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Type Annotations - Python 3.10

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Type Annotations - Python 3.10

This document captures type annotation guidance for Python 3.10. This is the baseline for modern Python type syntax.

Overview

Python 3.10 introduced major improvements to type annotation syntax through PEP 604 (union types via |) and PEP 585 (generic types in standard collections). These features eliminated the need for most typing module imports and made type annotations more concise and readable.

What's new in 3.10:

  • Union types with | operator (PEP 604)
  • Built-in generic types: list[T], dict[K, V], etc. (PEP 585)
  • No more need for List, Dict, Union, Optional from typing

What you need from typing module:

  • TypeVar for generic functions/classes
  • Protocol for structural typing (rare - prefer ABC)
  • TYPE_CHECKING for conditional imports
  • Any (use sparingly)

Complete Type Annotation Syntax for Python 3.10

Basic Collection Types

✅ PREFERRED - Use built-in generic types:

python
names: list[str] = []
mapping: dict[str, int] = {}
unique_ids: set[str] = set()
coordinates: tuple[int, int] = (0, 0)

❌ WRONG - Don't use typing module equivalents:

python
from typing import List, Dict, Set, Tuple  # Don't do this
names: List[str] = []
mapping: Dict[str, int] = {}

Why: Built-in types are more concise, don't require imports, and are the modern Python standard.

Union Types

✅ PREFERRED - Use | operator:

python
def process(value: str | int) -> str:
    return str(value)

def find_config(name: str) -> dict[str, str] | dict[str, int]:
    ...

# Multiple unions
def parse(input: str | int | float) -> str:
    return str(input)

❌ WRONG - Don't use typing.Union:

python
from typing import Union
def process(value: Union[str, int]) -> str:  # Don't do this
    ...

Optional Types

✅ PREFERRED - Use X | None:

python
def find_user(id: str) -> User | None:
    """Returns user or None if not found."""
    if id in users:
        return users[id]
    return None

def get_config(key: str) -> str | None:
    return config.get(key)

❌ WRONG - Don't use typing.Optional:

python
from typing import Optional
def find_user(id: str) -> Optional[User]:  # Don't do this
    ...

Generic Functions with TypeVar

✅ PREFERRED - Use TypeVar for generic functions:

python
from typing import TypeVar

T = TypeVar("T")

def first(items: list[T]) -> T | None:
    """Return first item or None if empty."""
    if not items:
        return None
    return items[0]

def identity(value: T) -> T:
    """Return the value unchanged."""
    return value

Note: This is the standard way in Python 3.10. Python 3.12 introduces better syntax (PEP 695).

Generic Classes

✅ PREFERRED - Use Generic with TypeVar:

python
from typing import Generic, TypeVar

T = TypeVar("T")

class Stack(Generic[T]):
    """A generic stack data structure."""

    def __init__(self) -> None:
        self._items: list[T] = []

    def push(self, item: T) -> None:
        self._items.append(item)

    def pop(self) -> T | None:
        if not self._items:
            return None
        return self._items.pop()

# Usage
int_stack = Stack[int]()
int_stack.push(42)

Note: Python 3.12 introduces cleaner syntax for this pattern.

Constrained and Bounded TypeVars

✅ Use TypeVar constraints when needed:

python
from typing import TypeVar

# Constrained to specific types
Numeric = TypeVar("Numeric", int, float)

def add(a: Numeric, b: Numeric) -> Numeric:
    return a + b

# Bounded to base class
T = TypeVar("T", bound=BaseClass)

def process(obj: T) -> T:
    return obj

Callable Types

✅ PREFERRED - Use collections.abc.Callable:

python
from collections.abc import Callable

# Function that takes int, returns str
processor: Callable[[int], str] = str

# Function with no args, returns None
callback: Callable[[], None] = lambda: None

# Function with multiple args
validator: Callable[[str, int], bool] = lambda s, i: len(s) > i

Type Aliases

✅ Use simple assignment for type aliases:

python
# Simple alias
UserId = str
Config = dict[str, str | int | bool]

# Complex nested type
JsonValue = dict[str, "JsonValue"] | list["JsonValue"] | str | int | float | bool | None

def load_config() -> Config:
    return {"host": "localhost", "port": 8080}

Note: Python 3.12 introduces type statement for better alias support.

when from future import annotations is Needed

Use from __future__ import annotations when you encounter:

Forward references (class referencing itself):

python
from __future__ import annotations

class Node:
    def __init__(self, value: int, parent: Node | None = None):
        self.value = value
        self.parent = parent

Circular type imports:

python
# a.py
from __future__ import annotations
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from b import B

class A:
    def method(self) -> B:
        ...

Complex recursive types:

python
from __future__ import annotations

JsonValue = dict[str, JsonValue] | list[JsonValue] | str | int | float | bool | None

Interfaces: ABC vs Protocol

✅ PREFERRED - Use ABC for interfaces:

python
from abc import ABC, abstractmethod

class Repository(ABC):
    @abstractmethod
    def get(self, id: str) -> User | None:
        """Get user by ID."""

    @abstractmethod
    def save(self, user: User) -> None:
        """Save user."""

🟡 VALID - Use Protocol only for structural typing:

python
from typing import Protocol

class Drawable(Protocol):
    def draw(self) -> None: ...

# Any object with draw() method matches
def render(obj: Drawable) -> None:
    obj.draw()

Dignified Python prefers ABC because it makes inheritance and intent explicit.

Complete Examples

Repository Pattern

python
from abc import ABC, abstractmethod

class Repository(ABC):
    """Abstract base class for data repositories."""

    @abstractmethod
    def get(self, id: str) -> dict[str, str] | None:
        """Get entity by ID."""

    @abstractmethod
    def save(self, entity: dict[str, str]) -> None:
        """Save entity."""

    @abstractmethod
    def delete(self, id: str) -> bool:
        """Delete entity, return success."""

class UserRepository(Repository):
    def __init__(self) -> None:
        self._users: dict[str, dict[str, str]] = {}

    def get(self, id: str) -> dict[str, str] | None:
        return self._users.get(id)

    def save(self, entity: dict[str, str]) -> None:
        if "id" not in entity:
            raise ValueError("Entity must have id")
        self._users[entity["id"]] = entity

    def delete(self, id: str) -> bool:
        if id in self._users:
            del self._users[id]
            return True
        return False

Generic Data Structures

python
from typing import Generic, TypeVar

T = TypeVar("T")

class Node(Generic[T]):
    """A node in a tree structure."""

    def __init__(self, value: T, children: list[Node[T]] | None = None) -> None:
        self.value = value
        self.children = children or []

    def add_child(self, child: Node[T]) -> None:
        self.children.append(child)

    def find(self, predicate: Callable[[T], bool]) -> Node[T] | None:
        """Find first node matching predicate."""
        if predicate(self.value):
            return self
        for child in self.children:
            result = child.find(predicate)
            if result:
                return result
        return None

# Usage
from collections.abc import Callable

root = Node[int](1)
root.add_child(Node[int](2))
root.add_child(Node[int](3))

Configuration Management

python
from dataclasses import dataclass

@dataclass(frozen=True)
class DatabaseConfig:
    host: str
    port: int
    username: str
    password: str | None = None
    ssl_enabled: bool = False

@dataclass(frozen=True)
class AppConfig:
    app_name: str
    debug_mode: bool
    database: DatabaseConfig
    feature_flags: dict[str, bool]

def load_config(path: str) -> AppConfig:
    """Load application configuration from file."""
    import json
    from pathlib import Path

    config_path = Path(path)
    if not config_path.exists():
        raise FileNotFoundError(f"Config not found: {path}")

    data: dict[str, str | int | bool | dict[str, str | int | bool]] = json.loads(
        config_path.read_text(encoding="utf-8")
    )

    # Parse and validate...
    return AppConfig(...)

API Client with Error Handling

python
from collections.abc import Callable
from typing import TypeVar

T = TypeVar("T")

class ApiResponse(Generic[T]):
    """Container for API response with data or error."""

    def __init__(self, data: T | None = None, error: str | None = None) -> None:
        self.data = data
        self.error = error

    def is_success(self) -> bool:
        return self.error is None

    def map(self, func: Callable[[T], U]) -> ApiResponse[U]:
        """Transform successful response data."""
        if self.is_success() and self.data is not None:
            return ApiResponse(data=func(self.data))
        return ApiResponse(error=self.error)

U = TypeVar("U")

def fetch_user(id: str) -> ApiResponse[dict[str, str]]:
    """Fetch user from API."""
    # Implementation...
    return ApiResponse(data={"id": id, "name": "Alice"})

Type Checking Rules

What to Type

✅ MUST type:

  • All public function parameters (except self, cls)
  • All public function return values
  • All class attributes (public and private)
  • Module-level constants

🟡 SHOULD type:

  • Internal function signatures
  • Complex local variables

🟢 MAY skip:

  • Simple local variables where type is obvious (count = 0)
  • Lambda parameters in short inline lambdas
  • Loop variables in short comprehensions

Running Type Checker

bash
uv run ty check

All code should pass type checking without errors.

Type Checking Configuration

Configure ty in pyproject.toml:

toml
[tool.ty.environment]
python-version = "3.10"

Common Patterns

Checking for None

✅ CORRECT - Check before use:

python
def process_user(user: User | None) -> str:
    if user is None:
        return "No user"
    return user.name

Dict.get() with Type Safety

✅ CORRECT - Handle None case:

python
def get_port(config: dict[str, int]) -> int:
    port = config.get("port")
    if port is None:
        return 8080
    return port

List Operations

✅ CORRECT - Check before accessing:

python
def first_or_default(items: list[str], default: str) -> str:
    if not items:
        return default
    return items[0]

Migration from Python 3.9

If upgrading from Python 3.9, apply these changes:

  1. Replace typing module types:

    • List[X] → list[X]
    • Dict[K, V] → dict[K, V]
    • Set[X] → set[X]
    • Tuple[X, Y] → tuple[X, Y]
    • Union[X, Y] → X | Y
    • Optional[X] → X | None

  2. Add future annotations if needed:

    • Add from __future__ import annotations for forward references
    • Add for circular imports with TYPE_CHECKING

  3. Remove unnecessary imports:

    • Remove from typing import List, Dict, Optional, Union
    • Keep only TypeVar, Generic, Protocol, TYPE_CHECKING, Any

References