This section introduces a few additional kinds of types, including NoReturn
,
NewType
, TypedDict
, and types for async code. It also discusses how to
give functions more precise types using overloads. All of these are only
situationally useful, so feel free to skip this section and come back when you
have a need for some of them.
Here's a quick summary of what's covered here:
NoReturn
lets you tell mypy that a function never returns normally.NewType
lets you define a variant of a type that is treated as a separate type by mypy but is identical to the original type at runtime. For example, you can haveUserId
as a variant ofint
that is just anint
at runtime.@overload
lets you define a function that can accept multiple distinct signatures. This is useful if you need to encode a relationship between the arguments and the return type that would be difficult to express normally.TypedDict
lets you give precise types for dictionaries that represent objects with a fixed schema, such as{'id': 1, 'items': ['x']}
.- Async types let you type check programs using
async
andawait
.
Mypy provides support for functions that never return. For example, a function that unconditionally raises an exception:
from typing import NoReturn
def stop() -> NoReturn:
raise Exception('no way')
Mypy will ensure that functions annotated as returning NoReturn
truly never return, either implicitly or explicitly. Mypy will also
recognize that the code after calls to such functions is unreachable
and will behave accordingly:
def f(x: int) -> int:
if x == 0:
return x
stop()
return 'whatever works' # No error in an unreachable block
In earlier Python versions you need to install typing_extensions
using
pip to use NoReturn
in your code. Python 3 command line:
python3 -m pip install --upgrade typing-extensions
This works for Python 2:
pip install --upgrade typing-extensions
There are situations where you may want to avoid programming errors by creating simple derived classes that are only used to distinguish certain values from base class instances. Example:
class UserId(int):
pass
get_by_user_id(user_id: UserId):
...
However, this approach introduces some runtime overhead. To avoid this, the typing
module provides a helper function NewType
that creates simple unique types with
almost zero runtime overhead. Mypy will treat the statement
Derived = NewType('Derived', Base)
as being roughly equivalent to the following
definition:
class Derived(Base):
def __init__(self, _x: Base) -> None:
...
However, at runtime, NewType('Derived', Base)
will return a dummy function that
simply returns its argument:
def Derived(_x):
return _x
Mypy will require explicit casts from int
where UserId
is expected, while
implicitly casting from UserId
where int
is expected. Examples:
from typing import NewType
UserId = NewType('UserId', int)
def name_by_id(user_id: UserId) -> str:
...
UserId('user') # Fails type check
name_by_id(42) # Fails type check
name_by_id(UserId(42)) # OK
num = UserId(5) + 1 # type: int
NewType
accepts exactly two arguments. The first argument must be a string literal
containing the name of the new type and must equal the name of the variable to which the new
type is assigned. The second argument must be a properly subclassable class, i.e.,
not a type construct like Union
, etc.
The function returned by NewType
accepts only one argument; this is equivalent to
supporting only one constructor accepting an instance of the base class (see above).
Example:
from typing import NewType
class PacketId:
def __init__(self, major: int, minor: int) -> None:
self._major = major
self._minor = minor
TcpPacketId = NewType('TcpPacketId', PacketId)
packet = PacketId(100, 100)
tcp_packet = TcpPacketId(packet) # OK
tcp_packet = TcpPacketId(127, 0) # Fails in type checker and at runtime
You cannot use isinstance()
or issubclass()
on the object returned by
NewType()
, because function objects don't support these operations. You cannot
create subclasses of these objects either.
Note
Unlike type aliases, NewType
will create an entirely new and
unique type when used. The intended purpose of NewType
is to help you
detect cases where you accidentally mixed together the old base type and the
new derived type.
For example, the following will successfully typecheck when using type aliases:
UserId = int
def name_by_id(user_id: UserId) -> str:
...
name_by_id(3) # ints and UserId are synonymous
But a similar example using NewType
will not typecheck:
from typing import NewType
UserId = NewType('UserId', int)
def name_by_id(user_id: UserId) -> str:
...
name_by_id(3) # int is not the same as UserId
Sometimes the arguments and types in a function depend on each other
in ways that can't be captured with a Union
. For example, suppose
we want to write a function that can accept x-y coordinates. If we pass
in just a single x-y coordinate, we return a ClickEvent
object. However,
if we pass in two x-y coordinates, we return a DragEvent
object.
Our first attempt at writing this function might look like this:
from typing import Union, Optional
def mouse_event(x1: int,
y1: int,
x2: Optional[int] = None,
y2: Optional[int] = None) -> Union[ClickEvent, DragEvent]:
if x2 is None and y2 is None:
return ClickEvent(x1, y1)
elif x2 is not None and y2 is not None:
return DragEvent(x1, y1, x2, y2)
else:
raise TypeError("Bad arguments")
While this function signature works, it's too loose: it implies mouse_event
could return either object regardless of the number of arguments
we pass in. It also does not prohibit a caller from passing in the wrong
number of ints: mypy would treat calls like mouse_event(1, 2, 20)
as being
valid, for example.
We can do better by using overloading which lets us give the same function multiple type annotations (signatures) to more accurately describe the function's behavior:
from typing import Union, overload
# Overload *variants* for 'mouse_event'.
# These variants give extra information to the type checker.
# They are ignored at runtime.
@overload
def mouse_event(x1: int, y1: int) -> ClickEvent: ...
@overload
def mouse_event(x1: int, y1: int, x2: int, y2: int) -> DragEvent: ...
# The actual *implementation* of 'mouse_event'.
# The implementation contains the actual runtime logic.
#
# It may or may not have type hints. If it does, mypy
# will check the body of the implementation against the
# type hints.
#
# Mypy will also check and make sure the signature is
# consistent with the provided variants.
def mouse_event(x1: int,
y1: int,
x2: Optional[int] = None,
y2: Optional[int] = None) -> Union[ClickEvent, DragEvent]:
if x2 is None and y2 is None:
return ClickEvent(x1, y1)
elif x2 is not None and y2 is not None:
return DragEvent(x1, y1, x2, y2)
else:
raise TypeError("Bad arguments")
This allows mypy to understand calls to mouse_event
much more precisely.
For example, mypy will understand that mouse_event(5, 25)
will
always have a return type of ClickEvent
and will report errors for
calls like mouse_event(5, 25, 2)
.
As another example, suppose we want to write a custom container class that
implements the __getitem__
method ([]
bracket indexing). If this
method receives an integer we return a single item. If it receives a
slice
, we return a Sequence
of items.
We can precisely encode this relationship between the argument and the return type by using overloads like so:
from typing import Sequence, TypeVar, Union, overload
T = TypeVar('T')
class MyList(Sequence[T]):
@overload
def __getitem__(self, index: int) -> T: ...
@overload
def __getitem__(self, index: slice) -> Sequence[T]: ...
def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
if isinstance(index, int):
# Return a T here
elif isinstance(index, slice):
# Return a sequence of Ts here
else:
raise TypeError(...)
Note
If you just need to constrain a type variable to certain types or subtypes, you can use a :ref:`value restriction <type-variable-value-restriction>`.
An overloaded function must consist of two or more overload variants followed by an implementation. The variants and the implementations must be adjacent in the code: think of them as one indivisible unit.
The variant bodies must all be empty; only the implementation is allowed to contain code. This is because at runtime, the variants are completely ignored: they're overridden by the final implementation function.
This means that an overloaded function is still an ordinary Python
function! There is no automatic dispatch handling and you must manually
handle the different types in the implementation (e.g. by using
if
statements and isinstance
checks).
If you are adding an overload within a stub file, the implementation function should be omitted: stubs do not contain runtime logic.
Note
While we can leave the variant body empty using the pass
keyword,
the more common convention is to instead use the ellipsis (...
) literal.
When you call an overloaded function, mypy will infer the correct return
type by picking the best matching variant, after taking into consideration
both the argument types and arity. However, a call is never type
checked against the implementation. This is why mypy will report calls
like mouse_event(5, 25, 3)
as being invalid even though it matches the
implementation signature.
If there are multiple equally good matching variants, mypy will select the variant that was defined first. For example, consider the following program:
from typing import List, overload
@overload
def summarize(data: List[int]) -> float: ...
@overload
def summarize(data: List[str]) -> str: ...
def summarize(data):
if not data:
return 0.0
elif isinstance(data[0], int):
# Do int specific code
else:
# Do str-specific code
# What is the type of 'output'? float or str?
output = summarize([])
The summarize([])
call matches both variants: an empty list could
be either a List[int]
or a List[str]
. In this case, mypy
will break the tie by picking the first matching variant: output
will have an inferred type of float
. The implementor is responsible
for making sure summarize
breaks ties in the same way at runtime.
However, there are two exceptions to the "pick the first match" rule.
First, if multiple variants match due to an argument being of type
Any
, mypy will make the inferred type also be Any
:
dynamic_var: Any = some_dynamic_function()
# output2 is of type 'Any'
output2 = summarize(dynamic_var)
Second, if multiple variants match due to one or more of the arguments being a union, mypy will make the inferred type be the union of the matching variant returns:
some_list: Union[List[int], List[str]]
# output3 is of type 'Union[float, str]'
output3 = summarize(some_list)
Note
Due to the "pick the first match" rule, changing the order of your overload variants can change how mypy type checks your program.
To minimize potential issues, we recommend that you:
- Make sure your overload variants are listed in the same order as
the runtime checks (e.g.
isinstance
checks) in your implementation. - Order your variants and runtime checks from most to least specific. (See the following section for an example).
Mypy will perform several checks on your overload variant definitions
to ensure they behave as expected. First, mypy will check and make sure
that no overload variant is shadowing a subsequent one. For example,
consider the following function which adds together two Expression
objects, and contains a special-case to handle receiving two Literal
types:
from typing import overload, Union
class Expression:
# ...snip...
class Literal(Expression):
# ...snip...
# Warning -- the first overload variant shadows the second!
@overload
def add(left: Expression, right: Expression) -> Expression: ...
@overload
def add(left: Literal, right: Literal) -> Literal: ...
def add(left: Expression, right: Expression) -> Expression:
# ...snip...
While this code snippet is technically type-safe, it does contain an
anti-pattern: the second variant will never be selected! If we try calling
add(Literal(3), Literal(4))
, mypy will always pick the first variant
and evaluate the function call to be of type Expression
, not Literal
.
This is because Literal
is a subtype of Expression
, which means
the "pick the first match" rule will always halt after considering the
first overload.
Because having an overload variant that can never be matched is almost certainly a mistake, mypy will report an error. To fix the error, we can either 1) delete the second overload or 2) swap the order of the overloads:
# Everything is ok now -- the variants are correctly ordered
# from most to least specific.
@overload
def add(left: Literal, right: Literal) -> Literal: ...
@overload
def add(left: Expression, right: Expression) -> Expression: ...
def add(left: Expression, right: Expression) -> Expression:
# ...snip...
Mypy will also type check the different variants and flag any overloads that have inherently unsafely overlapping variants. For example, consider the following unsafe overload definition:
from typing import overload, Union
@overload
def unsafe_func(x: int) -> int: ...
@overload
def unsafe_func(x: object) -> str: ...
def unsafe_func(x: object) -> Union[int, str]:
if isinstance(x, int):
return 42
else:
return "some string"
On the surface, this function definition appears to be fine. However, it will result in a discrepancy between the inferred type and the actual runtime type when we try using it like so:
some_obj: object = 42
unsafe_func(some_obj) + " danger danger" # Type checks, yet crashes at runtime!
Since some_obj
is of type object
, mypy will decide that unsafe_func
must return something of type str
and concludes the above will type check.
But in reality, unsafe_func
will return an int, causing the code to crash
at runtime!
To prevent these kinds of issues, mypy will detect and prohibit inherently unsafely overlapping overloads on a best-effort basis. Two variants are considered unsafely overlapping when both of the following are true:
- All of the arguments of the first variant are compatible with the second.
- The return type of the first variant is not compatible with (e.g. is not a subtype of) the second.
So in this example, the int
argument in the first variant is a subtype of
the object
argument in the second, yet the int
return type not is a subtype of
str
. Both conditions are true, so mypy will correctly flag unsafe_func
as
being unsafe.
However, mypy will not detect all unsafe uses of overloads. For example,
suppose we modify the above snippet so it calls summarize
instead of
unsafe_func
:
some_list: List[str] = []
summarize(some_list) + "danger danger" # Type safe, yet crashes at runtime!
We run into a similar issue here. This program type checks if we look just at the
annotations on the overloads. But since summarize(...)
is designed to be biased
towards returning a float when it receives an empty list, this program will actually
crash during runtime.
The reason mypy does not flag definitions like summarize
as being potentially
unsafe is because if it did, it would be extremely difficult to write a safe
overload. For example, suppose we define an overload with two variants that accept
types A
and B
respectively. Even if those two types were completely unrelated,
the user could still potentially trigger a runtime error similar to the ones above by
passing in a value of some third type C
that inherits from both A
and B
.
Thankfully, these types of situations are relatively rare. What this does mean, however, is that you should exercise caution when designing or using an overloaded function that can potentially receive values that are an instance of two seemingly unrelated types.
The body of an implementation is type-checked against the
type hints provided on the implementation. For example, in the
MyList
example up above, the code in the body is checked with
argument list index: Union[int, slice]
and a return type of
Union[T, Sequence[T]]
. If there are no annotations on the
implementation, then the body is not type checked. If you want to
force mypy to check the body anyways, use the --check-untyped-defs
flag (:ref:`more details here <untyped-definitions-and-calls>`).
The variants must also also be compatible with the implementation
type hints. In the MyList
example, mypy will check that the
parameter type int
and the return type T
are compatible with
Union[int, slice]
and Union[T, Sequence]
for the
first variant. For the second variant it verifies the parameter
type slice
and the return type Sequence[T]
are compatible
with Union[int, slice]
and Union[T, Sequence]
.
Note
The overload semantics documented above are new as of mypy 0.620.
Previously, mypy used to perform type erasure on all overload variants. For
example, the summarize
example from the previous section used to be
illegal because List[str]
and List[int]
both erased to just List[Any]
.
This restriction was removed in mypy 0.620.
Mypy also previously used to select the best matching variant using a different
algorithm. If this algorithm failed to find a match, it would default to returning
Any
. The new algorithm uses the "pick the first match" rule and will fall back
to returning Any
only if the input arguments also contain Any
.
Mypy supports the ability to type coroutines that use the async/await
syntax introduced in Python 3.5. For more information regarding coroutines and
this new syntax, see PEP 492.
Functions defined using async def
are typed just like normal functions.
The return type annotation should be the same as the type of the value you
expect to get back when await
-ing the coroutine.
import asyncio
async def format_string(tag: str, count: int) -> str:
return 'T-minus {} ({})'.format(count, tag)
async def countdown_1(tag: str, count: int) -> str:
while count > 0:
my_str = await format_string(tag, count) # has type 'str'
print(my_str)
await asyncio.sleep(0.1)
count -= 1
return "Blastoff!"
loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_1("Millennium Falcon", 5))
loop.close()
The result of calling an async def
function without awaiting will be a
value of type typing.Coroutine[Any, Any, T]
, which is a subtype of
Awaitable[T]
:
my_coroutine = countdown_1("Millennium Falcon", 5)
reveal_type(my_coroutine) # has type 'Coroutine[Any, Any, str]'
Note
:ref:`reveal_type() <reveal-type>` displays the inferred static type of an expression.
If you want to use coroutines in Python 3.4, which does not support
the async def
syntax, you can instead use the @asyncio.coroutine
decorator to convert a generator into a coroutine.
Note that we set the YieldType
of the generator to be Any
in the
following example. This is because the exact yield type is an implementation
detail of the coroutine runner (e.g. the asyncio
event loop) and your
coroutine shouldn't have to know or care about what precisely that type is.
from typing import Any, Generator
import asyncio
@asyncio.coroutine
def countdown_2(tag: str, count: int) -> Generator[Any, None, str]:
while count > 0:
print('T-minus {} ({})'.format(count, tag))
yield from asyncio.sleep(0.1)
count -= 1
return "Blastoff!"
loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_2("USS Enterprise", 5))
loop.close()
As before, the result of calling a generator decorated with @asyncio.coroutine
will be a value of type Awaitable[T]
.
Note
At runtime, you are allowed to add the @asyncio.coroutine
decorator to
both functions and generators. This is useful when you want to mark a
work-in-progress function as a coroutine, but have not yet added yield
or
yield from
statements:
import asyncio
@asyncio.coroutine
def serialize(obj: object) -> str:
# todo: add yield/yield from to turn this into a generator
return "placeholder"
However, mypy currently does not support converting functions into coroutines. Support for this feature will be added in a future version, but for now, you can manually force the function to be a generator by doing something like this:
from typing import Generator
import asyncio
@asyncio.coroutine
def serialize(obj: object) -> Generator[None, None, str]:
# todo: add yield/yield from to turn this into a generator
if False:
yield
return "placeholder"
You may also choose to create a subclass of Awaitable
instead:
from typing import Any, Awaitable, Generator
import asyncio
class MyAwaitable(Awaitable[str]):
def __init__(self, tag: str, count: int) -> None:
self.tag = tag
self.count = count
def __await__(self) -> Generator[Any, None, str]:
for i in range(n, 0, -1):
print('T-minus {} ({})'.format(i, tag))
yield from asyncio.sleep(0.1)
return "Blastoff!"
def countdown_3(tag: str, count: int) -> Awaitable[str]:
return MyAwaitable(tag, count)
loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_3("Heart of Gold", 5))
loop.close()
To create an iterable coroutine, subclass AsyncIterator
:
from typing import Optional, AsyncIterator
import asyncio
class arange(AsyncIterator[int]):
def __init__(self, start: int, stop: int, step: int) -> None:
self.start = start
self.stop = stop
self.step = step
self.count = start - step
def __aiter__(self) -> AsyncIterator[int]:
return self
async def __anext__(self) -> int:
self.count += self.step
if self.count == self.stop:
raise StopAsyncIteration
else:
return self.count
async def countdown_4(tag: str, n: int) -> str:
async for i in arange(n, 0, -1):
print('T-minus {} ({})'.format(i, tag))
await asyncio.sleep(0.1)
return "Blastoff!"
loop = asyncio.get_event_loop()
loop.run_until_complete(countdown_4("Serenity", 5))
loop.close()
For a more concrete example, the mypy repo has a toy webcrawler that demonstrates how to work with coroutines. One version uses async/await and one uses yield from.
Note
TypedDict is an officially supported feature, but it is still experimental.
Python programs often use dictionaries with string keys to represent objects. Here is a typical example:
movie = {'name': 'Blade Runner', 'year': 1982}
Only a fixed set of string keys is expected ('name'
and
'year'
above), and each key has an independent value type (str
for 'name'
and int
for 'year'
above). We've previously
seen the Dict[K, V]
type, which lets you declare uniform
dictionary types, where every value has the same type, and arbitrary keys
are supported. This is clearly not a good fit for
movie
above. Instead, you can use a TypedDict
to give a precise
type for objects like movie
, where the type of each
dictionary value depends on the key:
from mypy_extensions import TypedDict
Movie = TypedDict('Movie', {'name': str, 'year': int})
movie = {'name': 'Blade Runner', 'year': 1982} # type: Movie
Movie
is a TypedDict type with two items: 'name'
(with type str
)
and 'year'
(with type int
). Note that we used an explicit type
annotation for the movie
variable. This type annotation is
important -- without it, mypy will try to infer a regular, uniform
Dict
type for movie
, which is not what we want here.
Note
If you pass a TypedDict object as an argument to a function, no
type annotation is usually necessary since mypy can infer the
desired type based on the declared argument type. Also, if an
assignment target has been previously defined, and it has a
TypedDict type, mypy will treat the assigned value as a TypedDict,
not Dict
.
Now mypy will recognize these as valid:
name = movie['name'] # Okay; type of name is str
year = movie['year'] # Okay; type of year is int
Mypy will detect an invalid key as an error:
director = movie['director'] # Error: 'director' is not a valid key
Mypy will also reject a runtime-computed expression as a key, as it can't verify that it's a valid key. You can only use string literals as TypedDict keys.
The TypedDict
type object can also act as a constructor. It
returns a normal dict
object at runtime -- a TypedDict
does
not define a new runtime type:
toy_story = Movie(name='Toy Story', year=1995)
This is equivalent to just constructing a dictionary directly using
{ ... }
or dict(key=value, ...)
. The constructor form is
sometimes convenient, since it can be used without a type annotation,
and it also makes the type of the object explicit.
Like all types, TypedDicts can be used as components to build arbitrarily complex types. For example, you can define nested TypedDicts and containers with TypedDict items. Unlike most other types, mypy uses structural compatibility checking (or structural subtyping) with TypedDicts. A TypedDict object with extra items is compatible with a narrower TypedDict, assuming item types are compatible (totality also affects subtyping, as discussed below).
Note
You need to install mypy_extensions
using pip to use TypedDict
:
python3 -m pip install --upgrade mypy-extensions
Or, if you are using Python 2:
pip install --upgrade mypy-extensions
By default mypy ensures that a TypedDict object has all the specified keys. This will be flagged as an error:
# Error: 'year' missing
toy_story = {'name': 'Toy Story'} # type: Movie
Sometimes you want to allow keys to be left out when creating a
TypedDict object. You can provide the total=False
argument to
TypedDict(...)
to achieve this:
GuiOptions = TypedDict(
'GuiOptions', {'language': str, 'color': str}, total=False)
options = {} # type: GuiOptions # Okay
options['language'] = 'en'
You may need to use get()
to access items of a partial (non-total)
TypedDict, since indexing using []
could fail at runtime.
However, mypy still lets use []
with a partial TypedDict -- you
just need to be careful with it, as it could result in a KeyError
.
Requiring get()
everywhere would be too cumbersome. (Note that you
are free to use get()
with total TypedDicts as well.)
Keys that aren't required are shown with a ?
in error messages:
# Revealed type is 'TypedDict('GuiOptions', {'language'?: builtins.str,
# 'color'?: builtins.str})'
reveal_type(options)
Totality also affects structural compatibility. You can't use a partial TypedDict when a total one is expected. Also, a total TypedDict is not valid when a partial one is expected.
An alternative, class-based syntax to define a TypedDict is supported in Python 3.6 and later:
from mypy_extensions import TypedDict
class Movie(TypedDict):
name: str
year: int
The above definition is equivalent to the original Movie
definition. It doesn't actually define a real class. This syntax also
supports a form of inheritance -- subclasses can define additional
items. However, this is primarily a notational shortcut. Since mypy
uses structural compatibility with TypedDicts, inheritance is not
required for compatibility. Here is an example of inheritance:
class Movie(TypedDict):
name: str
year: int
class BookBasedMovie(Movie):
based_on: str
Now BookBasedMovie
has keys name
, year
and based_on
.
In addition to allowing reuse across TypedDict types, inheritance also allows
you to mix required and non-required (using total=False
) items
in a single TypedDict. Example:
class MovieBase(TypedDict):
name: str
year: int
class Movie(MovieBase, total=False):
based_on: str
Now Movie
has required keys name
and year
, while based_on
can be left out when constructing an object. A TypedDict with a mix of required
and non-required keys, such as Movie
above, will only be compatible with
another TypedDict if all required keys in the other TypedDict are required keys in the
first TypedDict, and all non-required keys of the other TypedDict are also non-required keys
in the first TypedDict.