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Python Tutorial

Python - Type Hints

Python type hints were introduced in PEP 484 to bring the benefits of static typing to a dynamically typed language. Although type hints do not enforce type checking at runtime, they provide a way to specify the expected types ofvariables, function parameters, and return values, which can be checked by static analysis tools such asmypy. This enhances code readability, facilitates debugging, and improves the overall maintainability of the code.

Type hints in Python use annotations for function parameters, return values and variable assignments.

Python'stype hints can be used to specify a wide variety of types such as basicdata types, collections, complex types and custom user-defined types. Thetyping module provides many built-in types to represent these various types −

Let's see each one, one after another in detail.

Basic Data Types

In Python when usingtype hints to specify basic types we can simply use the name of the type as the annotation.

Example

Following is the example of using the basic data types such as integer, float, string etc −

from typing import Optional# Integer typedef calculate_square_area(side_length: int) -> int:   return side_length ** 2# Float typedef calculate_circle_area(radius: float) -> float:   return 3.14 * radius * radius# String typedef greet(name: str) -> str:   return f"Hello, {name}"# Boolean typedef is_adult(age: int) -> bool:   return age >= 18# None typedef no_return_example() -> None:   print("This function does not return anything")# Optional type (Union of int or None)def safe_divide(x: int, y: Optional[int]) -> Optional[float]:   if y is None or y == 0:      return None   else:      return x / y# Example usageprint(calculate_square_area(5))        print(calculate_circle_area(3.0))     print(greet("Alice"))                 print(is_adult(22))                   no_return_example()                   print(safe_divide(10, 2))             print(safe_divide(10, 0))             print(safe_divide(10, None))

On executing the above code we will get the followingoutput −

2528.259999999999998Hello, AliceTrueThis function does not return anything5.0NoneNone

Collections Types

In Python when dealing with collections such aslists,tuples,dictionaries, etc. intype hints we typically use thetyping module to specify the collection types.

Example

Below is the example of the Collections using intype hints −

from typing import List, Tuple, Dict, Set, Iterable, Generator# List of integersdef process_numbers(numbers: List[int]) -> List[int]:   return [num * 2 for num in numbers]# Tuple of floatsdef coordinates() -> Tuple[float, float]:   return (3.0, 4.0)# Dictionary with string keys and integer valuesdef frequency_count(items: List[str]) -> Dict[str, int]:   freq = {}   for item in items:      freq[item] = freq.get(item, 0) + 1   return freq# Set of unique characters in a stringdef unique_characters(word: str) -> Set[str]:   return set(word)# Iterable of integersdef print_items(items: Iterable[int]) -> None:   for item in items:      print(item)# Generator yielding squares of integers up to ndef squares(n: int) -> Generator[int, None, None]:   for i in range(n):      yield i * i# Example usagenumbers = [1, 2, 3, 4, 5]print(process_numbers(numbers))                   print(coordinates())                            items = ["apple", "banana", "apple", "orange"]print(frequency_count(items))                    word = "hello"print(unique_characters(word))                   print_items(range(5))                           gen = squares(5)print(list(gen))

On executing the above code we will get the followingoutput −

[2, 4, 6, 8, 10](3.0, 4.0){'apple': 2, 'banana': 1, 'orange': 1}{'l', 'e', 'h', 'o'}01234[0, 1, 4, 9, 16]

Optional Types

In Python,Optional types are used to indicate that a variable can either be of a specified type or None. This is particularly useful when a function may not always return a value or when a parameter can accept a value or be left unspecified.

Example

Here is the example of using theoptional types intype hints −

from typing import Optionaldef divide(a: float, b: float) -> Optional[float]:   if b == 0:      return None   else:      return a / bresult1: Optional[float] = divide(10.0, 2.0)   # result1 will be 5.0result2: Optional[float] = divide(10.0, 0.0)   # result2 will be Noneprint(result1)  print(result2)

On executing the above code we will get the followingoutput −

5.0None

Union Types

Python uses Union types to allow a variable to accept values of different types. This is useful when a function or data structure can work with various types of inputs or produce different types of outputs.

Example

Below is the example of this −

from typing import Uniondef square_root_or_none(number: Union[int, float]) -> Union[float, None]:   if number >= 0:      return number ** 0.5   else:      return Noneresult1: Union[float, None] = square_root_or_none(50)   result2: Union[float, None] = square_root_or_none(-50)  print(result1)  print(result2)

On executing the above code we will get the followingoutput −

7.0710678118654755None

Any Type

In Python,Any type is a special type hint that indicates that a variable can be of any type. It essentially disables type checking for that particular variable or expression. This can be useful in situations where the type of a value is not known beforehand or when dealing with dynamic data.

Example

Following is the example of using Any type inType hint −

from typing import Anydef print_value(value: Any) -> None:   print(value)print_value(10)         print_value("hello")    print_value(True)       print_value([1, 2, 3])  print_value({'key': 'value'})

On executing the above code we will get the followingoutput −

10helloTrue[1, 2, 3]{'key': 'value'}

Type Aliases

Type aliases in Python are used to give alternative names to existing types. They can make code easier to read by giving clear names to complicated type annotations or combinations of types. This is especially helpful when working with nested structures or long-type hints.

Example

Below is the example of using theType Aliases in theType hints −

from typing import List, Tuple# Define a type alias for a list of integersVector = List[int]# Define a type alias for a tuple of coordinatesCoordinates = Tuple[float, float]# Function using the type aliasesdef scale_vector(vector: Vector, factor: float) -> Vector:    return [int(num * factor) for num in vector]def calculate_distance(coord1: Coordinates, coord2: Coordinates) -> float:   x1, y1 = coord1   x2, y2 = coord2   return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5# Using the type aliasesv: Vector = [1, 2, 3, 4]scaled_v: Vector = scale_vector(v, 2.5)print(scaled_v)  c1: Coordinates = (3.0, 4.0)c2: Coordinates = (6.0, 8.0)distance: float = calculate_distance(c1, c2)print(distance)

On executing the above code we will get the followingoutput −

[2, 5, 7, 10]5.0

Generic Types

Generic types create functions, classes or data structures that can handle any type while maintaining type safety. The typing module's TypeVar and Generic constructs make this possible. They are helpful for making reusable components that can work with various types without compromising type checking.

Example

Here is the example of it −

from typing import TypeVar, List# Define a type variable TT = TypeVar('T')# Generic function that returns the first element of a listdef first_element(items: List[T]) -> T:   return items[0]# Example usageint_list = [1, 2, 3, 4, 5]str_list = ["apple", "banana", "cherry"]first_int = first_element(int_list)      # first_int will be of type intfirst_str = first_element(str_list)      # first_str will be of type strprint(first_int)    print(first_str)

On executing the above code we will get the followingoutput −

1apple

Callable Types

Python'sCallable type is utilized to show that a type is a function or a callable object. It is found in the typing module and lets you define the types of the arguments and the return type of a function. This is handy for higher-order functions.

Example

Following is the example of using Callable type intype hint −

from typing import Callable# Define a function that takes another function as an argumentdef apply_operation(x: int, y: int, operation: Callable[[int, int], int]) -> int:   return operation(x, y)# Example functions to be passed as argumentsdef add(a: int, b: int) -> int:   return a + bdef multiply(a: int, b: int) -> int:   return a * b# Using the apply_operation function with different operationsresult1 = apply_operation(5, 3, add)        # result1 will be 8result2 = apply_operation(5, 3, multiply)   # result2 will be 15print(result1)  print(result2)

On executing the above code we will get the followingoutput −

815

Literal Types

The Literal type is used to specify that a value must be exactly one of a set of predefined values.

Example

Below is the example −

from typing import Literaldef move(direction: Literal["left", "right", "up", "down"]) -> None:   print(f"Moving {direction}")move("left")  # Validmove("up")    # Valid

On executing the above code we will get the followingoutput −

Moving leftMoving up

NewType

NewType is a function in the typing module that allows us to create distinct types derived from existing ones. This can be useful for adding type safety to our code by distinguishing between different uses of the same underlying type. For example we might want to differentiate between user IDs and product IDs even though both are represented as integers.

Example

Below is the example −

from typing import NewType# Create new typesUserId = NewType('UserId', int)ProductId = NewType('ProductId', int)# Define functions that use the new typesdef get_user_name(user_id: UserId) -> str:   return f"User with ID {user_id}"def get_product_name(product_id: ProductId) -> str:   return f"Product with ID {product_id}"# Example usageuser_id = UserId(42)product_id = ProductId(101)print(get_user_name(user_id))   # Output: User with ID 42print(get_product_name(product_id))  # Output: Product with ID 101

On executing the above code we will get the followingoutput −

User with ID 42Product with ID 101
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