Getting started with typing in Python is easy, but it’s important to understand a few simple concepts.
### Type Declarations
When you add a type annotation to a variable or a parameter in Python, you are _declaring_ that the symbol will be assigned values that are compatible with that type. You can think of type annotations as a powerful way to comment your code. Unlike text-based comments, these comments are readable by both humans and enforceable by type checkers.
If a variable or parameter has no type annotation, the type checker must assume that any value can be assigned to it. This eliminates the ability for a type checker to identify type incompatibilities.
### Type Assignability
When your code assigns a value to a symbol (in an assignment expression) or a parameter (in a call expression), the type checker first determines the type of the value being assigned. It then determines whether the target has a declared type. If so, it verifies that the type of the value is _assignable_ to the declared type.
Let’s look at a few simple examples. In this first example, the declared type of `a` is `float`, and it is assigned a value that is an `int`. This is permitted because `int` is assignable to `float`.
```
a: float = 3
```
In this example, the declared type of `b` is `int`, and it is assigned a value that is a `float`. This is flagged as an error because `float` is not assignable to `int`.
```
b: int = 3.4 # Error
```
This example introduces the notion of a _Union type_, which specifies that a value can be one of several distinct types.
```
c: Union[int, float] = 3.4
c = 5
c = a
c = b
c = None # Error
c = "" # Error
```
This example introduces the _Optional_ type, which is the same as a union with `None`.
```
d: Optional[int] = 4
d = b
d = None
d = "" # Error
```
Those examples are straightforward. Let’s look at one that is less intuitive. In this example, the declared type of `f` is `List[Optional[int]]`. A value of type `List[int]` is being assigned to `f`. As we saw above, `int` is assignable to `Optional[int]`. You might therefore assume that `List[int]` is assignable to `List[Optional[int]]`, but this is an incorrect assumption. To understand why, we need to understand generic types and type arguments.
```
e: List[int] = [3, 4]
f: List[Optional[int]] = e # Error
```
### Generic Types
A _generic type_ is a class that is able to handle different types of inputs. For example, the `List` class is generic because it is able to operate on different types of elements. The type `List` by itself does not specify what is contained within the list. Its element type must be specified as a _type argument_ using the indexing (square bracket) syntax in Python. For example, `List[int]` denotes a list that contains only `int` elements whereas `List[Union[int, float]]` denotes a list that contains a mixture of int and float elements.
We noted above that `List[int]` is not assignable to `List[Optional[int]]`. Why is this the case? Consider the following example.
The code is appending the value `None` to the list `my_list_2`, but `my_list_2` refers to the same object as `my_list_1`, which has a declared type of `List[int]`. The code has violated the type of `my_list_1` because it no longer contains only `int` elements. This broken assumption results in a runtime exception. The type checker detects this broken assumption when the code attempts to assign `my_list_1` to `my_list_2`.
`List` is an example of a _mutable container type_. It is mutable in that code is allowed to modify its contents — for example, add or remove items. The type parameters for mutable container types are typically marked as _invariant_, which means that an exact type match is enforced. This is why the type checker reports an error when attempting to assign a `List[int]` to a variable of type `List[Optional[int]]`.
Most mutable container types also have immutable counterparts.
| Mutable Type | Immutable Type |
| ----------------- | -------------- |
| List | Sequence |
| Dict | Mapping |
| Set | AbstractSet |
| n/a | Tuple |
Switching from a mutable container type to a corresponding immutable container type is often an effective way to resolve type errors relating to assignability. Let’s modify the example above by changing the type annotation for `my_list_2`.
```
my_list_1: List[int] = [1, 2, 3]
my_list_2: Sequence[Optional[int]] = my_list_1 # No longer an error
For more details about generic types, type parameters, and invariance, refer to [PEP 483 — The Theory of Type Hints](https://www.python.org/dev/peps/pep-0483/).
At the start of this function, the type checker knows nothing about `val` other than that its declared type is `Union[float, str, complex]`. Then it is assigned a value that has a known type of `int`. This is a legal assignment because `int` is considered a subclass of `float`. At the point in the code immediately after the assignment, the type checker knows that the type of `val` is an `int`. This is a “narrower” (more specific) type than `Union[float, str, complex]`. Type narrowing is applied when ever a symbol is assigned a new value.
Another assignment occurs several lines further down, this time within a conditional block. The symbol `val` is assigned a value known to be of type `str`, so the narrowed type of `val` is now `str`. Once the code flow of the conditional block merges with the main body of the function, the narrowed type of `val` becomes `Union[int, str]` because the type checker cannot statically predict whether the conditional block will be executed at runtime.
Another way that types can be narrowed is through the use of conditional code flow statements like `if`, `while`, and `assert`. Type narrowing applies to the block of code that is “guarded” by that condition, so type narrowing in this context is sometimes referred to as a “type guard”. For example, if you see the conditional statement `if x is None:`, the code within that `if` statement can assume that `x` contains `None`. Within the code sample above, we see an example of a type guard involving a call to `isinstance`. The type checker knows that `isinstance(val, int)` will return True only in the case where `val` contains a value of type `int`, not type `str`. So the code within the `if` block can assume that `val` contains a value of type `int`, and the code within the `else` block can assume that `val` contains a value of type `str`. This demonstrates how a type (in this case `Union[int, str]`) can be narrowed in both a positive (`if`) and negative (`else`) test.
*`x[I] is None` and `x[I] is not None` (where I is a literal expression and x is a known-length tuple that is distinguished by the index indicated by I)
Expressions supported for type guards include simple names, member access chains (e.g. `a.b.c.d`), the unary `not` operator, the binary `and` and `or` operators, subscripts that are constant numbers (e.g. `a[2]`), and call expressions. Other operators (such as arithmetic operators or other subscripts) are not supported.
Some type guards are able to narrow in both the positive and negative cases. Positive cases are used in `if` statements, and negative cases are used in `else` statements. (Positive and negative cases are flipped if the type guard expression is preceded by a `not` operator.) In some cases, the type can be narrowed only in the positive or negative case but not both. Consider the following examples:
```python
class Foo: pass
class Bar: pass
def func1(val: Union[Foo, Bar]):
if isinstance(Bar):
reveal_type(val) # Bar
else:
reveal_type(val) # Foo
def func2(val: Optional[int]):
if val:
reveal_type(val) # int
else:
reveal_type(val) # Optional[int]
```
In the example of `func1`, the type was narrowed in both the positive and negative cases. In the example of `func2`, the type was narrowed only the positive case because the type of `val` might be either `int` (specifically, a value of 0) or `None` in the negative case.
Pyright also supports a type guard expression `c`, where `c` is an identifier that refers to a local variable that is assigned one of the above supported type guard expression forms. These are called “aliased conditional expressions”. Examples include `c = a is not None` and `c = isinstance(a, str)`. When “c” is used within a conditional check, it can be used to narrow the type of expression `a`.
This pattern is supported only in cases where `c` is a local variable within a module or function scope and is assigned a value only once. It is also limited to cases where expression `a` is a simple identifier (as opposed to a member access expression or subscript expression), is local to the function or module scope, and is assigned only once within the scope. Unary `not` operators are allowed for expression `a`, but binary `and` and `or` are not.
When an “if” or “elif” clause is used without a corresponding “else”, Pyright will generally assume that the code can “fall through” without executing the “if” or “elif” block. However, there are cases where the analyzer can determine that a fall-through is not possible because the “if” or “elif” is guaranteed to be executed based on type analysis.
If you later added another color to the `Color` enumeration above (e.g. `YELLOW = 4`), Pyright would detect that `func3` no longer exhausts all members of the enumeration and possibly returns `None`, which violates the declared return type. Likewise, if you modify the type of the `value` parameter in `func4` to expand the union, a similar error will be produced.
This “narrowing for implied else” technique works for all narrowing expressions listed above with the exception of simple falsy/truthy statements and type guards. These are excluded because they are not generally used for exhaustive checks, and their inclusion would have a significant impact on analysis performance.
In general, the type `Any` is not narrowed. The only exceptions to this rule are the built-in `isinstance` and `issubclass` type guards, class pattern matching in “match” statements, and user-defined type guards. In all other cases, `Any` is left as is, even for assignments.
# The arguments for `a` and `b` are inconsistent types
v3 = add(1.3, "hi") # Error
```
When checking the implementation of a function that uses constrained type variables in its signature, the type checker must verify that type consistency is guaranteed. Consider the following example, where the input parameter and return type are both annotated with a constrained type variable. The type checker must verify that if a caller passes an argument of type `str`, then all code paths must return a `str`. Likewise, if a caller passes an argument of type `float`, all code paths must return a `float`.
Notice that the type of variable `sum` is reported with asterisks (`*`). This indicates that internally the type checker is tracking the type as conditional. In this particular example, it indicates that `sum` is a `str` type if the parameter `value` is a `str` but is a `float` if `value` is a `float`. By tracking these conditional types, the type checker can verify that the return type is consistent with the return type `_StrOrFloat`.
When a type annotation for a method’s `self` or `cls` parameter is omitted, pyright will infer its type based on the class that contains the method. The inferred type is internally represented as a type variable that is bound to the class.
The type of `self` is represented as `Self@ClassName` where `ClassName` is the class that contains the method. Likewise, the `cls` parameter in a class method will have the type `Type[Self@ClassName]`.
Some functions or methods can return one of several different types. In cases where the return type depends on the types of the input parameters, it is useful to specify this using a series of `@overload` signatures. When Pyright evaluates a call expression, it determines which overload signature best matches the supplied arguments.
[PEP 484](https://www.python.org/dev/peps/pep-0484/#function-method-overloading) introduced the `@overload` decorator and described how it can be used, but the PEP did not specify precisely how a type checker should choose the “best” overload. Pyright uses the following rules.
1. Pyright first filters the list of overloads based on simple “arity” (number of arguments) and keyword argument matching. For example, if one overload requires two position arguments but only one positional argument is supplied by the caller, that overload is eliminated from consideration. Likewise, if the call includes a keyword argument but no corresponding parameter is included in the overload, it is eliminated from consideration.
2. Pyright next considers the types of the arguments and compares them to the declared types of the corresponding parameters. If the types do not match for a given overload, that overload is eliminated from consideration. Bidirectional type inference is used to determine the types of the argument expressions.
3. If only one overload remains, it is the “winner”.
4. If more than one overload remains, the “winner” is chosen based on the order in which the overloads are declared. In general, the first remaining overload is the “winner”. One exception to this rule is when a `*args` (unpacked) argument matches a `*args` parameter in one of the overload signatures. This situation overrides the normal order-based rule.
5. If no overloads remain, Pyright considers whether any of the arguments are union types. If so, these union types are expanded into their constituent subtypes, and the entire process of overload matching is repeated with the expanded argument types. If two or more overloads match, the union of their respective return types form the final return type for the call expression.
6. If no overloads remain and all unions have been expanded, a diagnostic is generated indicating that the supplied arguments are incompatible with all overload signatures.
