B(), B()] lst = new_lst # OK * Make a copy of the right hand side: .. code-block:: python lst = list(new_lst) # Also OK * Use immutable collections as annotations whenever possible: .. code-block:: python 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: .. code-block:: python 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: .. code-block:: python 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 :py:func:`isinstance `, :py:func:`issubclass `, or ``type(obj) is some_class`` type tests, and even :ref:`user-defined type guards `, but for other kinds of checks you may need to add an explicit type cast: .. code-block:: python from collections.abc import Sequence from typing import 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: .. code-block:: python 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 :py:class:`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 :py:func:`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 :ref:`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. .. _version_and_platform_checks: 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 :py:data:`sys.version_info` and :py:data:`sys.platform` checks within ``if/elif/else`` statements. For example: .. code-block:: python 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: .. code-block:: python import sys assert sys.platform != 'win32' # The rest of this file doesn't apply to Windows. Some other expressions exhibit similar behavior; in particular, :py:data:`~typing.TYPE_CHECKING`, variables named ``MYPY``, and any variable whose name is passed to :option:`--always-true ` or :option:`--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 :py:data:`sys.version_info` or :py:data:`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 :py:data:`sys.version_info` and :py:data:`sys.platform`. To target a different Python version, use the :option:`--python-version X.Y ` flag. For example, to verify your code typechecks if were run using Python 3.8, pass in :option:`--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 :option:`--platform PLATFORM ` flag. For example, to verify your code typechecks if it were run in Windows, pass in :option:`--platform win32 `. See the documentation for :py:data:`sys.platform` for examples of valid platform parameters. .. _reveal-type: 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: .. code-block:: python 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: .. code-block:: python 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: 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: .. code-block:: python # 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: .. code-block:: python 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: .. code-block:: python from typing 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 :py:class:`@property ` in the protocol definition: .. code-block:: python from typing 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.: .. code-block:: python 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: .. code-block:: python 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: .. code-block:: text 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. 1. A variable with type ``type[...]`` is defined using an assignment with an explicit type annotation: .. code-block:: python class A: ... tp: type[A] = A 2. You can define a type alias using an assignment without an explicit type annotation at the top level of a module: .. code-block:: python class A: ... Alias = A You can also use ``TypeAlias`` (:pep:`613`) to define an *explicit type alias*: .. code-block:: python 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 :ref:`supported Python version and platform checks `): .. code-block:: python 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: .. code-block:: python from collections.abc import Sequence, 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: .. code-block:: python class NarrowerArgument(A): def test(self, t: List[int]) -> Sequence[str]: # type: ignore[override] ... .. _unreachable: 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: .. code-block:: python 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: .. code-block:: python 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: .. code-block:: python class Foo: bar: str = '' def bar() -> None: foo: Foo = Foo() if not foo.bar: return x: int = 'abc' # Reachable -- error If you use the :option:`--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: .. code-block:: python def foo(x: int | None) -> 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.