Python Type Safety Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically. When to Use This Skill Adding type hints to existing code Creating generic, reusable classes Defining structural interfaces with protocols Configuring mypy or pyright for strict checking Understanding type narrowing and guards Building type-safe APIs and libraries Core Concepts 1. Type Annotations Declare expected types for function parameters, return values, and variables. 2. Generics Write reusable code that preserves type information across different types. 3. Protocols Define structural interfaces without inheritance (duck typing with type safety). 4. Type Narrowing Use guards and conditionals to narrow types within code blocks. Quick Start def get_user ( user_id : str ) -
User | None : """Return type makes 'might not exist' explicit.""" . . .
Type checker enforces handling None case
user
get_user ( "123" ) if user is None : raise UserNotFoundError ( "123" ) print ( user . name )
Type checker knows user is User here
Fundamental Patterns Pattern 1: Annotate All Public Signatures Every public function, method, and class should have type annotations. def get_user ( user_id : str ) -
User : """Retrieve user by ID.""" . . . def process_batch ( items : list [ Item ] , max_workers : int = 4 , ) -
BatchResult [ ProcessedItem ] : """Process items concurrently.""" . . . class UserRepository : def init ( self , db : Database ) -
None : self . _db = db async def find_by_id ( self , user_id : str ) -
User | None : """Return User if found, None otherwise.""" . . . async def find_by_email ( self , email : str ) -
User | None : . . . async def save ( self , user : User ) -
User : """Save and return user with generated ID.""" . . . Use mypy --strict or pyright in CI to catch type errors early. For existing projects, enable strict mode incrementally using per-module overrides. Pattern 2: Use Modern Union Syntax Python 3.10+ provides cleaner union syntax.
Preferred (3.10+)
def find_user ( user_id : str ) -
User | None : . . . def parse_value ( v : str ) -
int | float | str : . . .
Older style (still valid, needed for 3.9)
from typing import Optional , Union def find_user ( user_id : str ) -
Optional [ User ] : . . . Pattern 3: Type Narrowing with Guards Use conditionals to narrow types for the type checker. def process_user ( user_id : str ) -
UserData : user = find_user ( user_id ) if user is None : raise UserNotFoundError ( f"User { user_id } not found" )
Type checker knows user is User here, not User | None
return UserData ( name = user . name , email = user . email , ) def process_items ( items : list [ Item | None ] ) -
list [ ProcessedItem ] :
Filter and narrow types
valid_items
[ item for item in items if item is not None ]
valid_items is now list[Item]
return [ process ( item ) for item in valid_items ] Pattern 4: Generic Classes Create type-safe reusable containers. from typing import TypeVar , Generic T = TypeVar ( "T" ) E = TypeVar ( "E" , bound = Exception ) class Result ( Generic [ T , E ] ) : """Represents either a success value or an error.""" def init ( self , value : T | None = None , error : E | None = None , ) -
None : if ( value is None ) == ( error is None ) : raise ValueError ( "Exactly one of value or error must be set" ) self . _value = value self . _error = error @property def is_success ( self ) -
bool : return self . _error is None @property def is_failure ( self ) -
bool : return self . _error is not None def unwrap ( self ) -
T : """Get value or raise the error.""" if self . _error is not None : raise self . _error return self . _value
type: ignore[return-value]
def unwrap_or ( self , default : T ) -
T : """Get value or return default.""" if self . _error is not None : return default return self . _value
type: ignore[return-value]
Usage preserves types
def parse_config ( path : str ) -
Result [ Config , ConfigError ] : try : return Result ( value = Config . from_file ( path ) ) except ConfigError as e : return Result ( error = e ) result = parse_config ( "config.yaml" ) if result . is_success : config = result . unwrap ( )
Type: Config
Advanced Patterns Pattern 5: Generic Repository Create type-safe data access patterns. from typing import TypeVar , Generic from abc import ABC , abstractmethod T = TypeVar ( "T" ) ID = TypeVar ( "ID" ) class Repository ( ABC , Generic [ T , ID ] ) : """Generic repository interface.""" @abstractmethod async def get ( self , id : ID ) -
T | None : """Get entity by ID.""" . . . @abstractmethod async def save ( self , entity : T ) -
T : """Save and return entity.""" . . . @abstractmethod async def delete ( self , id : ID ) -
bool : """Delete entity, return True if existed.""" . . . class UserRepository ( Repository [ User , str ] ) : """Concrete repository for Users with string IDs.""" async def get ( self , id : str ) -
User | None : row = await self . _db . fetchrow ( "SELECT * FROM users WHERE id = $1" , id ) return User ( ** row ) if row else None async def save ( self , entity : User ) -
User : . . . async def delete ( self , id : str ) -
bool : . . . Pattern 6: TypeVar with Bounds Restrict generic parameters to specific types. from typing import TypeVar from pydantic import BaseModel ModelT = TypeVar ( "ModelT" , bound = BaseModel ) def validate_and_create ( model_cls : type [ ModelT ] , data : dict ) -
ModelT : """Create a validated Pydantic model from dict.""" return model_cls . model_validate ( data )
Works with any BaseModel subclass
class User ( BaseModel ) : name : str email : str user = validate_and_create ( User , { "name" : "Alice" , "email" : "a@b.com" } )
user is typed as User
Type error: str is not a BaseModel subclass
result
validate_and_create ( str , { "name" : "Alice" } )
Error!
Pattern 7: Protocols for Structural Typing Define interfaces without requiring inheritance. from typing import Protocol , runtime_checkable @runtime_checkable class Serializable ( Protocol ) : """Any class that can be serialized to/from dict.""" def to_dict ( self ) -
dict : . . . @classmethod def from_dict ( cls , data : dict ) -
"Serializable" : . . .
User satisfies Serializable without inheriting from it
class User : def init ( self , id : str , name : str ) -
None : self . id = id self . name = name def to_dict ( self ) -
dict : return { "id" : self . id , "name" : self . name } @classmethod def from_dict ( cls , data : dict ) -
"User" : return cls ( id = data [ "id" ] , name = data [ "name" ] ) def serialize ( obj : Serializable ) -
str : """Works with any Serializable object.""" return json . dumps ( obj . to_dict ( ) )
Works - User matches the protocol
serialize ( User ( "1" , "Alice" ) )
Runtime checking with @runtime_checkable
isinstance ( User ( "1" , "Alice" ) , Serializable )
True
Pattern 8: Common Protocol Patterns Define reusable structural interfaces. from typing import Protocol class Closeable ( Protocol ) : """Resource that can be closed.""" def close ( self ) -
None : . . . class AsyncCloseable ( Protocol ) : """Async resource that can be closed.""" async def close ( self ) -
None : . . . class Readable ( Protocol ) : """Object that can be read from.""" def read ( self , n : int = - 1 ) -
bytes : . . . class HasId ( Protocol ) : """Object with an ID property.""" @property def id ( self ) -
str : . . . class Comparable ( Protocol ) : """Object that supports comparison.""" def lt ( self , other : "Comparable" ) -
bool : . . . def le ( self , other : "Comparable" ) -
bool : . . . Pattern 9: Type Aliases Create meaningful type names. Note: The type statement was introduced in Python 3.10 for simple aliases. Generic type statements require Python 3.12+.
Python 3.10+ type statement for simple aliases
type UserId = str type UserDict = dict [ str , Any ]
Python 3.12+ type statement with generics
type Handler [ T ] = Callable [ [ Request ] , T ] type AsyncHandler [ T ] = Callable [ [ Request ] , Awaitable [ T ] ]
Python 3.9-3.11 style (needed for broader compatibility)
from typing import TypeAlias from collections . abc import Callable , Awaitable UserId : TypeAlias = str Handler : TypeAlias = Callable [ [ Request ] , Response ]
Usage
def register_handler ( path : str , handler : Handler [ Response ] ) -
None : . . . Pattern 10: Callable Types Type function parameters and callbacks. from collections . abc import Callable , Awaitable
Sync callback
ProgressCallback
Callable [ [ int , int ] , None ]
(current, total)
Async callback
AsyncHandler
Callable [ [ Request ] , Awaitable [ Response ] ]
With named parameters (using Protocol)
class OnProgress ( Protocol ) : def call ( self , current : int , total : int , * , message : str = "" , ) -
None : . . . def process_items ( items : list [ Item ] , on_progress : ProgressCallback | None = None , ) -
list [ Result ] : for i , item in enumerate ( items ) : if on_progress : on_progress ( i , len ( items ) ) . . . Configuration Strict Mode Checklist For mypy --strict compliance:
pyproject.toml
[ tool.mypy ] python_version = "3.12" strict = true warn_return_any = true warn_unused_ignores = true disallow_untyped_defs = true disallow_incomplete_defs = true no_implicit_optional = true Incremental adoption goals: All function parameters annotated All return types annotated Class attributes annotated Minimize Any usage (acceptable for truly dynamic data) Generic collections use type parameters ( list[str] not list ) For existing codebases, enable strict mode per-module using
mypy: strict
or configure per-module overrides in pyproject.toml . Best Practices Summary Annotate all public APIs - Functions, methods, class attributes Use T | None - Modern union syntax over Optional[T] Run strict type checking - mypy --strict in CI Use generics - Preserve type info in reusable code Define protocols - Structural typing for interfaces Narrow types - Use guards to help the type checker Bound type vars - Restrict generics to meaningful types Create type aliases - Meaningful names for complex types Minimize Any - Use specific types or generics. Any is acceptable for truly dynamic data or when interfacing with untyped third-party code Document with types - Types are enforceable documentation