typing

Typing

Generic Types

What are generic types?

Generic types are like placeholders for any type of data. You can think of them as boxes that can hold any type of object, like a string, a number, or even another generic type.

Why use generic types?

Generic types are useful because they allow you to write code that can work with different types of data without having to rewrite it for each type. For example, you could have a sorting function that takes a list of any type of data and sorts it.

How to declare a generic type

You can declare a generic type by adding a list of type parameters after the class name. For example:

class Box[T]:
    def __init__(self, value: T):
        self.value = value

This code defines a generic class called Box that can hold any type of data. The T in the class name is the type parameter.

How to use a generic type

You can use a generic type by passing the type parameter to the class constructor. For example:

box = Box(10)

This code creates a Box object that holds the value 10.

Generic Functions

What are generic functions?

Generic functions are like generic classes, but they are functions instead of classes. They can take any type of data as arguments and return any type of data.

How to declare a generic function

You can declare a generic function by adding a list of type parameters after the function name. For example:

def max[T](a: T, b: T) -> T:
    return a if a > b else b

This code defines a generic function called max that takes two arguments of the same type and returns the larger one.

How to use a generic function

You can use a generic function by passing the type parameter to the function call. For example:

maximum = max(10, 20)

This code calls the max function with the type parameter int and passes the values 10 and 20. The function returns the larger value, which is 20.

Real-World Examples

Sorting a list of any type: You could use a generic sorting function to sort a list of any type of data, such as a list of strings, numbers, or even a list of other lists.
Creating a dictionary of any type: You could use a generic dictionary class to create a dictionary that can store any type of key and value, such as a dictionary of strings to numbers, numbers to strings, or even a dictionary of lists to lists.
Writing a function that works with any type of data: You could use a generic function to write a function that can perform any type of operation on any type of data, such as a function that adds two numbers, concatenates two strings, or compares two values of any type.

Applications in Real World

Generic types and functions are used extensively in many real-world applications, including:

Data science: Generic types and functions are used in data science libraries to handle data of different types, such as numerical data, categorical data, and time series data.
Machine learning: Generic types and functions are used in machine learning algorithms to handle different types of data, such as training data, test data, and model parameters.
Web development: Generic types and functions are used in web development frameworks to handle different types of data, such as user input, database queries, and API responses.
Game development: Generic types and functions are used in game development engines to handle different types of data, such as player data, level data, and game assets.

Type Variables

Type variables are a way to represent generic types, which are types that can work with any value. For example, a list or tuple can hold any type of value, so we can say it has a type variable T.

We use type variables in code like this:

def double(x: T) -> T:
    return x * 2

This function can double any type of value, such as integers, strings, or lists.

Bound Type Variables

Bound type variables are restricted to a specific type or set of types. For example, we can create a type variable S that is bound to the type str:

def upper(x: S) -> S:
    return x.upper()

This function can only be used on strings, because its type variable S is bound to str.

Constrained Type Variables

Constrained type variables are restricted to a set of types that meet some criteria. For example, we can create a type variable T that is constrained to be any type that has a len() method:

from typing import TypeVar, Callable

T = TypeVar('T', bound=Callable[[], int])

def len_checker(x: T) -> int:
    return len(x)

This function can be used on any type that has a len() method, such as strings, lists, or tuples.

Real-World Applications

Type variables are used in many different ways in real-world code. Here are a few examples:

Generic data structures: Data structures like lists and dictionaries can use type variables to represent the type of data they store. This allows them to work with any type of data, without having to be specifically defined for each type.
Generic functions: Functions like double() and upper() can use type variables to represent the type of value they operate on. This allows them to be used with any type of value, without having to be rewritten for each type.
Mixins: Mixins are classes with no state that add functionality to other classes. They can use type variables to represent the type of class they are mixed in with. This allows mixins to be used with any type of class, without having to be rewritten for each type.

Conclusion

Type variables are a powerful tool for writing generic code in Python. They allow us to create code that can work with any type of value, without having to be specifically defined for each type. This makes code more reusable and maintainable.

**Attribute: **name****

Simplified Explanation:

The __name__ attribute of a type variable represents the name of the variable. This name is used to refer to the type variable within type annotations and other code.

Detailed Explanation:

Type variables are used to represent generic types, such as lists, dictionaries, and classes. When defining a type variable, you can specify a name for it, using the __name__ attribute. This name is then used to refer to the type variable throughout your code.

For example, consider the following code that defines a generic function my_function that takes a list of values of a generic type T:

from typing import TypeVar, List

T = TypeVar('T')

def my_function(values: List[T]) -> T:
    ...

In this code, we have defined a type variable T with the name 'T'. We then use this type variable in the type annotation for the values parameter and the return type of the function. This indicates that the function can take a list of values of any type, and will return a value of the same type.

The __name__ attribute of the type variable can be accessed using the .__name__ syntax. For example, we could print the name of the type variable T using the following code:

print(T.__name__)  # Output: 'T'

Real-World Applications:

Type variables are commonly used in generic functions, classes, and other code structures that can work with different types of data. For example, a generic sorting algorithm could take a list of any type of elements and sort them in ascending order. The __name__ attribute allows you to refer to the type variable by name, which can be useful for debugging or other purposes.

**covariant** indicates whether a type variable has been explicitly marked as covariant using the syntax TypeVar(..., covariant=True).

Example:

from typing import TypeVar

T = TypeVar("T", covariant=True)  # Declare a covariant type variable T

def my_function(x: T) -> T:
    return x  # The return type of my_function is covariant with respect to T

Real-World Application:

Example:

class Animal:
    def __init__(self, name):
        self.name = name

class Dog(Animal):
    def __init__(self, name, breed):
        super().__init__(name)
        self.breed = breed

T = TypeVar("T", covariant=True)

def get_animal_name(animal: T) -> str:
    return animal.name

dog = Dog("Buddy", "Golden Retriever")
animal_name = get_animal_name(dog)  # This is valid because Dog is covariant with Animal

In this example, get_animal_name is defined to accept any type that is covariant with Animal. Since Dog is a subclass of Animal, Dog is covariant with Animal, and the function call is valid. This allows us to write code that works with different subclasses of Animal without having to repeat code for each subclass.

**contravariant attribute**

The **contravariant** attribute of a type variable indicates whether the type variable has been explicitly marked as contravariant.

Contravariance is a property of a type that means that it can be used in a more general context without changing its meaning. For example, a function that takes a list of strings can also take a list of any other type that is a subtype of string, such as a list of characters.

In Python, type variables can be marked as contravariant using the @contravariant decorator. For example:

from typing import TypeVar, contravariant

T = TypeVar("T", contravariant=True)

This indicates that the type variable T can only be used in contravariant positions. For example, it can be used as the type of a function parameter, but not as the type of a function return value.

Real-world applications

Contravariance is often used in situations where you want to be able to use a more general type in a more specific context. For example, a function that takes a list of strings can also take a list of any other type that is a subtype of string, such as a list of characters. This allows you to write more flexible code that can be used in a wider variety of situations.

**Attribute: **infer_variance****

Explanation:

Imagine a type variable as a box that can store any type of value. Sometimes, we want type checkers to guess whether this box can only hold values of a certain type (e.g., a box that can only store integers). This attribute tells type checkers whether or not they can make such assumptions.

Default Value:

False

Options:

True: Type checkers are allowed to infer the variance of the type variable.
False: Type checkers are not allowed to infer the variance of the type variable.

Example:

from typing import TypeVar, __infer_variance__

# Create a type variable with variance inference enabled
T = TypeVar('T', bound=int, __infer_variance__=True)

# Use the type variable in a function that expects a list of integers
def my_function(lst: list[T]) -> None:
    for item in lst:
        print(item)  # Will only print integers

# Create a type variable with variance inference disabled
U = TypeVar('U', bound=int, __infer_variance__=False)

# Use the type variable in a function that expects a list of any type
def my_other_function(lst: list[U]) -> None:
    for item in lst:
        print(item)  # Could print any type of value

Real-World Applications:

Performance optimization: By allowing type checkers to infer variance, you can reduce unnecessary type annotations and improve code readability.
Code correctness: In some cases, inferring variance can help type checkers identify potential type errors.

Type Variables with Bounds:

Explanation:

A type variable is like a placeholder that can represent different types. A bound on a type variable restricts what types it can represent. For example, a type variable T could be bound to only represent numeric types, like int or float.

Simplified Example:

Imagine you have a function that takes a list of numbers and returns the sum. You want to make sure that the function only accepts lists of numbers, not strings or other types. You can use a type variable with a bound to ensure this:

def sum_numbers(numbers: list[int]) -> int:
    """Returns the sum of a list of numbers."""
    total = 0
    for number in numbers:
        total += number
    return total

In this example, the type variable T is bound to the int type. This means that sum_numbers can only be called with lists of integers.

Lazy Evaluation:

For type variables created using type parameter syntax (e.g., T = TypeVar('T')), the bound is not evaluated when the type variable is created. Instead, it's evaluated only when the __bound__ attribute is accessed. This helps improve performance by avoiding unnecessary computations.

Real-World Applications:

Type variables with bounds are useful for:

Defining generic functions and classes that can work with different types.
Ensuring that functions and methods receive the expected types of arguments.
Improving type safety and reducing errors in code.

Attribute: __constraints__

Type: Tuple

Purpose: Contains the constraints of the type variable, if any.

Version Introduced: 3.12

How it Works:

A type variable is like a placeholder that represents a type. It allows you to define general functions or classes that can work with different types without specifying them explicitly. Constraints are like rules that restrict the values that the type variable can represent.

For example, you might have a function that takes a type variable as a parameter, and you want to make sure that the parameter can only be used to represent types that are subclasses of a certain class. You can do this by specifying a constraint on the type variable.

Lazy Evaluation:

In Python 3.12, the constraints for type variables created using the type parameter syntax are not evaluated until the __constraints__ attribute is accessed. This means that the constraints are not checked until you actually use them, which can improve performance.

Type Parameter Syntax:

def my_function(T: type[int | str]) -> T:
    ...

In this example, T is a type variable that is constrained to be either int or str. The type parameter syntax is a new way to define type variables introduced in Python 3.12.

Real-World Applications:

Type variables and constraints are used in various real-world applications, such as:

Generic functions: Functions that can work with different types without specifying them explicitly.
Generic classes: Classes that can represent different types of data.
Type checking: Ensuring that the types of variables and expressions are correct and consistent.

Example:

from typing import TypeVar, Tuple

# Define a type variable constrained to be either int or str.
T = TypeVar("T", int, str)

# Define a generic function that takes a type variable as a parameter.
def my_function(value: T) -> T:
    return value

# Call the function with different types.
result1 = my_function(10)
result2 = my_function("Hello")

# Print the results.
print(result1)  # 10
print(result2)  # Hello

Type Variable Tuples

Explanation: Imagine you have a function that takes a tuple and does something with it. You want to make the function work for tuples of any length and with any types of elements. This is where type variable tuples come in.

Example:

def move_first_element_to_last(tup):
    return (*tup[1:], tup[0])

In this function, tup is annotated as a tuple of any type and any length. tup[1:] extracts all the elements except the first one, and tup[0] gets the first element. Then, it puts the rest of the elements first, followed by the first element.

Benefits:

Flexibility: You can pass tuples of any length and type and the function will handle them.
Generic: Anyone can use this function regardless of the specific data types they're dealing with.

Real-World Application: Suppose you have a list of employees and want to sort them alphabetically. You could use a type variable tuple to create a sorting function that works for any number of employees and any order of names.

Usage: To use a type variable tuple, you can either:

Define it with a single asterisk before the name, like *Ts.
Use the TypeVarTuple constructor, like Ts = TypeVarTuple("Ts").

Important:

Type variable tuples must always be unpacked (prefixed with *).
Only one type variable tuple can appear in a single list of type arguments or type parameters.

Additional Examples:

Class with Type Variable Tuple:

class Array[*Shape]:
    def __getitem__(self, key):
        pass

This class represents an array of any shape. The *Shape tuple allows you to specify the dimensions of the array.

Function with Type Variable Tuple as *args:

def call_soon(*args):
    pass

This function can accept any number of arguments of any type and ensures that the types of the arguments match the types of the callback function's positional arguments.

Conclusion: Type variable tuples provide a powerful way to create flexible and generic functions and classes that can handle data of varying types and lengths.

Type Variable Tuples

In Python, type variables are used to represent unknown types. A type variable tuple is a tuple of type variables. For example, the following code declares a type variable tuple called T:

from typing import TypeVar

T = TypeVar("T")

You can then use T to represent any type:

def func(arg: T) -> T:
    return arg

This function can be used to work with any type, regardless of what it is.

Real-World Applications

Type variable tuples are useful in a variety of situations. For example, they can be used to:

Define generic functions and classes that can work with any type.
Represent complex types that cannot be expressed using simple types.
Create type annotations that are more precise.

Complete Code Example

The following code shows a complete example of how to use type variable tuples:

from typing import TypeVar

T = TypeVar("T")
U = TypeVar("U")

def func(arg1: T, arg2: U) -> T:
    return arg1

print(func(1, "hello"))  # 1

Output

In this example, the function func is generic and can work with any two types. In this case, the types of the arguments are int and str. The return value of the function is also int.

Conclusion

Type variable tuples are a powerful tool that can be used to represent complex and generic types. They are a valuable addition to the Python typing module.

Parameter Specification Variables (ParamSpec)

What are ParamSpec Variables?

ParamSpecs are like placeholders for the types of parameters in functions or classes. They allow type checkers to understand how the parameters of two functions or classes are related.

Why Use ParamSpec Variables?

ParamSpecs help type checkers understand the types of parameters in decorators. A decorator is a function that modifies another function. By using a ParamSpec, the type checker can understand how the decorated function's parameters relate to the parameters of the decorator function.

Example of Using ParamSpec Variables

Here's a simple example of using a ParamSpec variable in a decorator:

from typing import ParamSpec, Callable

P = ParamSpec("P")

def add_logging(f: Callable[P, int]) -> Callable[P, int]:
    def wrapper(*args, **kwargs):
        print(f"Logging call to {f.__name__}")
        return f(*args, **kwargs)
    return wrapper

@add_logging
def add_two(x: int, y: int) -> int:
    return x + y

In this example, the add_logging decorator uses a ParamSpec variable P to represent the type of the parameters of the decorated function. The type checker can then understand that the parameters of the decorated function add_two are of type int.

Real-World Applications

ParamSpecs can be used in any situation where you need to pass a function as an argument to another function, and the type checker needs to understand the relationship between the parameters of the two functions. Some real-world applications include:

Logging decorators
Caching decorators
Profiling decorators

Improved Version of Code Snippet

Here's an improved version of the code snippet above that uses a more descriptive ParamSpec variable name:

from typing import ParamSpec, Callable

ParamSpec("FnParams")

def add_logging(f: Callable[..., int]) -> Callable[..., int]:
    def wrapper(*args, **kwargs):
        print(f"Logging call to {f.__name__}")
        return f(*args, **kwargs)
    return wrapper

@add_logging
def add_two(x: int, y: int) -> int:
    return x + y

Attribute in Python's typing module is used to add metadata to a variable, class, or function. This metadata can be used for documentation, type checking, or other purposes.

Syntax

@attribute(name, value)
def function():
    pass

name: The name of the attribute.
value: The value of the attribute.

Example

The following example adds a "description" attribute to a function.

@attribute("description", "This function prints a greeting.")
def greet(name):
    print(f"Hello, {name}!")

Real-World Applications

Attributes can be used in a variety of ways, including:

Documentation: Attributes can be used to provide documentation for variables, classes, and functions. This documentation can be accessed using the help() function.

>>> help(greet)
Help on function greet in module __main__:

greet(name)
    This function prints a greeting.

Type Checking: Attributes can be used to specify the type of a variable, class, or function. This information can be used by type checkers to ensure that your code is correct.

@attribute("type", str)
def get_name():
    return "John Doe"

Other Purposes: Attributes can also be used for other purposes, such as tracking the history of a variable or class.

Potential Applications

Attributes have a wide range of potential applications, including:

Documentation: Attributes can be used to provide documentation for your code, making it easier for other developers to understand and use.
Type Checking: Attributes can be used to type-check your code, ensuring that it is correct and reliable.
Code Generation: Attributes can be used to generate code, such as documentation or unit tests.

ParamSpec: Parameter Specifications

A ParamSpec is a way to describe the expected parameters of a function or method. It allows you to specify the names, types, and default values of the parameters.

ParamSpec.args and ParamSpec.kwargs

ParamSpec.args represents the tuple of positional parameters (e.g., *args) in a function call, and ParamSpec.kwargs represents the dictionary of keyword parameters (e.g., **kwargs).

Example:

from typing import ParamSpec

P = ParamSpec("P")  # Create a ParamSpec named "P"

def foo(a: int, b: str, *args: P.args, **kwargs: P.kwargs) -> None:
    pass

In the above example, the foo function takes two positional parameters (a and b), followed by any number of positional parameters (*args, annotated with P.args) and keyword parameters (**kwargs, annotated with P.kwargs).

Real-World Application:

ParamSpecs are useful for documenting the expected parameters of functions and methods, especially when you want to allow for flexible parameter passing. For example, if you have a function that can take any number of strings as input, you can use a ParamSpec to specify that the parameters should be strings. This helps other developers understand the expected format of the function call.

Complete Code Implementation:

from typing import ParamSpec

P = ParamSpec("P")

def concatenate(*args: P.args, sep: str = ",") -> str:
    """
    Concatenate a variable number of strings using a specified separator.

    Args:
        *args: The strings to concatenate.
        sep (str, optional): The separator to use between the strings. Defaults to ",".

    Returns:
        A single string representing the concatenation of the input strings.
    """

    result = ""
    for arg in args:
        result += arg + sep
    return result[:-len(sep)]  # Remove the trailing separator

# Example usage:
print(concatenate("Hello", "World", "!"))

In this example, we use a ParamSpec to specify that the concatenate function can take any number of strings as input. We also specify a keyword-only parameter sep, which allows the user to specify a custom separator.

ParamSpec: Parameter Specification Variables

Simplified Explanation:

Imagine you're building a function that can work with different types of data, like numbers or strings. You want to make sure the function can handle any data type, so you need to specify the types of data it can accept. This is where ParamSpec comes in. It allows you to describe the types of parameters your function can take.

Code Snippet:

from typing import ParamSpec

# Create a parameter specification named "P"
P = ParamSpec("P")

# Define a function that takes parameters of type "P"
def my_function(param: P):
    # Do something with the parameter

Bound, Covariant, and Contravariant:

These are advanced concepts that are still being developed. For now, you can think of them as ways to restrict or extend the types of data that can be assigned to a ParamSpec variable.

ParamSpecArgs and ParamSpecKwargs:

These are attributes of a ParamSpec that represent the positional and keyword arguments that the ParamSpec can accept. They are mainly used for runtime introspection and have no special meaning to type checkers.

Real-World Applications:

Creating generic functions that can handle multiple data types
Defining interfaces for classes and protocols
Validating input parameters to functions and methods

Example:

Here's a complete example of how to create and use a ParamSpec:

from typing import ParamSpec

# Create a parameter specification named "T"
T = ParamSpec("T")

# Define a generic function that takes a parameter of type "T"
def my_generic_function(param: T) -> T:
    return param

# Example usage:
my_generic_function(5)  # Works with integers
my_generic_function("Hello")  # Works with strings
my_generic_function([1, 2, 3])  # Works with lists

TypeAliasType

Simplified Explanation:

A TypeAliasType is a special type created using the type statement in Python. It's like a nickname for an existing type. For example, you can create an alias called Alias for the int type, so Alias behaves just like int.

Technical Details:

name: The name of the alias, such as Alias.
value: The type that the alias refers to, such as int.
type_params (optional): Any type parameters that the alias uses, but this is usually empty.

Example:

# Create a type alias called "Alias" for the "int" type
type Alias = int

# Use the alias like a regular type
number: Alias = 10

Real-World Applications:

Making code more readable and concise by using shorter, more meaningful names for types.
Improving code organization by grouping related types under a single alias.
Creating custom types that combine existing types or add additional functionality.

Type aliases are a way to give a more meaningful name to an existing type. For example, you could create a type alias called UserId to represent the type of a user ID:

UserId = int

Now, you can use UserId instead of int in your code:

def get_user_by_id(user_id: UserId) -> User:
    ...

This makes your code more readable and easier to understand.

Type aliases can also be used to create more complex types. For example, you could create a type alias called MaybeInt to represent a type that can be either an int or None:

MaybeInt = Optional[int]

Now, you can use MaybeInt in your code to represent values that may or may not be integers:

def get_user_age(user_id: UserId) -> MaybeInt:
    ...

Type aliases are a powerful tool that can make your code more readable, understandable, and maintainable. They are especially useful when working with complex types or when you want to give a more meaningful name to an existing type.

Here are some real-world applications of type aliases:

Representing complex types: Type aliases can be used to represent complex types that would otherwise be difficult to read or understand. For example, you could create a type alias called Order to represent the type of an order in an e-commerce system:

Order = Dict[str, int]

Now, you can use Order in your code to represent orders in a more readable and understandable way:

def get_order_total(order: Order) -> float:
    ...

Giving more meaningful names to existing types: Type aliases can be used to give more meaningful names to existing types. For example, you could create a type alias called UserId to represent the type of a user ID:

UserId = int

Now, you can use UserId instead of int in your code to make your code more readable and understandable:

def get_user_by_id(user_id: UserId) -> User:
    ...

Creating generic types: Type aliases can be used to create generic types. For example, you could create a type alias called List[T] to represent a list of elements of type T:

List[T] = list[T]

Now, you can use List[T] in your code to represent lists of any type:

def get_user_names(users: List[User]) -> List[str]:
    ...

**Attribute: **module****

Purpose: This attribute provides information about the module in which the type alias was defined.

Simplified Explanation: Imagine you have different boxes of chocolates, labeled "box_1," "box_2," and so on. Each box contains specific types of chocolate. A type alias is like a shortcut that gives a name to a particular box. For example, you could create an alias called "dark_chocolate" that represents the contents of "box_1."

The __module__ attribute tells you which box, or module, the alias points to. In our example, the alias dark_chocolate would have a __module__ value of "box_1."

Code Example:

from typing import TypeAlias

# Define a module named "chocolate_box"
chocolate_box = TypeAlias("chocolate_list", list[str])

# Create an alias "dark_chocolate" that points to "chocolate_box"
dark_chocolate = chocolate_box

# Check the __module__ attribute of "dark_chocolate"
print(dark_chocolate.__module__)  # Output: "chocolate_box"

Real-World Applications:

Customizing code readability: Type aliases help make code more readable and understandable by providing clear names for common types.
Enforcing data consistency: Aliases can ensure that certain data types are used consistently throughout a codebase, reducing the likelihood of errors.
Creating reusable types: Type aliases allow you to create and reuse custom types across multiple modules.

**Attribute: **type_params****

Simplified Explanation:

Imagine you have a special "type" called ListOrSet. This type allows you to store data in either a list (ordered collection) or a set (unordered collection), but the actual storage is unknown.

The __type_params__ attribute tells you what types can be stored in this special type. In this case, it's a generic placeholder called T. So, ListOrSet[T] can store any data type T, such as numbers, strings, objects, or even other types.

Code Snippet:

from typing import TypeVar, Union

T = TypeVar("T")  # Create a placeholder type variable

ListOrSet = Union[list[T], set[T]]  # Define the type alias

result = ListOrSet[int](range(5))  # Create a ListOrSet of integers
print(type(result))  # Output: <class 'list'>

Real-World Example:

You're building a function to handle a list of expenses. Some expenses are categorized into categories (e.g., "Food", "Entertainment"), while others have no category.

Using ListOrSet, you can define a type that allows you to store both categorized and uncategorized expenses.

from typing import ListOrSet

Expense = ListOrSet[tuple[float, str]]  # A tuple of (amount, category)

expenses: Expense = [
    (10.50, "Food"),
    (15.25, "Entertainment"),
    (8.75),  # Uncategorized expense
]

Applications:

Defining generic data structures that can store different types of data
Creating functions that accept a range of input types
Enforcing type safety in your code to prevent errors and improve code quality

Type Alias

Explanation:

A type alias is a way to create a new name for an existing type. This can be useful to make your code more readable and to avoid repeating yourself.

Example:

MyType = int  # Create an alias named MyType for the int type
number: MyType = 10  # Use MyType as the type of a variable

Lazy Evaluation

Explanation:

When you create a type alias, the value of the alias is not evaluated immediately. Instead, it is evaluated lazily, meaning that it is only evaluated when it is actually needed. This can improve performance in some cases.

Example:

type Mutually = Recursive
type Recursive = Mutually

In this example, the value of the Mutually alias is not evaluated until the __value__ attribute is accessed. This means that the following code will not raise an error, even though Mutually and Recursive are mutually recursive:

Mutually.__value__

Other Special Directives

Explanation:

The following are additional functions and classes that can be used to create and declare types:

NewType: Creates a new type with a different name but the same underlying representation.
Union: Creates a type that can be one of several other types.
Literal: Creates a type that is only valid for a specific set of values.
Final: Creates a type that cannot be subclassed or modified.

Applications

Type aliases and other special directives can be used in a variety of ways to improve the readability and maintainability of your code. Some potential applications include:

Creating custom types for specific domains or use cases.
Refactoring code to use a more consistent and concise type system.
Improving performance by lazily evaluating type expressions.

Complete Code Implementations and Examples

Custom Type:

from typing import NewType

UserId = NewType("UserId", int)  # Create a new type for user IDs

def get_user(user_id: UserId) -> str:
    """Get the user associated with the given user ID."""
    return f"User {user_id}"

Union Type:

from typing import Union

def get_value(key: str) -> Union[int, str]:
    """Get the value associated with the given key from a dictionary."""
    return {"foo": 1, "bar": "two"}[key]

Literal Type:

from typing import Literal

def get_status(status: Literal["active", "inactive"]) -> str:
    """Get a description of the given status."""
    if status == "active":
        return "The status is active."
    elif status == "inactive":
        return "The status is inactive."
    else:
        raise ValueError("Invalid status.")

Final Type:

from typing import Final

class MyClass:
    """A class with a final attribute."""

    NAME: Final[str] = "MyClass"  # Create a final attribute

    def __init__(self, name: str):
        self.name = name

    def get_name(self) -> str:
        """Get the name of the class."""
        return self.name

NamedTuple

A NamedTuple is a special kind of data structure that lets you create a custom type that stores multiple values. It's like a regular tuple, but you can give each value a name.

Usage:

# Create a NamedTuple called "Employee" with fields "name" and "id"
class Employee(NamedTuple):
    name: str  # Name of the employee (a string)
    id: int  # ID of the employee (an integer)

This is equivalent to:

# Create a regular tuple called "Employee" with fields "name" and "id"
Employee = collections.namedtuple('Employee', ['name', 'id'])

Fields with Default Values:

You can also give fields default values by assigning them in the class body:

class Employee(NamedTuple):
    name: str
    id: int = 3  # Default value for the "id" field

Fields with default values must come after fields without default values.

Accessing Values:

You can access the values of a NamedTuple using the dot notation:

employee = Employee('Guido', 123)
print(employee.name)  # Prints "Guido"
print(employee.id)  # Prints 123

Benefits:

Readability: NamedTuples are easier to read and understand than regular tuples, as the field names make it clear what each value represents.
Type Safety: NamedTuples enforce type checking, so you can be confident that the values in the tuple are of the correct type.
Immutability: NamedTuples are immutable, meaning that once they are created, they cannot be changed.

Real-World Examples:

Storing user data: You could use a NamedTuple to store information about a user, such as their name, email, and address.
Representing data from a database: You could use a NamedTuple to represent a row of data from a database, with each field representing a column.
Passing data between functions: You could use a NamedTuple to pass a collection of related data between functions, making it easier to keep track of the data.

NewType

What is it?

A NewType is a way to create a new type that is distinct from other types, even though it 实际上 is just another type.

Why use it?

NewTypes are useful when you want to make it clear that a value has a specific meaning or purpose, even though it's technically the same type as another value.

How to use it:

To create a NewType, you use the NewType class. The first argument is the name of your new type, and the second argument is the underlying type that your new type will be based on.

For example, the following code creates a NewType called UserId that is based on the int type:

from typing import NewType

UserId = NewType('UserId', int)

Once you've created a NewType, you can use it like any other type. For example, you can assign a value to a NewType variable:

first_user = UserId(1)

You can also pass NewType values to functions and methods:

def get_user_by_id(user_id: UserId) -> User:
    ...

Real-world applications:

NewTypes can be used in a variety of real-world applications, such as:

Enforcing data integrity: NewTypes can be used to ensure that data is only used in the ways that it's intended. For example, you could create a NewType called EmailAddress that is only used to store email addresses.
Improving code readability: NewTypes can make your code more readable by making it clear what the purpose of each value is. For example, you could create a NewType called OrderNumber that is used to store order numbers.
Preventing errors: NewTypes can help to prevent errors by making it more difficult to misuse data. For example, you could create a NewType called CreditCardNumber that is only used to store credit card numbers.

Here is a complete code implementation of a NewType for representing user IDs:

from typing import NewType

UserId = NewType('UserId', int)

def get_user_by_id(user_id: UserId) -> User:
    """
    Get a user by their ID.

    Args:
        user_id (UserId): The ID of the user to get.

    Returns:
        User: The user with the given ID.
    """

    ...

def create_user(name: str, email: str) -> UserId:
    """
    Create a new user.

    Args:
        name (str): The name of the new user.
        email (str): The email address of the new user.

    Returns:
        UserId: The ID of the new user.
    """

    ...

This NewType can be used to ensure that user IDs are only used in the ways that they're intended. For example, the following code would raise an error:

user_id = 1
get_user_by_id(user_id)

This error would be raised because the get_user_by_id function expects a UserId argument, not an int.

**Attribute: **module****

Simplified Explanation:

Imagine your favorite ice cream flavor. Each flavor comes from a specific brand, right? Similarly, each new type in Python belongs to a specific module. The __module__ attribute tells you which module the new type is defined in.

Code Snippet:

class MyNewType:
    pass

In this example, we have a new type called MyNewType. To find out which module it belongs to, we can access its __module__ attribute:

print(MyNewType.__module__)  # Output: __main__

Real-World Complete Code Implementation and Examples:

Suppose you have a module named my_module.py with the following code:

# my_module.py
class MyClass:
    pass

Then, in another module, you can create an instance of MyClass and print its __module__ attribute:

# other_module.py
from my_module import MyClass

instance = MyClass()
print(instance.__module__)  # Output: my_module

Potential Applications in Real World:

Module Management: Using the __module__ attribute, you can easily determine which module imported a specific class or function.
Dependency Analysis: By checking the __module__ attribute of various types, you can analyze the dependencies between different modules in your codebase.
Type Identification: You can use the __module__ attribute to distinguish between different types with similar names that may have been defined in different modules.

**Attribute: **name****

Explanation:

When creating a new custom type using the @dataclass decorator, you can specify a __name__ attribute to give the new type a custom name. This name is used to identify the type within your code and in error messages.

Simplified Example:

from dataclasses import dataclass

@dataclass(name="Person")
class Person:
    name: str
    age: int

In this example, the new type Person has been given the custom name "Person".

Real-World Applications:

Creating clear and descriptive type names for improved code readability and understanding.
Distinguishing between different custom types that may have similar functionality but different purposes.

Complete Code Implementation:

from dataclasses import dataclass

@dataclass(name="Person")
class Person:
    name: str
    age: int

person = Person(name="John", age=30)

print(person.name)  # Output: 'John'
print(type(person))  # Output: `<class '__main__.Person'>`

In this example:

We create a custom type Person with the __name__ attribute set to "Person".
We create an instance of the Person type and print its attributes.
We print the type of the person object, which shows the custom name "Person" assigned to the type.

NewType

Simplified Explanation:

NewType allows you to create a new data type that behaves like an existing data type but with a different name. It's like giving your new data type a fancy alias.

Detailed Explanation:

NewType takes two arguments: the name of the new type and the supertype it's based on. For example:

from typing import NewType

UserId = NewType("UserId", int)

Now, UserId is a new data type that's based on int. Any value of type UserId can be used anywhere an int can be used.

Real-World Application:

NewType can be useful when you want to create a data type that has a specific meaning in your application. For example, in a user management system, you could use UserId to represent the unique ID of a user, making it clear that it's not just an ordinary number.

Improved Code Example:

from typing import NewType

UserId = NewType("UserId", int)

def get_user(user_id: UserId) -> None:
    # Code to retrieve a user from the database
    pass

user_id = UserId(1234)
get_user(user_id)

In this example, we create a UserId type and use it in a function that takes a UserId as an argument. This makes it clear that the function expects a unique ID of a user, not just any integer.

Protocol Classes

In Python, we can define a protocol class to specify a set of methods that a class must implement in order to be considered compatible with the protocol.

Example:

# Define a protocol class
class Drawable(Protocol):
    def draw(self):
        ...  # Placeholder for the method implementation

This protocol class defines that any class that wants to be considered "drawable" must implement a draw method.

Using Protocol Classes

Protocol classes can be used to enforce a certain interface on a class. For example:

# Define a class that implements the Drawable protocol
class Rectangle:
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def draw(self):
        print(f"Drawing a rectangle with width {self.width} and height {self.height}")

# Create a Rectangle instance and draw it
rect = Rectangle(10, 5)
rect.draw()  # Output: Drawing a rectangle with width 10 and height 5

Generic Protocol Classes

Protocol classes can also be generic, meaning they can take type parameters. For example:

# Define a generic protocol class
class Container(Protocol[T]):
    def add(self, item: T):
        ...  # Placeholder for the method implementation

    def remove(self, item: T):
        ...  # Placeholder for the method implementation

This protocol class defines that any class that wants to be considered a "container" must implement add and remove methods that operate on items of a specific type T.

Example:

# Define a class that implements the Container protocol
class ListContainer:
    def __init__(self):
        self.items = []

    def add(self, item):
        self.items.append(item)

    def remove(self, item):
        self.items.remove(item)

# Create a ListContainer instance
container = ListContainer()

# Add and remove items to the container
container.add(10)
container.remove(10)

Potential Applications

Protocol classes have many applications in real-world code:

Static type checking: Protocol classes can be used to ensure that classes conform to a certain interface.
Duck typing: Protocol classes can be used to check if a class has the required methods, even if it doesn't explicitly inherit from the protocol class.
Code organization: Protocol classes can help organize code by grouping related methods and interfaces.

Introducing runtime_checkable Decorator in Python

1. What is runtime_checkable?

Imagine you have a set of rules that describe what a "Closable" object should have or do. You can define a "Closable" protocol to represent these rules using Python's @runtime_checkable decorator.

2. Using runtime_checkable:

@runtime_checkable
class Closable:
    def close(self):
        ...

Now, let's create a File object that follows the "Closable" rules:

class File:
    def close(self):
        print("Closing the file...")

file1 = File()
assert isinstance(file1, Closable)  # True, it has the "close" method

3. Benefits of runtime_checkable:

You can now use isinstance to check if an object meets the "Closable" criteria:

if isinstance(file1, Closable):
    file1.close()  # Calls the "close" method

4. Real-World Application:

You can ensure that you interact with objects that have specific capabilities.
It helps in building flexible systems that can accept objects based on their behavior, not their type.

Example:

# A generic function that takes any object that can be "closed"
def close_it(closable: Closable):
    closable.close()

# Pass different types of objects that implement "close"
close_it(file1)
close_it(open("another_file.txt"))

5. Considerations:

runtime_checkable checks only for the presence of methods or attributes, not their types or values.
isinstance checks against runtime-checkable protocols can be slow compared to regular isinstance checks.

6. Alternative Idioms:

For performance-sensitive code, consider using hasattr calls instead of isinstance for structural checks.

What is a TypedDict?

A TypedDict is a special type of dictionary in Python that allows you to define the expected keys and their types. This means that when you create a TypedDict, you must specify the keys that it will have and the types of values that those keys can hold.

Why use a TypedDict?

TypedDicts can be useful for several reasons:

Improved type safety: TypedDicts help ensure that the keys and values in a dictionary are of the correct types. This can prevent errors from occurring when you access or modify the dictionary.
Improved code readability: TypedDicts make it easier to understand the structure and expected values of a dictionary. This can be helpful when you are reading or writing code that uses dictionaries.
Easier to maintain: TypedDicts can help you keep your code consistent by ensuring that dictionaries are always created with the same keys and types. This can make it easier to maintain your code over time.

Creating a TypedDict

You can create a TypedDict using two different syntaxes:

Class-based syntax:

class Point2D(TypedDict):
    x: int
    y: int
    label: str

Function-call syntax:

Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})

Accessing TypedDict values

You can access values in a TypedDict just like you would in a regular dictionary:

point = Point2D(x=1, y=2, label='good')
print(point['x'])  # prints 1

Modifying TypedDict values

You can modify values in a TypedDict just like you would in a regular dictionary:

point['label'] = 'bad'

Type checking TypedDict values

TypedDicts support type checking, which means that you can use type checkers (such as mypy) to verify that the values in a TypedDict are of the correct types.

For example, the following code will fail type checking because the value assigned to the label key is not a string:

point = Point2D(x=1, y=2, label=123)

Real-world examples of TypedDict

TypedDicts can be used in a variety of real-world applications, such as:

Data validation: TypedDicts can be used to validate data that is input into a program. This can help ensure that the data is in the correct format and that it contains the expected values.
Data serialization: TypedDicts can be used to serialize data into a format that can be easily stored or transmitted. This can be useful for storing data in a database or for sending data over a network.
Code generation: TypedDicts can be used to generate code that interacts with dictionaries. This can be useful for creating code that can read and write dictionaries in a consistent manner.

Conclusion

TypedDicts are a powerful tool that can help you improve the type safety, readability, and maintainability of your code. They are a great way to define and work with dictionaries in Python.

**total Attribute for TypedDicts in Python**

Explanation:

A TypedDict is a special kind of dictionary in Python that allows you to define the types of keys and values it can contain.
The __total__ attribute of a TypedDict indicates whether the TypedDict is considered "total" or not.

Total vs. Non-Total TypedDicts:

Total TypedDict: All keys defined in the TypedDict are required. This means that when you create an instance of the TypedDict, you must specify values for all the keys.
Non-Total TypedDict: Some keys in the TypedDict may be optional. This means that when you create an instance of the TypedDict, you can leave out values for optional keys.

How to Set __total__:

You can set the __total__ attribute when you define a TypedDict:

from typing import TypedDict

class Point2D(TypedDict):
    x: int
    y: int
    __total__: True  # Set to True to make the TypedDict total

Using __total__ for Introspection:

You can use the __total__ attribute to check whether a TypedDict is total or not:

if Point2D.__total__:
    print("Point2D is a total TypedDict.")
else:
    print("Point2D is not a total TypedDict.")

Real-World Applications:

Total TypedDicts can be used to represent data structures where all fields are required. For example, a record in a database.
Non-Total TypedDicts can be used to represent data structures where some fields may be missing or optional. For example, a user profile where some fields may be left empty.

Improved Code Example:

from typing import TypedDict, NotRequired

# Total TypedDict with all required keys
class Point3D(TypedDict):
    x: int
    y: int
    z: int
    __total__: True

# Non-Total TypedDict with optional keys
class User(TypedDict):
    name: str
    email: str
    age: int
    address: str  # Optional key
    __total__: False

# Create instances of the TypedDicts
point3d = Point3D(x=1, y=2, z=3)
user = User(name="Alice", email="alice@example.com", age=25)

# Check if the TypedDicts are total
print(Point3D.__total__)  # True
print(User.__total__)  # False

# Access values from the TypedDicts
print(point3d["x"], point3d["y"], point3d["z"])  # 1, 2, 3
print(user["name"], user["email"], user["age"])  # Alice, alice@example.com, 25

# Optional keys cannot be accessed if not provided
print(user.get("address"))  # None

**Attribute: **required_keys****

Simplified Explanation:

The __required_keys__ attribute is a special attribute that you can add to a class to specify which keys are required when creating objects of that class.

Version:

This attribute was added in Python 3.9.

Code Snippet:

# Create a class with required keys
class MyClass:
    __required_keys__ = ["name", "age"]  # The required keys

    def __init__(self, **kwargs):
        # Validate the required keys are present
        for key in self.__required_keys__:
            if key not in kwargs:
                raise TypeError(f"Missing required key: {key}")

        # Initialize the object
        for key, value in kwargs.items():
            setattr(self, key, value)

# Create an object of the class
obj = MyClass(name="John", age=30)

Real-World Application:

Enforcing data integrity by ensuring that certain keys are always provided when creating objects.
Simplifying data validation by centralizing the required key validation in one place.

Example Implementation:

# A simple data parsing class
class DataParser:
    __required_keys__ = ["header", "data"]

    def __init__(self, **kwargs):
        for key in self.__required_keys__:
            if key not in kwargs:
                raise TypeError(f"Missing required key: {key}")

        self.header = kwargs["header"]
        self.data = kwargs["data"]

def main():
    # Create a data parser with the required data
    parser = DataParser(header="Column Names", data=[[1, 2, 3], [4, 5, 6]])

    # Process the data
    for row in parser.data:
        print(row)

if __name__ == "__main__":
    main()

TypedDict

A TypedDict is like a regular Python dictionary, but with a fixed set of key-value pairs.
Each key must have a specific type, and the value for each key must also have a specific type.
You can use a TypedDict to enforce the structure of your data.
TypedDicts are useful for representing data that comes from a fixed source, such as a database or API.

Required and Optional Keys

A TypedDict can have both required and optional keys.
Required keys must always be present in the TypedDict, while optional keys may or may not be present.
You can use the __required_keys__ and __optional_keys__ attributes to get a list of the required and optional keys in a TypedDict.

Example

Here is an example of a TypedDict that represents a point in 2D space:

Point2D = TypedDict('Point2D', {'x': int, 'y': int})

This TypedDict has two required keys: 'x' and 'y'. The value for 'x' must be an integer, and the value for 'y' must also be an integer.

Here is an example of how to use this TypedDict:

point = Point2D(x=10, y=20)
print(point['x'])  # 10
print(point['y'])  # 20

Real-World Applications

TypedDicts can be used in a variety of real-world applications, such as:

Validating input data.
Representing data from a database or API.
Enforcing the structure of a configuration file.

Protocols

Protocols are like interfaces in other programming languages.
They define a set of methods that a class must implement.
You can use protocols to check if a class implements a specific set of methods.

Example

Here is an example of a protocol that defines a set of methods for a class that can be used to compare objects:

from typing import Protocol

class Comparable(Protocol):
    def __eq__(self, other: Any) -> bool: ...
    def __lt__(self, other: Any) -> bool: ...
    def __gt__(self, other: Any) -> bool: ...

This protocol defines three methods: __eq__, __lt__, and __gt__. These methods are used to compare objects for equality, less than, and greater than, respectively.

Here is an example of a class that implements this protocol:

class Point2D:
    def __init__(self, x: int, y: int):
        self.x = x
        self.y = y

    def __eq__(self, other: Any) -> bool:
        if not isinstance(other, Point2D):
            return False
        return self.x == other.x and self.y == other.y

    def __lt__(self, other: Any) -> bool:
        if not isinstance(other, Point2D):
            return False
        return self.x < other.x and self.y < other.y

    def __gt__(self, other: Any) -> bool:
        if not isinstance(other, Point2D):
            return False
        return self.x > other.x and self.y > other.y

This class implements the Comparable protocol by defining the three required methods.

Real-World Applications

Protocols can be used in a variety of real-world applications, such as:

Checking if a class implements a specific set of methods.
Creating generic functions that can work with any class that implements a specific set of methods.

An Abstract Base Class (ABC)

Imagine a blueprint for a house. An ABC is like a blueprint for a class. It defines what methods and attributes a class must have, but it doesn't actually create the class itself.

Covariance in Return Type

A method is covariant in its return type if it can return a more specific type of object than the type it's declared to return. For example, a method that's declared to return a Shape object could actually return a Circle object, which is a more specific type of shape.

The SupportsAbs ABC

The SupportsAbs ABC represents classes that have an __abs__ method. This method is used to get the absolute value of an object.

Real-World Example

Here's a simplified example of using the SupportsAbs ABC:

from typing import SupportsAbs

class ComplexNumber(SupportsAbs):
    def __init__(self, real, imaginary):
        self.real = real
        self.imaginary = imaginary

    def __abs__(self):
        return (self.real**2 + self.imaginary**2)**0.5

# Create a complex number
complex_number = ComplexNumber(3, 4)

# Get the absolute value using the __abs__ method
absolute_value = abs(complex_number)

# Print the absolute value
print(absolute_value)

Output:

5.0

Potential Applications

The SupportsAbs ABC can be used in any situation where you need to work with objects that have an absolute value. For example:

In physics, the absolute value of a vector represents its magnitude.
In mathematics, the absolute value of a number represents its distance from zero.
In computer graphics, the absolute value of a color value represents its intensity.

Abstract Base Class (ABC)

An ABC is a special kind of class that defines a set of methods that subclasses must implement.
They are used to ensure that subclasses have the necessary methods to perform their intended functions.

SupportsBytes

SupportsBytes is an ABC that requires subclasses to define a __bytes__ method.
The __bytes__ method returns a byte representation of the object.

Simplified Explanation:

Imagine a class of vehicles. You can define an ABC called Vehicle that requires all vehicles to have a drive() method. This ensures that any car, truck, or motorcycle that inherits from Vehicle can be driven.

Similarly, SupportsBytes is an ABC that requires all subclasses to have a __bytes__ method. This ensures that any object that inherits from a class that supports bytes can be converted to a byte representation.

Real-World Example:

import io

class MyObject:
    def __bytes__(self):
        return b"Hello, world!"

# Create an instance of MyObject
obj = MyObject()

# Convert obj to a byte representation
bytes_obj = bytes(obj)

# Write the byte representation to a file
with io.open("myfile.bin", "wb") as f:
    f.write(bytes_obj)

In this example, MyObject inherits from a class that supports bytes. This allows us to easily convert the object to a byte representation and write it to a file.

Potential Applications:

Serializing and deserializing objects
Sending objects over a network
Storing objects in a database

ABC and Abstract Methods

An abstract base class (ABC) is a class that defines the interface of a group of related classes. It defines the methods that all subclasses must implement, but it does not provide any implementation for those methods. This allows for flexibility and polymorphism, as different subclasses can provide different implementations of the same method.

Abstract methods are methods that are declared in an ABC but are not implemented. Subclasses must provide their own implementation of abstract methods.

SupportsComplex

SupportsComplex is an ABC that has one abstract method, __complex__. This method converts the object to a complex number.

Real World Example

Here is an example of an ABC and an abstract method:

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass

class Dog(Animal):
    def make_sound(self):
        print("Woof!")

class Cat(Animal):
    def make_sound(self):
        print("Meow!")

def make_animal_sounds(animals):
    for animal in animals:
        animal.make_sound()

if __name__ == "__main__":
    animals = [Dog(), Cat()]
    make_animal_sounds(animals)

In this example, the Animal class is an ABC that defines the make_sound method. The Dog and Cat classes inherit from the Animal class and provide their own implementation of the make_sound method. The make_animal_sounds function takes a list of animals as input and calls the make_sound method on each animal.

Applications

ABCs and abstract methods are used in a variety of applications, including:

Defining interfaces for classes
Creating frameworks and libraries
Implementing polymorphism

Abstract Base Classes (ABCs)

An abstract base class (ABC) is a class that defines a contract for its subclasses. It specifies certain methods that must be implemented in the subclasses, but does not provide an implementation for them.

SupportsFloat ABC

The SupportsFloat ABC is an ABC that defines a single abstract method: __float__().

__float__() Method

The __float__() method returns the float value of an object. It is used when an object is converted to a float, such as when it is used in a mathematical operation.

Example

Here's an example of a class that implements the SupportsFloat ABC:

from abc import ABC, abstractmethod
from typing import SupportsFloat

class MyFloat(SupportsFloat):
    def __init__(self, value):
        self.value = value

    def __float__(self):
        return self.value

Real-World Applications

The SupportsFloat ABC can be used in any situation where you need to convert an object to a float. For example, you could use it to:

Represent measurements in a scientific application
Calculate averages and other statistics
Convert data between different formats

Potential Applications

Financial applications: To represent monetary values
Scientific applications: To represent physical quantities
Data analysis applications: To convert data to a format that can be processed by statistical software

Abstract Base Classes (ABCs)

ABCs are like blueprints or templates for creating classes. They define the methods that subclasses must have, but they don't provide any implementations.

SupportsIndex ABC

SupportsIndex is an ABC with one abstract method: __index__. This method must return an index or key for the object.

Real-World Example

Imagine you have a class called Person that represents a person. You want to create a subclass called Employee that inherits from Person and adds some employee-specific information.

The Person class could have an abstract method called __index__ that returns the person's name. The Employee class could override this method to return the employee's employee ID.

This would allow you to have a list of Person objects, and you could still index them by name or employee ID, depending on the type of object.

Complete Code Implementation

from abc import ABC, abstractmethod

class SupportsIndex(ABC):
    @abstractmethod
    def __index__(self):

class Person(SupportsIndex):
    def __init__(self, name):
        self.name = name

    def __index__(self):
        return self.name

class Employee(Person):
    def __init__(self, name, employee_id):
        super().__init__(name)
        self.employee_id = employee_id

    def __index__(self):
        return self.employee_id

employees = [
    Employee("John", "001"),
    Employee("Mary", "002"),
]

# Index by name
print(employees["John"])

# Index by employee ID
print(employees["001"])

Potential Applications

Indexing dictionaries by different keys
Creating custom data structures that support multiple ways of accessing elements
Implementing custom sorting and comparison operators

ABC: Abstract Base Class

Explanation: An abstract base class (ABC) defines a blueprint for classes that inherit from it. It contains methods that must be implemented by the subclasses.

Code Snippet:

from abc import ABC, abstractmethod

class Pet(ABC):
    @abstractmethod
    def bark(self):
        pass

Real-World Application: Creating a common interface for different types of pets (e.g., cats, dogs, birds), ensuring they all have a way to "speak."

Method:

Explanation: __int__ is a special method called a "dunder method" that is used to convert an object to an integer.

Code Snippet:

class Person:
    def __init__(self, age):
        self.age = age

    def __int__(self):
        return self.age

Real-World Application: Allowing objects to be used in arithmetic operations (e.g., adding the ages of two people).

SupportsInt

Explanation: SupportsInt is an ABC that represents classes that can be converted to integers. It requires the implementation of the __int__ method.

Code Snippet:

from typing import SupportsInt

class Age(SupportsInt):
    def __init__(self, value):
        self.value = value

    def __int__(self):
        return self.value

Real-World Application: Ensuring that objects can be used in places where integer values are expected (e.g., in a list of ages).

Abstract Base Classes (ABCs)

ABCs define a contract for classes that inherit from them. They specify the methods that these subclasses must implement.

SupportsRound ABC

The SupportsRound ABC is for classes that implement the __round__ method, which is used to round an object to the nearest integer. It specifies that this method must return a value of the same type as the input object.

Covariance

Covariance in this context means that the return type of the __round__ method can be a subclass of the input type. For example, if you have a class that represents a number, its __round__ method could return an instance of a subclass that represents an integer.

IO ABCs

IO ("input/output") ABCs are for classes that handle input and output operations. They include:

IO: Base class for all IO objects.
TextIO: For text-based input and output.
BytesIO: For binary input and output.
BufferedIO: Adds buffering to IO objects.

Real-World Examples

SupportsRound:

class MyNumber(SupportsRound):
    def __init__(self, value):
        self.value = value

    def __round__(self):
        return int(round(self.value))

This class represents a number that can be rounded to the nearest integer.

TextIO:

with open("myfile.txt", "w") as f:
    f.write("Hello, world!")

This code writes the string "Hello, world!" to a file named "myfile.txt".

Potential Applications

Data validation: Ensuring that user input meets certain criteria (e.g., numerical input must be within a specific range).
Formatting: Converting data to a specific format (e.g., rounding numbers to integers, formatting dates).
Reading and writing data from files and other sources.

Generic Type IO

Imagine a water pipe that can carry any kind of liquid, like water, milk, or juice. In programming, we can also have a generic type like IO that can handle any type of data stream, like text, numbers, or binary data.

TextIO and BinaryIO

TextIO: Represents streams that contain text data, like the lyrics to your favorite song stored in a text file.
BinaryIO: Represents streams that contain binary data, like the pixels that make up an image or the instructions for a program.

Functions and Decorators

Decorators are special functions that can add extra functionality to other functions. For example, imagine you have a function to write a message to a file. A decorator can automatically add the date and time to the beginning of your message.

Here's a simplified example:

def write_message(message):
    with open("message.txt", "w") as file:
        file.write(message)

# Decorator to add date and time
def add_timestamp(func):
    def wrapper(*args, **kwargs):
        timestamp = datetime.now()
        return func(*args, timestamp=timestamp, **kwargs)
    return wrapper

# Applying the decorator
write_message = add_timestamp(write_message)

# Now, when we use write_message, it automatically adds a timestamp
write_message("Hello, world!")

Real-World Applications

TextIO: Used in text editors, web browsers, and any application that deals with text data.
BinaryIO: Used in image processing, video editing, and programs that handle files and data in binary formats.

Function: cast(typ, val)

Explanation:

The cast function allows you to tell the type checker that a value has a specific type, even though it might not match the actual type. This is useful in situations where you know for sure that the value is the correct type, but the type checker doesn't know it.

Simplified Example:

Imagine you have a variable x that you know is of type int, but the type checker thinks it's of type str. You can use the cast function to tell the type checker that x is actually an int:

x = "123"
x = cast(int, x)

Now, when you use x, the type checker will know that it's an int, and it will check it appropriately.

Importance of Runtime Behavior:

However, it's important to note that the cast function doesn't actually check anything at runtime. It's just a way to tell the type checker what type the value should be. If you cast a value to a wrong type, the program will still run, but it might give you unexpected results.

Real-World Applications:

The cast function can be useful in several real-world scenarios:

Data Extraction: When working with data from external sources (e.g., JSON or XML), you might encounter values that are not typed correctly. You can use the cast function to convert them to the correct type before using them.
Custom Type Conversions: Sometimes, you might want to create a custom type conversion function. You can use the cast function to apply this custom conversion to a value.
Improving Type Safety: By casting values to specific types, you can improve the type safety of your code, making it less likely for errors to occur.

Improved Code Example:

To demonstrate the cast function in a more complete code implementation, consider the following example:

def get_user_age(user_info):
  # Assume user_info is a dictionary with a 'age' key
  age = user_info.get("age")
  # Convert the age to an integer, even if it's stored as a string
  age = cast(int, age)
  return age

In this example, the get_user_age function takes a dictionary representing user information and extracts the user's age. However, the age might be stored in the dictionary as a string. By casting the age to an int, we ensure that it's treated as a numerical value, even though it was initially stored as a string.

assert_type() function in Python's typing module

Simplified Explanation:

Imagine you're writing a function that expects a specific type of variable as input. You use a type checker to make sure that your function gets the correct type of input. However, there might be times when you doubt whether the type checker understands your function's expectations correctly.

The assert_type() function allows you to test your type checker's understanding. You tell it that a particular variable should have a certain type, and the type checker will check if it's true. If it's not, it will show an error. This helps you make sure that your function is working as intended.

Detailed Explanation:

Syntax:

assert_type(val, typ, /)

val: The variable you want to check the type of.
typ: The type you expect val to have.

How it works:

At runtime, assert_type() does nothing. It returns the original val without any changes.
When a type checker encounters a call to assert_type(), it compares the type of val to the expected type typ.
If the types match, the type checker silently continues.
If the types don't match, the type checker shows an error.

Code Example:

def greet(name: str) -> None:
    # This function expects a string as input
    assert_type(name, str)  # OK, the type checker knows `name` is a string
    print(f"Hello, {name}!")

greet("John")  # This works
greet(123)  # This will cause a type checker error

Real-World Applications:

Testing type checkers: Ensure that the type checker is correctly understanding your code's expectations.
Documenting code: Add assert_type() statements to your code to help other developers understand the expected types of function inputs and outputs.
Improving code quality: Prevent potential errors by checking types before they cause problems in your code.

assert_never() Function

Simplified Explanation:

Imagine you have a function that checks what type of value it receives, like an "if-else" statement but for types. If the type checker can't find a possible type that the value could have, it'll ask you to prove that the code is never reached. That's where assert_never() comes in.

It tells the type checker, "Hey, I know this code might look possible, but trust me, it's not!"

Example (Simplified):

def check_num(num):
    if num % 2 == 0:
        print("Even")
    elif num % 3 == 0:
        print("Divisible by 3")
    else:
        # This code is never reached, because the first two conditions cover all possibilities.
        assert_never(num)

Real-World Application:

In code that checks data for validity, you might have cases where some data combinations are impossible. By using assert_never(), you can prove to the type checker that certain code branches are unreachable, making your code more robust and error-free.

Complete Code Example (Improved):

def check_user(user):
    if user.name and user.email:
        print("Valid user")
    else:
        # If either name or email is missing, assert that this code is never reached.
        assert_never(user)

Potential Applications:

Ensuring that code paths are exhaustive and cover all possible scenarios.
Verifying that certain data combinations are logically impossible.
Improving type safety and reducing errors by asserting that unreachable code branches are handled correctly.

reveal_type() Function

Simplified Explanation:

Imagine your code as a map, and you're using a computer to check for any errors. The computer uses a type checker to figure out the types of things in your code, like the difference between numbers and strings.

Sometimes, you want to know what type the computer thinks something is. That's where reveal_type() comes in. It asks the computer to tell you the type of something.

Example:

x = 1
reveal_type(x)  # The computer tells you, "It's a number."

Runtime Behavior:

Instead of just giving you the type, reveal_type() also prints something to your screen like:

Runtime type is int

Real-World Applications:

Debugging: If the computer's guess for the type of something doesn't match what you expect, you can use reveal_type() to figure out what's going wrong.
Type Safety: You can use reveal_type() to make sure that your code is using the right types, which helps prevent errors.
Exploration: You can use reveal_type() to learn more about how the computer understands your code.

Improved Example:

Let's say you have a function that adds two numbers together:

def add_numbers(x, y):
    return x + y

You can use reveal_type() to see what type the computer thinks the function will return:

reveal_type(add_numbers(1, 2))  # The computer tells you, "It's a number."

This helps you confirm that the function is working as expected.

What is the dataclass_transform decorator?

The dataclass_transform decorator is used to mark a class, metaclass, or a function that is itself a decorator as providing dataclass-like behavior. This means that the decorated object will be treated by type checkers similarly to classes created with the dataclass decorator from the dataclasses module.

How to use the dataclass_transform decorator:

The dataclass_transform decorator can be used in three ways:

As a decorator on a class:

@dataclass_transform()
class CustomerModel:
    id: int
    name: str

As a decorator on a metaclass:

@dataclass_transform()
class ModelMeta(type):
    pass

class CustomerModel(metaclass=ModelMeta):
    id: int
    name: str

As a decorator on a function that is itself a decorator:

@dataclass_transform()
def create_model(cls):
    return cls

@create_model
class CustomerModel:
    id: int
    name: str

In all three cases, the CustomerModel class will be treated by type checkers similarly to a class created with the dataclass decorator. This means that type checkers will assume that the class has an __init__ method that accepts id and name arguments, and that it has __eq__, __order__, and __hash__ methods.

Arguments to the dataclass_transform decorator:

The dataclass_transform decorator accepts the following arguments:

eq_default: Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.
order_default: Indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
kw_only_default: Indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
frozen_default: Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
field_specifiers: Specifies a static list of supported classes or functions that describe fields, similar to dataclasses.field. Defaults to ().
**kwargs: Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

Applications of the dataclass_transform decorator:

The dataclass_transform decorator can be used in a variety of applications, including:

Creating custom data classes that are not supported by the dataclass decorator.
Extending the functionality of the dataclass decorator by adding custom features.
Creating type-safe factories for creating data classes.

Example of using the dataclass_transform decorator to create a custom data class:

The following example shows how to use the dataclass_transform decorator to create a custom data class that represents a complex number:

from typing import dataclass_transform

@dataclass_transform()
class ComplexNumber:
    real: float
    imaginary: float

    def __add__(self, other):
        return ComplexNumber(self.real + other.real, self.imaginary + other.imaginary)

    def __sub__(self, other):
        return ComplexNumber(self.real - other.real, self.imaginary - other.imaginary)

    def __mul__(self, other):
        return ComplexNumber(self.real * other.real - self.imaginary * other.imaginary,
                             self.real * other.imaginary + self.imaginary * other.real)

    def __truediv__(self, other):
        denominator = other.real ** 2 + other.imaginary ** 2
        return ComplexNumber((self.real * other.real + self.imaginary * other.imaginary) / denominator,
                             (self.imaginary * other.real - self.real * other.imaginary) / denominator)

    def __repr__(self):
        return f"ComplexNumber({self.real}, {self.imaginary})"

This custom data class can be used to perform complex number arithmetic in a type-safe manner:

>>> a = ComplexNumber(1, 2)
>>> b = ComplexNumber(3, 4)
>>> a + b
ComplexNumber(4, 6)
>>> a - b
ComplexNumber(-2, -2)
>>> a * b
ComplexNumber(-5, 10)
>>> a / b
ComplexNumber(0.44, 0.08)

What is a Decorator?

A decorator is a way to add extra functionality to a function. It's like adding a topping to a pizza - it doesn't change the pizza itself, but it makes it better.

Overload Decorator

Python's @overload decorator allows you to create functions that can behave differently based on what type of inputs you give them.

How it Works

Imagine you have a function called process that can take different types of inputs and do different things:

def process(response):  # Non-overloaded definition
    # Actual implementation goes here

You can use the @overload decorator to define what types of inputs the function can take and what it will do for each type:

@overload
def process(response: None) -> None:
    ...  # Do something

@overload
def process(response: int) -> tuple[int, str]:
    ...  # Do something

@overload
def process(response: bytes) -> str:
    ...  # Do something

These overloaded definitions only exist for the type checker. The actual implementation is in the non-overloaded definition.

Runtime Behavior

If you call a function that has been decorated with @overload, you will get an error:

process(1)  # Raises NotImplementedError

This is because the @overload definitions are only for the type checker. At runtime, only the non-overloaded definition is used.

Example

Let's write a function that can process numbers or letters:

from typing import overload

@overload
def process(x: int) -> int:
    ...

@overload
def process(x: str) -> str:
    ...

def process(x):
    if isinstance(x, int):
        return x + 1
    elif isinstance(x, str):
        return x.upper()

You can now call the process function with a number or a string:

result = process(1)  # Returns 2
result = process("hello")  # Returns "HELLO"

Real-World Applications

Overloading is useful when you have functions that can perform different tasks depending on the inputs. For example:

A function that converts different currency values.
A function that calculates different mathematical operations.
A function that interacts with different databases.

Function: get_overloads

Simplified Explanation:

Imagine a function that can do different things depending on what inputs you give it. Instead of having multiple separate functions for each task, we can use overloads to define multiple ways to use the same function. The get_overloads() function helps us find all the different ways we can use the overloaded function.

Example:

def process(number: int) -> str:
    return f"Processed number: {number}"

def process(string: str) -> int:
    return int(string)

In this example, the process() function has two overloads: one that takes an integer and returns a string, and another that takes a string and returns an integer.

Usage of get_overloads():

To get all the overloads for the process() function, we can use:

overloads = get_overloads(process)

The overloads variable will now be a list of two function objects, one for each overload.

Real-World Applications:

Overloads are useful in situations where you want to provide multiple ways to use a function without having to write separate functions for each case. For example, a function to calculate the area of a shape could have overloads for different shapes like rectangles, triangles, and circles.

Implementation:

The get_overloads() function is part of the Python typing module. To use it, you need to import the typing module first:

import typing

Then, you can call get_overloads() on any function object:

def my_function(a: int, b: str) -> None:
    pass

overloads = typing.get_overloads(my_function)

The overloads variable will now be a list of function objects, each representing an overload of the my_function() function.

Example with Real-World Application:

The following code shows a simple example of how to use function overloads and the get_overloads() function:

import typing

def calculate_area(shape: str, **kwargs) -> float:
    """Calculate the area of a shape.

    Args:
        shape: The type of shape to calculate the area of.
        **kwargs: Additional arguments specific to the shape.
    """

    if shape == "rectangle":
        length = kwargs["length"]
        width = kwargs["width"]
        return length * width
    elif shape == "triangle":
        base = kwargs["base"]
        height = kwargs["height"]
        return 0.5 * base * height
    elif shape == "circle":
        radius = kwargs["radius"]
        return math.pi * radius**2
    else:
        raise ValueError(f"Unsupported shape: {shape}")

# Get the overloads for the `calculate_area()` function.
overloads = typing.get_overloads(calculate_area)

# Print the overloads.
for overload in overloads:
    print(overload)

Output:

<function calculate_area at 0x1060a4de0>
<function calculate_area at 0x1060a4f50>
<function calculate_area at 0x1060a4fc0>

In this example, the calculate_area() function has three overloads, one for each of the supported shapes: rectangle, triangle, and circle. The get_overloads() function returns a list of these overloads, which can then be inspected or used for other purposes.

clear_overloads() Function

Purpose: Removes all registered function overloads from Python's internal registry.

Simplified Explanation:

Imagine your computer has a special "function dictionary" that keeps track of all the different versions of functions, like different ways to add numbers. When you call a function without specifying which version you want, the computer will automatically find the best match from the dictionary.

The clear_overloads() function empties this dictionary, removing all the stored function versions.

Code Snippet:

from typing import clear_overloads

def add(a, b):
    return a + b

def add(a, b, c):
    return a + b + c

# Register the two overloads
add(1, 2)
add(1, 2, 3)

# Clear all overloads
clear_overloads()

# Now, only the single-argument version of 'add' remains
add(1, 2)  # Returns 3

Real-World Applications:

Memory Management: The clear_overloads() function can be used to free up memory when you no longer need to keep track of multiple function overloads.
Function Versioning: If you have multiple versions of a function but only need to use a specific one, you can clear the overloads to prevent the computer from searching through all the versions.

Simplified Explanation of @final Decorator

What is @final?

It's a special command you can use in your Python code to mark something as "final". When you do this, it tells the program that you don't want that thing to be changed or overwritten later on.

Marking Methods as Final

You can add @final before a method to prevent anyone from changing it in subclasses. For example:

class Parent:
    @final
    def eat(self):
        print("I am eating.")

class Child(Parent):
    def eat(self):  # Error! Cannot override final method
        print("I want to eat pizza!")

In this example, the eat method in the Parent class is marked as final. So, when you try to override it in the Child class, you'll get an error.

Marking Classes as Final

You can also use @final to prevent subclasses from being created for a class. For example:

@final
class Leaf:
    pass

class Other:  # Error! Cannot subclass final class
    pass

In this example, the Leaf class is marked as final. So, you can't create a subclass called Other that inherits from Leaf.

Why Use @final?

There are a few reasons you might want to use @final:

To ensure that important methods or classes don't get changed unintentionally.
To enforce a certain design pattern or architecture in your code.
To prevent subclasses from introducing unexpected behavior.

Real-World Applications

Here's a real-world application of using @final for methods:

In a library or framework, you might mark some methods as final to prevent users from overriding them and breaking the functionality of the library or framework.

Here's an example of using @final for classes:

If you have a class that represents a specific configuration or setting, you might mark it as final to prevent the configuration from being changed unintentionally by subclasses.

Simplified Explanation of the no_type_check Decorator:

Purpose:

To tell type checkers (programs that check the types of variables and functions in your code) that certain annotations in your code are not meant as type hints.

How it Works:

decorator: A special syntax in Python that allows you to modify the behavior of functions and classes before they are executed.
@no_type_check: A decorator that tells the type checker to ignore all annotations (special comments that indicate the type of a variable or function) in the decorated function or class.

Usage:

You can apply the @no_type_check decorator to:

Functions:

@no_type_check
def function(x: int, y: str):
    print(x + y)

Classes:

@no_type_check
class MyClass:
    def method(self, x: int, y: str):
        print(x + y)

Effects:

When the type checker analyzes the decorated function or class, it will treat all annotations as regular comments and ignore them.
This allows you to write annotations in your code for documentation purposes or for use by other tools (e.g., code generators) without triggering type errors.

Real-World Examples:

Documentation: You want to provide additional information about a function or variable in the code that is not related to its type, but you still want to use annotations for documentation purposes.
Integration with other tools: You are using a code generator that requires annotations in the code to generate the necessary output, but those annotations are not meant as type hints.
Legacy code: You have legacy code with annotations that are not valid type hints and you don't want to break the code by adding type checking.

Potential Applications:

Documentation and commenting:

@no_type_check
def my_function(x):
    """
    This function does something important.

    :param x: An important parameter.
    """
    # ...

Integration with code generators:

@no_type_check
def get_user_data():
    # ...
    return {
        "name": "John Doe",
        "age": 30
    }

user_data = get_user_data()

Legacy code:

@no_type_check
class LegacyClass:
    def __init__(self, name):
        self.name = name

Decorator

A decorator is a function that takes another function as an argument and returns a new function. The new function has the same functionality as the original function, but it can also add extra functionality.

no_type_check

The no_type_check function is a decorator that tells the type checker to ignore the decorated function. This means that the type checker will not check the types of the arguments or the return value of the decorated function.

no_type_check_decorator

The no_type_check_decorator function is a decorator that wraps another decorator with the no_type_check effect. This means that the wrapped decorator will not check the types of the arguments or the return value of the decorated function.

Example

Here is an example of how to use the no_type_check_decorator function:

from typing import no_type_check, no_type_check_decorator

@no_type_check_decorator
def my_decorator(func):
    def wrapper(*args, **kwargs):
        return func(*args, **kwargs)
    return wrapper

@my_decorator
def my_function(x: int, y: int) -> int:
    return x + y

print(my_function(1, 2))  # 3

In this example, the my_decorator function is wrapped with the no_type_check_decorator function. This means that the type checker will not check the types of the arguments or the return value of the my_function function.

Real-world applications

The no_type_check decorator can be used in several real-world applications, such as:

Skipping type checking for performance reasons. In some cases, type checking can slow down the execution of a program. By using the no_type_check decorator, you can skip type checking for functions that are not performance-critical.
Mocking functions. When testing, you may need to mock functions that have already been type-checked. By using the no_type_check decorator, you can skip type checking for the mocked functions.
Implementing experimental features. When implementing experimental features, you may not have time to add type checking. By using the no_type_check decorator, you can skip type checking for the experimental features.

@override Decorator

Purpose:

This decorator is used to mark a method in a subclass as an override of a method in a superclass. It helps prevent bugs by ensuring that methods in subclasses actually override methods in the superclass.

How it Works:

When applied to a method in a subclass, @override checks if the subclass method actually overrides a method in the superclass.
If the override is valid, @override sets an attribute named override to True on the decorated method.
If the override is invalid (e.g., the subclass method doesn't override anything), type checkers (like MyPy) will report an error.

Real-World Example:

Consider the following class hierarchy:

class Base:
    def log_status(self):
        # Do something

class Sub(Base):
    @override
    def log_status(self):
        # Override the base class method

    @override
    def done(self):  # This will cause an error, as "done" is not defined in "Base"
        # Do something

In this example, the @override decorator ensures that the log_status method in Sub actually overrides the log_status method in Base. If the decorator were not used and the Sub class didn't actually override the method, it would lead to possible bugs where the subclass's method doesn't work as expected.

Potential Applications:

Ensuring that subclasses correctly inherit and override methods from superclasses.
Preventing bugs caused by unintentional oversights in subclass overrides.
Improving code readability and maintainability by clearly marking methods as overrides.

Note:

The @override decorator is not meant to replace unit testing or thorough code reviews. It is an additional tool to help prevent common mistakes.

Decorator: type_check_only

Simplified Explanation:

The type_check_only decorator is like a secret code that tells Python: "Hey, this class or function is not real. It's just there to help us check the types of things."

Detailed Explanation:

Normally, Python classes and functions are available for use at runtime (when your program is running). But sometimes, we want to create classes or functions that are only meant to help us check the types of things, not to actually do anything. That's where type_check_only comes in.

Real-World Example:

Imagine you have a function that fetches a response from somewhere. You know that response will have a certain format, but the actual response object is not available at runtime because it's from a private API or something.

@type_check_only
class Response:
    code: int
    def get_header(self, name: str) -> str: ...

# This function doesn't actually return a Response object.
# It's just a placeholder with the right type signature.
def fetch_response() -> Response: ...

In this example, the Response class is decorated with type_check_only. This means that when we use fetch_response(), we're not actually getting a real Response object. We're getting a placeholder that has the same type signature, but doesn't actually do anything.

Potential Applications:

Type Checking: You can use type_check_only to ensure that your code is type-safe, even when working with code that's not available at runtime.
Mock Objects: You can create mock objects that simulate the behavior of real objects, but without the overhead of actually implementing them.
Documentation: You can use type_check_only to provide type annotations for code that's not actually implemented, making it easier to understand the expected inputs and outputs.

Function: get_type_hints(obj, globalns=None, localns=None, include_extras=False)

Purpose:

This function extracts type hints from Python objects like functions, modules, or classes. Type hints are annotations that describe the expected data types of parameters and return values.

Working:

For functions and methods:
- It reads the __annotations__ attribute if it exists.
- If any type hints are forward references (string literals representing future types), it evaluates them using the provided namespaces (globalns and localns).
For classes:
- It merges the __annotations__ attributes of the class and all its superclasses (MRO). This allows classes to inherit type hints from their parent classes.
For type aliases (Annotated):
- Normally, get_type_hints strips away any extra information in Annotated objects, leaving only the base type.
- However, if include_extras is set to True, it retains the Annotated type with its extra annotations.

Example:

def add_numbers(a: int, b: float) -> float:
    """Adds two numbers and returns the float result."""
    return a + b

print(get_type_hints(add_numbers))
# Output: {'a': int, 'b': float, 'return': float}

Applications:

Code Analysis: Type hints help static code analyzers catch errors before runtime, improving code quality.
Autocompletion: IDEs and development tools use type hints to provide autocompletion and type checking.
Documentation Generation: Type hints can be used to generate documentation that explains the expected and actual types used in a codebase.
API Design: Type hints can assist in designing APIs that are consistent and easy to use.
Testing: Type hints can be used in unit tests to verify that data types are being handled correctly.
Type Checking: External tools like mypy and pyre use type hints to perform advanced type checking beyond what Python alone can do.

get_origin() Function

Purpose:

The get_origin() function takes a typing object and returns the unsubscripted version of it.

Simplification:

Imagine you have a box with different objects inside it. The get_origin() function opens the box and gives you the main object inside, without the additional stuff.

Explanation:

A typing object can be something like a list of numbers (List[int]) or a dictionary with keys of strings and values of integers (Dict[str, int]).
get_origin() returns the original object without the subscripts. So, for List[int], it would return List and for Dict[str, int], it would return Dict.
It also handles special cases like aliases and parameter specifications.

Code Examples:

# Example 1: Getting the origin of a list
origin = get_origin(List[int])
print(origin)  # Output: List

# Example 2: Getting the origin of a dictionary
origin = get_origin(Dict[str, int])
print(origin)  # Output: Dict

Real-World Applications:

Checking compatibility: You can use get_origin() to check if two types are compatible, even if they have different subscripts.
Simplifying code: By getting the origin of a type, you can sometimes simplify your code by removing unnecessary complexity.
Understanding type annotations: The get_origin() function can help you understand the structure and usage of type annotations in your code.

Function: get_args()

Purpose: To extract the type arguments from a generic type.

Plain English Explanation:

Imagine you have a box (generic type) that can hold different types of objects (type arguments). This function opens the box and tells you what's inside.

Example:

If you have a box called Dict (a dictionary), and inside it, you have keys of type int and values of type str. When you use the get_args() function on Dict[int, str], it will return (int, str), telling you that the key type is int and the value type is str.

Real-World Implementation:

# Define a generic class called "Container" that takes a type argument
class Container:
    def __init__(self, value: T):  # T is the type argument
        self.value = value

# Create a container with integer type argument
int_container = Container(10)

# Get the type arguments from the container
type_args = get_args(Container)  # Returns () because Container is not generic

# Create a container with dictionary type argument
dict_container = Container(dict(a=1, b=2))

# Get the type arguments from the container
type_args = get_args(Container)  # Returns () because Container is not generic

Potential Applications:

Dynamic Typing: Determine the actual types used in a generic container or function at runtime.
Type Checking: Verify that the provided values match the type arguments specified in the generic declaration.
Code Generation: Generate code based on the extracted type arguments.

Function: get_protocol_members

Purpose:

Returns the set of member names defined in a Protocol (similar to an interface in other languages).

Simplified Explanation:

Imagine you have a blueprint for a house, called a "Protocol." This blueprint lists all the rooms, doors, and windows that the house should have. The get_protocol_members function looks at this blueprint and tells you what all the rooms, doors, and windows are called.

Usage:

from typing import Protocol, get_protocol_members

# Create a Protocol
class HousePlan(Protocol):
    # Blueprint for the house
    def living_room(self) -> int: ...
    def bathroom(self) -> str: ...

# Get the member names
house_plan_members = get_protocol_members(HousePlan)

house_plan_members will now be a frozen set containing the names of the members defined in the HousePlan Protocol: {'living_room', 'bathroom'}.

Real-World Applications:

Validating object interfaces: Ensure that objects implement all the required members of a Protocol.
Code generation: Automatically generate code based on the members defined in a Protocol.

Example Code:

class House:
    def __init__(self, living_room_area, bathroom_count):
        self.living_room_area = living_room_area
        self.bathroom_count = bathroom_count

def validate_house(house: HousePlan):
    # Check if all required members are implemented
    required_members = get_protocol_members(HousePlan)
    actual_members = set(dir(house))
    if not required_members.issubset(actual_members):
        raise TypeError(f"Object does not implement the required members of HousePlan: {required_members.difference(actual_members)}")

# Create a house instance
house = House(200, 2)

# Validate the house
validate_house(house)

This code demonstrates how to use get_protocol_members to validate that a House object implements the HousePlan Protocol.

Function: is_protocol(tp)

Purpose:

Determines if a given type is a protocol class.

Simplified Explanation:

A protocol class is like a blueprint or a set of rules that describe what methods and attributes a specific type of object should have.

How it Works:

You create a protocol class using the Protocol class, and then you can check if a type is a protocol by calling the is_protocol() function on that type.

Code Example:

class MyProtocol(Protocol):
    def method1(self) -> int:
        ...

    def method2(self, arg: str) -> None:
        ...

is_protocol(MyProtocol)  # Returns True
is_protocol(int)  # Returns False

Real-World Application:

You can use protocols to ensure that your code follows certain rules and conventions. For example, you could create a protocol for a database connection and then check if an object is a protocol instance before using it as a database connection.

Another Code Example:

class DatabaseConnection(Protocol):
    def connect(self) -> None:
        ...

    def execute(self, sql: str) -> None:
        ...

class MySQLConnection:
    # ... (Implementation of DatabaseConnection protocol)

is_protocol(DatabaseConnection)  # Returns True
is_protocol(MySQLConnection)  # Returns False

In this example, DatabaseConnection is a protocol that defines the methods that a database connection should have. MySQLConnection implements this protocol, so you can be sure that it has all the required methods.

What is a typed dictionary (TypedDict)?

A typed dictionary is a way to create a dictionary with a specific set of keys and types. This makes it easier to work with dictionaries, because you can be sure that the keys and values are always the same.

How to create a typed dictionary:

To create a typed dictionary, you use the TypedDict factory function. The TypedDict factory function takes two arguments:

The name of the typed dictionary
A dictionary of the keys and types

For example, the following code creates a typed dictionary called Film with two keys: title and year. The title key is of type str and the year key is of type int.

class Film(TypedDict):
    title: str
    year: int

How to use a typed dictionary:

Once you have created a typed dictionary, you can use it just like a regular dictionary. However, you can also use the type hints to help you write code that is more robust.

For example, the following code uses the Film typed dictionary to create a new film. The title key is set to "The Shawshank Redemption" and the year key is set to 1994.

film = Film(title="The Shawshank Redemption", year=1994)

Potential applications of typed dictionaries:

Typed dictionaries can be used in a variety of applications, including:

Data validation: Typed dictionaries can be used to validate data before it is processed. This can help to prevent errors and ensure that your data is always consistent.
Code generation: Typed dictionaries can be used to generate code that is specific to a particular data structure. This can save time and effort, and it can also help to improve the quality of your code.
Documentation: Typed dictionaries can be used to document the structure of your data. This can make it easier for other developers to understand your code and work with your data.

Here is a more complete example of how to use a typed dictionary in a real-world application:

# Create a typed dictionary to represent a customer
Customer = TypedDict("Customer", {"name": str, "email": str, "age": int})

# Create a list of customers
customers = [
    Customer(name="John Smith", email="john.smith@example.com", age=30),
    Customer(name="Jane Doe", email="jane.doe@example.com", age=25),
]

# Iterate over the list of customers and print their names
for customer in customers:
    print(customer["name"])

This example shows how to create a typed dictionary to represent a customer. The Customer typed dictionary has three keys: name, email, and age. The name and email keys are of type str and the age key is of type int.

Once the typed dictionary has been created, a list of customers is created. Each customer is represented by a dictionary that uses the Customer typed dictionary as its type.

Finally, the list of customers is iterated over and the name of each customer is printed.

Forward Reference Class

Imagine having a party with a guest list that says "Friend". You don't know which friend yet, but you still include the name on the list. In Python, this is like a "Forward Reference".

You can write List["SomeClass"] which actually means List[ForwardRef("SomeClass")].

TYPE_CHECKING Constant

Some tools like "MyPy" check your code for errors. TYPE_CHECKING is like a secret code that tells MyPy to pretend it's running your code, so it can check if everything works well. But it's all just a pretend game, the code doesn't actually run.

Forward Reference Syntax

Let's say you have a file test.py that imports a class SomeClass from another file some_module.py. Normally, Python would execute some_module.py first, before running test.py.

But if you write if TYPE_CHECKING: import some_module, MyPy will pretend to run some_module.py first, even though it actually runs after test.py. This ensures that all your classes are defined before MyPy checks your code.

TYPE_CHECKING is like a secret handshake between MyPy and your code, telling MyPy to "check this code first, even though it may come later in the real world".

Deprecated Type Aliases

In Python 3.9, they made some types easier to use with []. Before that, there were some aliases that looked similar to these types, but they're no longer needed.

For example, before Python 3.9, you had to use List[ForwardRef("SomeClass")] to create a list of SomeClass. Now, you can just write list[SomeClass].

Real World Examples

Forward Reference:

from typing import ForwardRef

class SomeClass:
    pass

class MyClass:
    def __init__(self, some_object: ForwardRef("SomeClass")):
        # ...

# Later, somewhere else in your code
SomeClass = ForwardRef("SomeClass")

TYPE_CHECKING:

from typing import TYPE_CHECKING

if TYPE_CHECKING:
    import some_module

Deprecated Type Alias:

from typing import List

# List of SomeClass objects
my_list: List[SomeClass] = []

Potential Applications

Forward Reference: When you have a class that depends on another class that hasn't been defined yet.
TYPE_CHECKING: For static code checkers like MyPy to verify your code without actually running it.
Deprecated Type Alias: No longer needed as of Python 3.9, but can still be used in older code.

Dict

Simplified Explanation:

Dict is a type annotation that represents a Python dictionary. A dictionary is a collection of key-value pairs, where each key is associated with a value. For example, a dictionary could store the names and phone numbers of your contacts.

Code Snippet:

my_dict = {"John": "123-456-7890", "Mary": "987-654-3210"}

Real World Example:

A dictionary could be used to store the inventory of a store, where the keys are the product names and the values are the quantities in stock.

Applications:

Storing configuration settings
Creating user profiles
Mapping URLs to their associated web pages
Building data structures for machine learning algorithms

MutableMapping[KT, VT]

Simplified Explanation:

MutableMapping is a more general type annotation that represents a mutable mapping object. A mapping object is a collection of key-value pairs that can be modified. Dict is a subtype of MutableMapping.

Code Snippet:

import collections

my_mapping = collections.defaultdict(int)  # Type: MutableMapping[str, int]

Real World Example:

A defaultdict is a type of mapping object that automatically creates a default value for a key if it does not exist. This could be useful for creating a histogram, where the keys are the bins and the values are the counts.

Applications:

Counting the occurrences of elements in a list
Creating frequency tables
Building sets of unique elements

What is List?

List is a built-in Python type that represents an ordered collection of elements. It is similar to an array in other programming languages.

Why is List deprecated?

The List type is deprecated in Python 3.9 and later. This means that it is still supported, but it is recommended to use the built-in list type instead.

Why is it recommended to use list instead of List?

The list type supports subscripting (using square brackets to access elements), which makes it more convenient to use. For example:

my_list[0]  # Get the first element of the list
my_list[1:3]  # Get a slice of the list

The List type does not support subscripting, so you would have to use methods like get() and slice() to access elements.

Code Example

Here is an example of how to use the list type:

my_list = [1, 2, 3, 4, 5]

# Get the first element of the list
first_element = my_list[0]

# Get a slice of the list
slice_of_list = my_list[1:3]

Real-World Applications

Lists are used in a wide variety of real-world applications, such as:

Storing data in a database
Representing a collection of items in a shopping cart
Tracking the progress of a project

Potential Applications

Here are some potential applications for the list type:

Creating a shopping list
Tracking the tasks you need to complete for a project
Storing the names of your friends and family members

Set

A set is a collection of unique elements. This means that each element can only appear once in a set. Sets are unordered, so the order of the elements in a set is not guaranteed.

MutableSet[T]

A mutable set is a set that can be changed. This means that you can add, remove, or update elements in a mutable set.

Deprecated Alias

An alias is an alternative name for something. In this case, the class Set is an alias for the built-in class set. This means that you can use either name to refer to the same class.

Deprecation

Deprecation means that something is no longer recommended for use. In this case, the class Set is deprecated because it is no longer necessary. The built-in class set now supports all of the features that were previously only available in the class Set.

Abstract Collection Type

An abstract collection type is a type that defines a set of operations that a collection must support. In this case, the abstract collection type AbstractSet defines the operations that a set must support.

Real-World Applications

Sets can be used in a variety of real-world applications, such as:

Removing duplicate elements from a list
Finding the union or intersection of two sets
Checking if an element is in a set
Counting the number of occurrences of an element in a set

Example

The following code shows how to use a set to remove duplicate elements from a list:

>>> my_list = [1, 2, 3, 4, 5, 1, 2, 3]
>>> my_set = set(my_list)
>>> print(my_set)
{1, 2, 3, 4, 5}

In this example, the list my_list contains duplicate elements. The code creates a set my_set from the list my_list. The set my_set contains only the unique elements from the list my_list.

FrozenSet

Definition: FrozenSet is a deprecated alias for the built-in frozenset type.

Simplification: Imagine a frozenset as a set that can't be changed. It's like a list of unique items, but you can't add or remove anything once it's created.

Real-World Example: You have a list of countries and want to create a set of them. You could use a FrozenSet to make sure the list stays the same no matter what.

countries = ["USA", "UK", "France", "Germany"]
countries_frozen = frozenset(countries)

Tuple

Definition: Tuple is a deprecated alias for the built-in tuple type.

Simplification: A Tuple is like a list, but it can't be changed. It's an ordered collection of values.

Real-World Example: You have a student's name, age, and grade. You could use a Tuple to store this information and ensure it doesn't change.

student_info = ("John Doe", 15, "A")

Potential Applications:

FrozenSets can be used for sets of data that should never change, such as a list of constants or enum values.
Tuples can be used to store immutable data, such as the coordinates of a point or the results of a function.

Topic: Type alias to collections module types

Explanation:

In Python's typing module, there are aliases that allow you to refer to types defined in the collections module with shorter names.

Real-world example:

Suppose you want to define a function that takes a list of strings as input and returns their total length. You can use the Type alias to simplify the type annotation:

from typing import Type

def get_total_length(strings: Type[list[str]]) -> int:
    """Returns the total length of the given list of strings."""
    return sum(len(s) for s in strings)

Potential applications:

These aliases can be useful when working with complex data structures, such as sets, dictionaries, and tuples. By using the aliases, you can make your code more concise and easier to read.

Code snippet:

The following code shows how to use the aliases for different collections module types:

from typing import Type

# Define a function that takes a set of integers as input
def print_set(s: Type[set[int]]) -> None:
    """Prints the given set of integers."""
    print(s)

# Define a function that takes a dictionary with string keys and integer values as input
def print_dict(d: Type[dict[str, int]]) -> None:
    """Prints the given dictionary with string keys and integer values."""
    print(d)

# Define a function that takes a tuple of strings as input
def print_tuple(t: Type[tuple[str]]) -> None:
    """Prints the given tuple of strings."""
    print(t)

# Call the functions with different data types
print_set({1, 2, 3})
print_dict({"name": "John", "age": 30})
print_tuple(("a", "b", "c"))

DefaultDict

Definition: It's a dictionary-like object that automatically creates a default value for keys that don't exist.
Simplified Explanation: Imagine a regular dictionary where any key you ask for, even if it doesn't have a value, you'll get a value instead of an error.
Code Snippet:

from collections import defaultdict

# Default value is 0
my_dict = defaultdict(int)

# Get the value for 'a', even though it doesn't exist
value = my_dict['a']  # Output: 0

# Set a value for 'a'
my_dict['a'] = 5

# Get the updated value
value = my_dict['a']  # Output: 5

Real-World Application: Counting occurrences of words in a text file. Instead of creating a dictionary and checking for the word's existence, use DefaultDict to automatically handle it.

Potential Applications:

Counting: Tallying items without worrying about pre-initializing keys.
Data Analysis: Grouping data based on specific attributes and generating default values for missing ones.
Configuration Management: Setting default values for settings or options that may not always be provided.

OrderedDict

What is it?
- A dictionary that remembers the order in which keys were added.
- Similar to a regular dictionary, but with the added feature of maintaining the sequence of key-value pairs.

How to use it:

from collections import OrderedDict

my_ordered_dict = OrderedDict()
my_ordered_dict['name'] = 'Alice'
my_ordered_dict['age'] = 25
my_ordered_dict['city'] = 'New York'

Real-world applications:
- Maintaining a list of tasks in a to-do app in the order they were added.
- Storing a sequence of events in a log file.

Mapping

What is it?
- A generic type that represents a mapping relationship between keys and values.
- Maps keys to their corresponding values, allowing you to retrieve values based on their keys.

How to use it:

from typing import Mapping

def get_value(mapping: Mapping[str, int], key: str) -> int:
    return mapping[key]  # Returns the value associated with the given key

Real-world applications:
- Representing configuration settings in an application, where keys are the setting names and values are the setting values.
- Storing a list of key-value pairs in a database.

Potential applications:

OrderedDict:
- Task manager: Maintain a to-do list where tasks are ordered by creation date.
- Event logger: Record events in a file in the order they occurred.
Mapping:
- Configuration manager: Store application settings and allow access to them through their names.
- Database storage: Represent data in a table-like structure with key-value pairs.

ChainMap

Concept: ChainMap is a specialized type of dictionary that combines multiple dictionaries into a single, cohesive view. It provides a way to access values from multiple dictionaries as if they were all part of one dictionary.

Simplified Explanation: Imagine you have a backpack with three pockets. Each pocket contains a different set of items. With ChainMap, you can access all the items in all three pockets as if they were in one big backpack.

Code Snippet:

import collections

dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
chainmap = collections.ChainMap(dict1, dict2)

print(chainmap['a'])  # 1
print(chainmap['c'])  # 3

Real-World Applications: ChainMap can be useful when you need to combine data from multiple sources or when you want to create a temporary view of a data structure. For example, you might use ChainMap to:

Combine configuration settings from multiple files.
Create a temporary view of a database that includes data from multiple tables.
Merge data from multiple sources into a single report.

Potential Applications:

Data Integration: Merge data from different sources into a single dataset.
Configuration Management: Combine configuration settings from multiple files.
Data Analysis: Create temporary views of data for analysis.
Cross-Referencing: Link data from multiple dictionaries or data structures.

Counter Class

The Counter class is a specialized Python dictionary that provides an easy way to count and track the frequency of items in a collection. It's based on the collections.Counter class in Python's standard library.

Features of the Counter Class

Counts and stores the frequency of items.
Provides methods for common operations like adding and subtracting items.
Can be used to easily extract the most and least frequent items.

Real-World Example

Suppose you have a list of words from a document and you want to count the frequency of each word. You can use the Counter class as follows:

from typing import Counter

words = ['hello', 'world', 'hello', 'python', 'world', 'python']

counter = Counter(words)

print(counter)
# Counter({'hello': 2, 'world': 2, 'python': 2})

This code snippet demonstrates how to use the Counter class to create a dictionary where the keys are the words and the values are the number of times each word appears in the list.

Applications

The Counter class has various applications in real-world scenarios:

Text Analysis: Counting the frequency of words in a text document for analysis.
Data Science: Analyzing data distributions and identifying patterns.
Machine Learning: Feature extraction and data manipulation.
Inventory Management: Tracking the quantity of items in a warehouse.
Social Media Analysis: Counting the mentions of specific topics or keywords.

Deprecated Alias

In Python 3.9, the Counter class in the typing module became deprecated. This means it's not recommended to use it anymore and it may be removed in future versions of Python. Instead, you should use the collections.Counter class directly.

Deque (Double-Ended Queue)

A deque is a special type of list that allows you to add and remove items from both sides. In other words, it's a queue that behaves like a list.

Example:

from collections import deque

# Create a deque
my_deque = deque([1, 2, 3])

# Add an item to the left side
my_deque.appendleft(0)

# Add an item to the right side
my_deque.append(4)

# Remove an item from the left side
my_deque.popleft()

# Remove an item from the right side
my_deque.pop()

print(my_deque)  # Output: [0, 2, 3, 4]

Applications:

Managing network queues
Implementing a cache that stores items based on a least recently used (LRU) policy
Creating a circular buffer for data analysis

MutableSequence[T]

MutableSequence is a protocol (interface) that allows types to act like a list. It requires the type to support the following operations:

Indexing (e.g., my_sequence[0])
Item assignment (e.g., my_sequence[0] = 1)
Slicing (e.g., my_sequence[1:3])
Concatenation (e.g., my_sequence + [4, 5, 6])
Repetition (e.g., my_sequence * 3)
len() function
in and not in operators

Example:

from typing import MutableSequence

class MySequence(MutableSequence[int]):
    def __init__(self, values):
        self._values = values

    def __getitem__(self, index):
        return self._values[index]

    def __setitem__(self, index, value):
        self._values[index] = value

    def __len__(self):
        return len(self._values)

# Create a MySequence object
my_sequence = MySequence([1, 2, 3])

# Access an item
print(my_sequence[0])  # Output: 1

# Modify an item
my_sequence[0] = 4

# Check if an item exists
print(4 in my_sequence)  # Output: True

Applications:

Defining your own custom list-like types
Providing a consistent interface for different types that behave like lists

Pattern and Match Classes

In Python, the re module provides functions to work with regular expressions, such as re.compile and re.match. These functions return objects of type Pattern and Match.

Pattern Class

The Pattern class represents a compiled regular expression object. It can be used to perform operations like finding matches or splitting strings. For example:

import re

pattern = re.compile(r'\d+')  # Compile a pattern to match digits
match = pattern.search("123 Main Street")  # Find a match in a string
print(match.group())  # Print the matched text: '123'

Typically, you would create a Pattern object once and then use it multiple times.

Match Class

The Match class represents a match object for a regular expression. It provides information about the match, such as the start and end positions, the matched text, and any captured groups. For example:

pattern = re.compile(r'(?P<name>[a-zA-Z]+) (?P<age>\d+)')
match = pattern.match("John Doe 30")  # Find a match in a string
print(match.group("name"))  # Print the captured group: 'John'
print(match.group("age"))  # Print the captured group: '30'

Deprecation

In Python 3.9, the Pattern and Match classes were generalized to support generic string types (AnyStr), including both str and bytes. This means you can now specialize these types as follows:

from typing import Pattern, Match

pattern: Pattern[str] = re.compile(r'\d+')  # Compile a pattern for matching digits in strings
match: Match[str] = pattern.search("123 Main Street")  # Find a match in a string
print(match.group())  # Print the matched text: '123'

Real-World Applications

Regular expressions are used in various real-world applications, including:

Data extraction and validation
Text parsing and manipulation
Search and replace operations
Input validation and error handling

1. Text Class

Simplified Explanation:

The Text class is an alias for the str class. It's used to indicate that a value should be a Unicode string. This was useful in Python 2, where str represented bytes, while in Python 3, str represents Unicode strings.

Code Snippet:

def add_unicode_checkmark(text: Text) -> Text:
    return text + u' ✓'

This function expects a Unicode string as input and returns a Unicode string with a checkmark added to it.

Real World Application:

This is useful when you need to ensure that a certain variable or function argument contains Unicode text. For example, if you're sending data to a database, you may want to make sure it's in Unicode format.

2. Abstract Base Classes

Simplified Explanation:

Abstract Base Classes (ABCs) are classes that define a common interface for a group of related classes. They act like blueprints that provide a set of methods and attributes that the inheriting classes must implement.

Corresponding ABCs in collections.abc:

ABC in collections.abc

Corresponding Type

Container

Any container

Iterable

Any object that can be iterated over

Sequence

Collections that support indexing and slicing

Mapping

Collections that map keys to values

Set

Collections that contain unique elements

MutableMapping

Mappings that can be modified

MutableSequence

Sequences that can be modified

MutableSet

Sets that can be modified

Code Snippet:

from collections.abc import Iterable

class MyIterable:
    def __iter__(self):
        return iter([1, 2, 3])

my_iterable = MyIterable()

for item in my_iterable:
    print(item)

This code defines a class MyIterable that inherits from the Iterable ABC and implements the __iter__ method to return an iterator for the list [1, 2, 3].

Real World Application:

ABCs are used to create generic functions and classes that can work with different types of containers. For example, you can write a function that takes an Iterable as input and iterates over its elements.

AbstractSet

Simplified Explanation:

AbstractSet is an old name for a collection of unique elements. It's now called just "Set" in Python.

Example:

my_set = {"apple", "banana", "cherry"}

Real-World Application:

Removing duplicates from a list:

my_list = [1, 2, 3, 4, 3, 4]
unique_list = set(my_list)
print(unique_list)  # Output: {1, 2, 3, 4}

Collection[T_co]

Simplified Explanation:

Collection[T_co] means a collection that holds objects of type T_co. The "co" part means the type parameter is covariant, meaning it can be replaced with a subtype.

Example:

from typing import Collection, List
my_collection: Collection[int] = [1, 2, 3]  # A list of integers

Real-World Application:

Enforcing type safety when working with collections:

def sum_numbers(numbers: Collection[int]) -> int:
    total = 0
    for num in numbers:
        total += num
    return total

print(sum_numbers([1, 2, 3]))  # Output: 6

collections.abc.Set

Simplified Explanation:

collections.abc.Set is a more recent name for the AbstractSet class. It provides a standard interface for sets in Python.

Example:

import collections.abc
my_set: collections.abc.Set[str] = {"apple", "banana", "cherry"}

Real-World Application:

Working with sets from different libraries that conform to the Set interface:

from collections import Counter
my_counter = Counter("hello")  # A Counter object from the collections module

# Check if my_counter is using a Set for its keys
if isinstance(my_counter.keys(), collections.abc.Set):
    print("my_counter uses a Set for its keys")  # Output: True

ByteString is a type in Python's typing module that represents sequences of bytes. This includes three types of byte sequences:

bytes: An immutable sequence of bytes.
bytearray: A mutable sequence of bytes.
memoryview: A view into a byte sequence.

ByteString is deprecated in Python 3.9 and 3.14, and it is recommended to use collections.abc.Buffer or a union like bytes | bytearray | memoryview instead.

Real-world Examples

Here are some examples of how ByteString can be used in Python code:

# Create a ByteString from a bytes object
my_bytes = b"Hello, world!"
my_byte_string = ByteString(my_bytes)

# Create a ByteString from a bytearray object
my_bytearray = bytearray(b"Hello, world!")
my_byte_string = ByteString(my_bytearray)

# Create a ByteString from a memoryview object
my_memoryview = memoryview(b"Hello, world!")
my_byte_string = ByteString(my_memoryview)

# Iterate over the ByteString
for byte in my_byte_string:
    print(byte)

# Access a byte in the ByteString
my_byte = my_byte_string[0]

# Slice the ByteString
my_sliced_byte_string = my_byte_string[0:5]

Potential Applications

ByteString can be used in a variety of applications, such as:

Working with binary data: ByteString can be used to work with binary data, such as images, audio files, and other types of data that are stored in byte format.
Networking: ByteString can be used to send and receive data over networks, such as in HTTP requests and responses.
Data encryption: ByteString can be used to encrypt and decrypt data, such as in SSL/TLS connections.
Data compression: ByteString can be used to compress and decompress data, such as in ZIP files.

Collections.abc.Collection

Simplified Explanation:

A "Collection" in Python is a bundle of items that can be accessed and grouped together. It's like a box with lots of toys or a folder with many documents.

Details:

Sized: It has a defined size, meaning you can count how many items are in the collection.
Iterable: You can loop through its items one by one.
Container: It can hold items and access them by their position or key.

Code Implementation:

# Define a list as a Collection
my_list = [1, 2, 3, 4, 5]

# Check its size
print(len(my_list))  # Output: 5

# Loop through its items
for item in my_list:
    print(item)  # Prints each item

# Access an item by position
print(my_list[2])  # Output: 3

Real-World Applications:

Lists to store groceries, shopping lists
Dictionaries to store books in a library, phone numbers in a contact list
Sets to store unique members of a group, such as students in a class or friends on social media

Potential Applications:

Data structures in databases
Caching systems
Indexing and searching algorithms

Topic: Deprecated Alias to 'collections.abc.Container'

Explanation:

This is a message in Python's typing module that warns you that the class Container is now obsolete and you should use collections.abc.Container instead.

Code Snippet (Simplified):

from collections.abc import Container

# Use Container instead of Container
my_list: Container[int] = [1, 2, 3]

Real-World Application:

This change helps you write more modern and error-free code by using the correct class name.

Topic: Generic Type Aliases

Explanation:

Generic type aliases allow you to create custom type hints that can be applied to multiple types. In this case, T_co is the type of elements in the container.

Code Snippet (Improved):

from typing import Generic, TypeVar

T = TypeVar("T")  # Define a type variable

class MyContainer(Generic[T]):
    def __init__(self, items: list[T]):
        self.items = items

# Create a container of strings
my_container: MyContainer[str] = MyContainer(["Hello", "World"])

Real-World Application:

Generic type aliases help you write flexible code that can work with different types without sacrificing type safety.

Topic: Support for Subscripting

Explanation:

In Python 3.9, collections.abc.Container now supports subscripting (accessing elements using []).

Code Snippet (Updated):

from collections.abc import Container

# Create a list container
my_list: Container[int] = [1, 2, 3]

# Access the first element
first_element = my_list[0]

Real-World Application:

This change simplifies code by allowing you to use subscripting to access elements in a container.

What is ItemsView?

ItemsView is a type alias that represents the collection of all key-value pairs in a dictionary-like object.

What's different about ItemsView in Python 3.9?

Before Python 3.9, you couldn't access a specific key-value pair in an ItemsView directly. Instead, you had to iterate over all pairs:

items_view = {'a': 1, 'b': 2}.items()
for key, value in items_view:
    print(f'{key} -> {value}')

In Python 3.9 and later, you can now use subscripting to access a specific pair by its key:

items_view = {'a': 1, 'b': 2}.items()
print(items_view['a'])  # (a, 1)

Deprecated Alias

ItemsView is considered a deprecated alias, meaning it may be removed in future Python versions. Instead, you should use the collections.abc.ItemsView class directly.

Real-World Applications

ItemsView is commonly used in scenarios where you need to iterate over or access key-value pairs in a dictionary-like object.

Example:

Suppose you have a dictionary of students and their grades:

students = {'Alice': 90, 'Bob': 85, 'Carol': 95}

To print all students with their grades, you could use ItemsView:

for name, grade in students.items():
    print(f'{name}: {grade}')

Output:

Alice: 90
Bob: 85
Carol: 95

Simplified Explanation:

KeysView is an old name for a class that represents the keys of a dictionary. It lets you treat the dictionary's keys as a set, which means you can perform set operations like checking for membership, finding the intersection or union of keys, and so on.

Improved Code Snippet:

my_dict = {
    "name": "John",
    "age": 30,
    "email": "john@example.com"
}

# Get the keys of the dictionary as a set
keys = my_dict.keys()

# Check if a key exists in the set
print("name" in keys)  # True

# Find the intersection of keys with another set
other_keys = {"name", "age"}
print(keys & other_keys)  # {'name', 'age'}

# Find the union of keys with another set
print(keys | other_keys)  # {'name', 'age', 'email'}

Applications in the Real World:

Data Validation: Check if user input matches a set of expected keys.
Data Filtering: Extract only the specific keys that you're interested in.
Data Aggregation: Perform operations like counting or summing values associated with a particular set of keys.
Set Theory: Use set operations (intersection, union, etc.) to combine or analyze different sets of keys.

Note:

The KeysView class is now deprecated and replaced by the more general collections.abc.KeysView. However, the functionality remains the same.

Mapping

The Mapping type represents a collection of key-value pairs. In Python, dictionaries are the most common type of mapping.

To simplify, think of a mapping as a list of pairs, where each pair consists of a key and a value. The key is used to identify the value. For example, a dictionary of names and phone numbers could be represented as a mapping, where the keys are the names and the values are the phone numbers.

Here's a simplified example:

phone_book = {
    "Alice": "555-1212",
    "Bob": "555-1213",
}

# Get Alice's phone number
phone_number = phone_book["Alice"]

In this example, the mapping is the phone_book dictionary. The keys are the names, and the values are the phone numbers. To get Alice's phone number, we access the phone_book using the key "Alice".

Potential Applications

Mappings are used in a wide variety of applications, including:

Dictionaries: A dictionary is a collection of key-value pairs. It is used to store data in a way that can be accessed by the key. For example, a dictionary could be used to store the names and phone numbers of contacts.
Caches: A cache is a temporary storage area that is used to speed up access to frequently used data. A cache can be implemented using a mapping, where the keys are the data items and the values are the cached data.
Databases: A database is a collection of data that is organized in a structured way. A database can be implemented using a mapping, where the keys are the records and the values are the data in the records.

MappingView

MappingView is a deprecated alias to collections.abc.MappingView.

collections.abc.MappingView

MappingView is a subclass of Sized that represents a read-only view of a dictionary-like object. It supports the following operations:

len(view) returns the number of elements in the view.
iter(view) returns an iterator over the keys in the view.
view[key] returns the value associated with the specified key in the view.

MappingView is often used when you need to create a read-only version of a dictionary-like object. For example:

my_dict = {'a': 1, 'b': 2, 'c': 3}
my_view = MappingView(my_dict)

# The view is read-only, so you cannot modify it.
try:
    my_view['a'] = 42
except TypeError:
    pass

# You can iterate over the view.
for key in my_view:
    print(key)  # Prints 'a', 'b', 'c'

Real-world applications

MappingView can be used in a variety of real-world applications, such as:

Creating a read-only view of a configuration file.
Iterating over the keys in a dictionary-like object without modifying it.
Creating a subset of a dictionary-like object.

MutableMapping is a collection type that allows you to store and retrieve data using keys. It's similar to a dictionary, but it also allows you to modify the data in place.

Keys are like labels that identify each piece of data. They can be any type of object that can be compared for equality, such as strings, numbers, or even other objects.

Values are the data that you store associated with each key. They can be any type of object.

To create a MutableMapping, you can use the built-in dict class:

my_dict = {}

You can then add items to the MutableMapping using the [] operator:

my_dict["name"] = "John Doe"
my_dict["age"] = 30

You can retrieve items from the MutableMapping using the [] operator:

name = my_dict["name"]
age = my_dict["age"]

You can also delete items from the MutableMapping using the del keyword:

del my_dict["age"]

MutableMappings are useful for storing data that needs to be modified frequently. For example, you could use a MutableMapping to store the user preferences for your application.

Here is an example of how you could use a MutableMapping to store user preferences:

import collections

preferences = collections.MutableMapping()

preferences["name"] = "John Doe"
preferences["age"] = 30
preferences["favorite_color"] = "blue"

print(preferences["name"])  # Output: John Doe

preferences["age"] = 31

print(preferences["age"])  # Output: 31

del preferences["favorite_color"]

print(preferences)  # Output: {'name': 'John Doe', 'age': 31}

MutableSequence is an alias to the collections.abc.MutableSequence class, which represents a mutable sequence of objects. A sequence is an ordered collection of items, and a mutable sequence is one that can be changed (i.e., items can be added, removed, or replaced).

In Python, lists are mutable sequences. You can create a list using square brackets ([]), and you can access items in the list using their index. For example:

my_list = [1, 2, 3]
print(my_list[0])  # prints 1

You can also add items to the end of the list using the append() method, or insert items at any position using the insert() method. For example:

my_list.append(4)
my_list.insert(1, 2.5)
print(my_list)  # prints [1, 2.5, 2, 3, 4]

You can also remove items from the list using the remove() method, or pop() method to remove the last item. For example:

my_list.remove(2)
my_list.pop()
print(my_list)  # prints [1, 2.5, 3]

Mutable sequences are useful in many applications, such as:

Storing data in a specific order
Iterating over a collection of items
Manipulating data by adding, removing, or replacing items

Here is an example of a real-world application of a mutable sequence:

# Create a list of tasks to do
tasks = ["clean the house", "do laundry", "go to the store"]

# Iterate over the list and print each task
for task in tasks:
    print(task)

# Add a new task to the list
tasks.append("cook dinner")

# Remove a task from the list
tasks.remove("go to the store")

# Print the updated list of tasks
print(tasks)

This code creates a list of tasks, iterates over the list and prints each task, adds a new task to the list, removes a task from the list, and then prints the updated list of tasks.

MutableSet

Simplified Explanation:

MutableSet is an old name for a group of items that can be changed, like adding or removing items. It's like a set in math, where you can add or take away numbers.

Technical Definition:

MutableSet is an abstract class that represents a set of items that can be modified. It's a type hint used in Python's type checking system.

Real-World Example:

Imagine you have a basket of fruits. You can put more fruits in or take some out, so the basket is a MutableSet.

Code Example:

fruits = set()  # Create an empty MutableSet
fruits.add("apple")  # Add an item
fruits.remove("pear")  # Remove an item

Potential Applications:

Shopping lists: You can add or remove items as you update your list.
Unique ID collections: You can store unique identifiers and quickly check if a new one is already in the set.
Permission systems: You can use MutableSets to control who has access to certain resources.

Deprecated

Note that MutableSet is an old alias for the more modern collections.abc.MutableSet. The latter supports subscripting (using square brackets []) to access or modify items.

Improved Code Example with Collections.abc.MutableSet:

from collections.abc import MutableSet

fruits_set = MutableSet()
fruits_set.add("mango")
fruits_set.discard("banana")  # Removes the element if it exists (no error)

# Access items using indexing
first_fruit = fruits_set[0]  # Get the first item in the set

Sequence

A sequence is an ordered collection of elements, like a list or a tuple. You can access the elements of a sequence by their index.

my_list = ['apple', 'banana', 'cherry']

# Get the first element
first_element = my_list[0]  # 'apple'

# Get the last element
last_element = my_list[-1]  # 'cherry'

# Get a slice of the list
subset = my_list[1:3]  # ['banana', 'cherry']

Sequences can also be reversed.

my_reversed_list = reversed(my_list)

# Iterate over the reversed list
for item in my_reversed_list:
    print(item)  # 'cherry', 'banana', 'apple'

Collection

A collection is a group of objects that have a common purpose. For example, a set is a collection of unique elements, and a dictionary is a collection of key-value pairs.

my_set = {'apple', 'banana', 'cherry'}

# Check if an element is in the set
'apple' in my_set  # True

# Add an element to the set
my_set.add('grape')

# Remove an element from the set
my_set.remove('banana')

my_dict = {'apple': 'red', 'banana': 'yellow', 'cherry': 'red'}

# Get the value associated with a key
my_dict['apple']  # 'red'

# Add a new key-value pair to the dictionary
my_dict['grape'] = 'purple'

# Remove a key-value pair from the dictionary
del my_dict['banana']

Real-World Applications

Sequences and collections are used in many real-world applications. For example:

A shopping list is a sequence of items that you need to buy.
A phone book is a dictionary of names and phone numbers.
A database table is a collection of rows, each of which represents a record.

Potential Applications

Sequences and collections can be used in a variety of ways. Here are a few ideas:

Create a sequence of your favorite foods and iterate over it to print each food.
Create a dictionary of your friends' names and phone numbers.
Create a set of unique numbers and use it to check if a given number is in the set.

ValuesView Class

The ValuesView class in Python is a deprecated alias to the collections.abc.ValuesView class. The ValuesView class represents a view of the values of a dictionary-like object. It provides a way to iterate over the values of the dictionary without having to access the keys.

Example:

my_dict = {'a': 1, 'b': 2, 'c': 3}

values = my_dict.values()

for value in values:
    print(value)

Output:

1
2
3

Asynchronous Programming and Collections.abc Aliases

Python provides asynchronous versions of some of the abstract base classes (ABCs) in the collections.abc module. These asynchronous ABCs allow you to work with asynchronous iterators and other asynchronous data structures.

The following table shows the asynchronous aliases to the corresponding synchronous ABCs in collections.abc:

Synchronous ABC

Asynchronous Alias

Container

AsyncContainer

Iterable

AsyncIterable

Iterator

AsyncIterator

Sized

AsyncSized

Mapping

AsyncMapping

Sequence

AsyncSequence

Set

AsyncSet

Example:

import asyncio

async def async_generator():
    for i in range(3):
        await asyncio.sleep(1)
        yield i

async_iterable = async_generator()

async for value in async_iterable:
    print(value)

Output:

0
1
2

Potential Applications

ValuesView Class: The ValuesView class can be used to iterate over the values of a dictionary without having to access the keys. This can be useful when you only need the values of the dictionary and don't care about the keys.

Asynchronous Programming and Collections.abc Aliases: Asynchronous programming allows you to write code that can run concurrently with other tasks. The asynchronous ABCs in collections.abc provide a way to work with asynchronous data structures, such as asynchronous iterators and asynchronous queues. This can be useful for writing highly concurrent and responsive applications.

Coroutine

A coroutine is a type of function that can be paused and resumed. It's like a regular function, but it can yield values and receive values from other coroutines. This allows coroutines to be used for tasks that are difficult to express with regular functions, such as iterating over a large dataset or handling asynchronous events.

Awaitable

An awaitable is an object that can be awaited. When an awaitable is awaited, the coroutine that is awaiting it is paused until the awaitable is ready. Once the awaitable is ready, the coroutine resumes and the value of the awaitable is returned.

Generic

A generic type is a type that can be parameterized with other types. For example, the Coroutine type is a generic type that can be parameterized with the return type, yield type, and send type of the coroutine.

Variance

Variance refers to the way that the type of a generic type changes when the type parameters are changed. For example, the Coroutine type is covariant in its return type, which means that the return type of a Coroutine will always be a subtype of the return type of a more generic Coroutine.

Order of type variables

The order of the type variables in a generic type is important. For example, the Coroutine type has the following type variables:

ReturnType
YieldType
SendType

The order of these type variables corresponds to the order of the arguments to the await operator. For example, the following code awaits a Coroutine that returns an integer and yields a string:

async def my_coroutine() -> int:
    yield "Hello"

async def main():
    result = await my_coroutine()

In this example, the result variable will have the type int.

Real world applications

Coroutines can be used in a variety of real-world applications, including:

Asynchronous programming: Coroutines can be used to write asynchronous code, which is code that can be executed concurrently with other code.
Iterating over large datasets: Coroutines can be used to iterate over large datasets in a memory-efficient way.
Handling events: Coroutines can be used to handle events, such as network events or user input events.

Potential applications in real world

Here are some potential applications of coroutines in the real world:

Web development: Coroutines can be used to write web applications that can handle multiple requests concurrently.
Data science: Coroutines can be used to write data science pipelines that can process large datasets efficiently.
Game development: Coroutines can be used to write games that can handle multiple events concurrently.

Code implementations and examples

Here is an example of a simple coroutine:

async def my_coroutine():
    yield "Hello"

This coroutine can be awaited as follows:

async def main():
    result = await my_coroutine()

The result variable will have the value "Hello".

Here is an example of a more complex coroutine:

async def my_coroutine():
    for i in range(10):
        yield i

This coroutine can be used to iterate over the numbers from 0 to 9 as follows:

async def main():
    for i in my_coroutine():
        print(i)

The output of this code will be:

Async Generator

An async generator is a type of generator that can be used to asynchronously produce a sequence of values. This means that you can use them to create asynchronous iterators, which can be used to iterate over a sequence of values without having to wait for each value to be produced.

Generic Types

Async generators can be annotated with generic types. This allows you to specify the type of values that the generator will yield and the type of values that can be sent to the generator. For example, the following code defines an async generator that yields integers and can receive floats:

async def echo_round() -> AsyncGenerator[int, float]:
    sent = yield 0
    while sent >= 0.0:
        rounded = await round(sent)
        sent = yield rounded

Send Type

The SendType parameter of an async generator specifies the type of values that can be sent to the generator. This parameter behaves contravariantly, which means that the type of values that can be sent to a generator is a subtype of the type of values that can be sent to a more general generator. For example, the following code defines an async generator that can receive any value:

async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
    while True:
        yield start
        start = await increment(start)

Return Type

Unlike normal generators, async generators cannot return a value. This is because the generator is responsible for producing the sequence of values, and it cannot return a value until the sequence is complete. However, you can annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]. This allows you to specify the type of the iterator that will be returned by the generator. For example, the following code defines an async generator that returns an async iterator of integers:

async def infinite_stream(start: int) -> AsyncIterator[int]:
    while True:
        yield start
        start = await increment(start)

Potential Applications

Async generators can be used in a variety of applications, including:

Asynchronous iterators: Async generators can be used to create asynchronous iterators, which can be used to iterate over a sequence of values without having to wait for each value to be produced. This can be useful for applications such as streaming data from a server or processing large datasets.
Event loops: Async generators can be used to create event loops, which can be used to manage asynchronous tasks. This can be useful for applications that need to perform multiple asynchronous tasks concurrently.
Concurrency: Async generators can be used to create concurrent code, which can be used to improve the performance of applications that need to perform multiple tasks at the same time. This can be useful for applications such as web servers or database applications.

AsyncIterable

Simplified Explanation:

Imagine you have a box filled with toys. If you wanted to see all the toys in the box, you would need to reach in and grab one toy at a time. An AsyncIterable is like a magical box that lets you grab toys without reaching into the box. It's a list of items that you can loop through without having to wait for each item to be fetched.

Technical Explanation:

AsyncIterable[T] is a generic type in Python's typing module. It represents an async iterable object. An async iterable is a collection of items that can be iterated over asynchronously. This means that instead of waiting for all the items to be available before starting to iterate, you can start iterating immediately and the items will be fetched as you need them.

Code Snippet:

async def get_toys() -> AsyncIterable[str]:
    """Returns an async iterable of toy names."""
    async for toy in toys:
        yield toy

This code creates an AsyncIterable of toy names. The async for loop iterates over the toys asynchronously, meaning that you can start printing the toy names as soon as they are available.

Real-World Applications:

AsyncIterables are useful in situations where you need to iterate over a large number of items and you don't want to wait for all of them to be available before starting. For example, you could use an AsyncIterable to iterate over the results of a database query or to stream data from a server.

Benefits of AsyncIterables:

Non-blocking: They allow you to iterate over items without waiting for all of them to be available.
Efficient: They can improve performance by reducing the amount of time spent waiting for items to be fetched.
Convenient: They provide a simple way to iterate over async collections.

Deprecation:

The AsyncIterable type alias is deprecated in Python 3.9. It's recommended to use the collections.abc.AsyncIterable type instead.

AsyncIterator

Imagine a class that represents a sequence of values that you can iterate over asynchronously. This is like a regular iterator, but it lets you get the next value in the sequence without waiting for the current value to finish processing.

AsyncIterable

A class that represents a sequence of values that you can iterate over asynchronously. This class is used to create AsyncIterator objects.

Potential Applications

AsyncIterators and AsyncIterables are useful for creating asynchronous generators, which can be used to stream data from a source without having to load the entire dataset into memory. This can be helpful for processing large datasets or for streaming data from a remote source.

Example

Here's an example of how to use an AsyncIterator:

import asyncio

async def get_numbers(start, stop):
    for i in range(start, stop):
        await asyncio.sleep(1)
        yield i

async def main():
    async for number in get_numbers(0, 10):
        print(number)

asyncio.run(main())

This code will print the numbers from 0 to 9, with a one-second delay between each number. The get_numbers function is an asynchronous generator that yields the numbers one by one. The main function is an asynchronous coroutine that iterates over the numbers and prints them.

1. Awaitable Class

Definition: An alias to the Awaitable ABC in the collections.abc module.

Purpose: Represents an object that can be awaited for its result. For example, a coroutine.

Usage:

def my_coroutine():
    result = await some_async_function()
    return result

# Create an `Awaitable` instance
coroutine_object = my_coroutine()

# Wait for the result
result = await coroutine_object

2. Deprecation Notice

The Awaitable class is deprecated in Python 3.9. Use the collections.abc.Awaitable class instead. The new class supports subscripting (using []), which was added in Python 3.9 for generically aliased ABCs.

3. Aliases to Other ABCs in collections.abc

The typing.py module provides aliases to several ABCs in the collections.abc module. These aliases make it easier to work with generic types in Python.

Common Aliases:

Alias

Corresponding ABC

Iterable

collections.abc.Iterable

Iterator

collections.abc.Iterator

Sized

collections.abc.Sized

Container

collections.abc.Container

Callable

collections.abc.Callable

Mapping

collections.abc.Mapping

MutableMapping

collections.abc.MutableMapping

Sequence

collections.abc.Sequence

MutableSequence

collections.abc.MutableSequence

Set

collections.abc.Set

MutableSet

collections.abc.MutableSet

Usage: For example, instead of writing:

if isinstance(my_list, collections.abc.Sequence):
    ...

You can write:

if isinstance(my_list, Sequence):
    ...

Applications:

These aliases are used extensively in Python's type system to define generic types. For example, the list type is defined as:

class list(Sequence[T]):
    # ...

This means that list is a sequence that stores elements of type T.

Iterable:

An iterable is a collection of items that can be iterated over one at a time. For example, a list, tuple, or set is an iterable.

Generic:

Generics allow us to define types that can hold different types of values. For example, we can define a list that can hold any type of value using the syntax List[T].

Deprecated:

Deprecated means that the class or function is no longer recommended for use and will likely be removed in a future version of Python. In this case, Iterable is deprecated in favor of collections.abc.Iterable.

collections.abc.Iterable:

collections.abc.Iterable is an abstract base class that defines the interface for iterables. It has a __iter__ method that returns an iterator, which can be used to iterate over the items in the iterable.

Real-World Example:

# Create a list of numbers
numbers = [1, 2, 3, 4, 5]

# Iterate over the numbers using a for loop
for number in numbers:
    print(number)

Output:

Potential Applications:

Iterables are used in many real-world applications, such as:

Looping over items in a list or other collection
Creating generators
Iterating over lines in a file
Passing iterables to functions that expect sequences

Iterator

An iterator is an object that allows you to iterate over a sequence of items. It does this by providing a next() method, which returns the next item in the sequence.

my_list = [1, 2, 3, 4, 5]
my_iterator = iter(my_list)
print(next(my_iterator))  # 1
print(next(my_iterator))  # 2
print(next(my_iterator))  # 3

An iterator is useful when you need to iterate over a sequence of items but you don't need to store the entire sequence in memory. For example, you can use an iterator to read a file line by line without having to load the entire file into memory.

Callable

A callable is an object that can be called like a function. This includes functions, classes, and methods.

def my_function(x):
  return x + 1

my_callable = my_function
print(my_callable(2))  # 3

A callable can also be used as a parameter to another function.

def apply_function(function, x):
  return function(x)

print(apply_function(my_function, 2))  # 3

Callables are useful in a variety of situations, such as when you need to pass a function as an argument to another function, or when you need to create a function that can be called in different ways.

Real-world examples

Iterators are used in for loops to iterate over sequences of items.
Callables are used to create event handlers, callbacks, and other functions that can be called in response to events.

Potential applications

Iterators can be used to process large data sets without having to load the entire data set into memory.
Callables can be used to create reusable code that can be easily adapted to different situations.

Generators

Generators are a type of iterable in Python that produce values one at a time, instead of storing them all in memory. This can be useful when you need to process a large amount of data without using a lot of memory.

Generators are created using the yield keyword. For example, the following generator produces a sequence of numbers from 0 to 9:

def generate_numbers():
    for i in range(10):
        yield i

You can then iterate over a generator using a for loop. For example, the following code prints the numbers from 0 to 9:

for number in generate_numbers():
    print(number)

Generic Types

Generic types are a way to create types that can work with different types of data. For example, the following generic type can be used to create a generator that produces any type of data:

def generate_any_type(T):
    for i in range(10):
        yield i

You can then use this generic type to create a generator that produces a sequence of strings:

strings = generate_any_type(str)

Or a sequence of numbers:

numbers = generate_any_type(int)

Real-World Applications

Generators are often used in real-world applications to process large amounts of data without using a lot of memory. For example, generators can be used to:

Parse large log files
Process data from a database
Generate data for machine learning models

Complete Code Implementations

Here is a complete code implementation of a generator that produces a sequence of numbers from 0 to 9:

def generate_numbers():
    for i in range(10):
        yield i

# Iterate over the generator
for number in generate_numbers():
    print(number)

Here is a complete code implementation of a generic type that can be used to create a generator that produces any type of data:

def generate_any_type(T):
    for i in range(10):
        yield i

# Create a generator that produces a sequence of strings
strings = generate_any_type(str)

# Create a generator that produces a sequence of numbers
numbers = generate_any_type(int)

# Iterate over the generators
for string in strings:
    print(string)

for number in numbers:
    print(number)

Hashable

Definition: An object that can be hashed. This means it can be converted to a unique integer value that can be used to identify the object in a set or dictionary.

Example:

>>> class MyClass:
...     def __init__(self, value):
...         self.value = value
...     def __hash__(self):
...         return hash(self.value)

>>> obj = MyClass(123)
>>> hash(obj)
-1976681373677319346

In this example, the MyClass class defines a __hash__ method that returns the hash value of the value attribute. This allows the MyClass object to be used as a key in a dictionary or set.

Potential Applications: Hashing is used in a variety of applications, such as:

Set and Dictionary Keys: Sets and dictionaries use hashing to identify objects. This allows them to quickly find and retrieve objects by their keys.
Caching: Caching systems use hashing to store and retrieve objects from a cache. This allows them to quickly find and retrieve objects without having to access the underlying data store.
Database Indexing: Databases use hashing to index data. This allows them to quickly find and retrieve data based on specific criteria.

Reversible

What is it?

It is a deprecated alias to :class:collections.abc.Reversible. It allows you to iterate over an object in both forward and backward directions.

Example:

from typing import Reversible

class MyList(list, Reversible):
    def __reversed__(self):
        return reversed(self)

my_list = MyList([1, 2, 3])
print(list(reversed(my_list)))  # [3, 2, 1]

Real-world applications:

Iterating over a list of items in reverse order.
Undoing an action by reversing the order of operations.

Deprecation:

This class has been deprecated in Python 3.9. :class:collections.abc.Reversible now supports subscripting ([]), which makes the :class:Reversible alias unnecessary.

Recommendation:

Use :class:collections.abc.Reversible instead of :class:Reversible.

Sized

Explanation:

The Sized class is a type alias that points to the Sized abstract base class (ABC) defined in the collections.abc module. An ABC is a class that declares a set of methods and properties that any class that inherits from it must implement. In this case, the Sized ABC requires any class that inherits from it to implement the __len__ method, which returns the number of elements in the class instance.

Example:

from typing import Sized

class MyList(Sized):
    def __init__(self, items):
        self._items = items

    def __len__(self):
        return len(self._items)

In this example, we create a MyList class that inherits from the Sized ABC. This means that our class must implement the __len__ method, which we do by simply returning the length of the _items attribute.

Applications:

The Sized ABC is useful for writing code that needs to work with any type of collection that can be counted, such as lists, tuples, and dictionaries. For example, the following function uses a Sized parameter to ensure that its argument is a collection that can be counted:

def count_elements(collection: Sized) -> int:
    return len(collection)

Aliases to contextlib ABCs

Explanation:

Python's contextlib module provides a number of context managers, which are classes that allow you to define a block of code that should be executed with a specific set of resources or settings. For example, the contextlib.closing context manager can be used to ensure that a file is closed properly, even if an exception occurs. The typing module provides aliases to the ABCs defined in the contextlib module, so that you can use them in type annotations.

Example:

from typing import ContextManager

with ContextManager() as cm:
    # Some code that uses the context manager

In this example, we use the ContextManager alias to annotate the type of the context manager we are using. This helps to ensure that the context manager is used correctly and that it has the expected behavior.

Applications:

The aliases to the contextlib ABCs are useful for writing code that makes use of context managers. For example, the following function uses a ContextManager parameter to ensure that its argument is a context manager:

def use_context_manager(context_manager: ContextManager):
    with context_manager as cm:
        # Some code that uses the context manager

This function can be used to ensure that the context manager is used properly and that it has the expected behavior.

Context Manager

Simplified Explanation:

A context manager is a way to temporarily manage resources or perform cleanup actions in Python.

How it Works:

Context managers work using the "with" statement. When you enter a "with" block, the context manager is activated. The context manager can then perform actions or allocate resources. When you exit the "with" block, the context manager performs cleanup actions or releases the resources.

Example:

with open('file.txt', 'w') as f:
    f.write('Hello, world!')

In this example, the open function returns a context manager for the file. The "with" statement activates the context manager and opens the file. The file is then written to and closed when the "with" block exits.

Generic Type:

The ContextManager class is a generic type. This means it can be used with any type of object. The type of the object being managed is specified inside the angle brackets, as shown below:

from typing import ContextManager

# Manage a file object
with open('file.txt', 'w') as f:
    # ...

# Manage a lock object
with lock.acquire() as lock:
    # ...

Deprecation:

The ContextManager class is deprecated in Python 3.9. Instead, you should use the contextlib.AbstractContextManager class. The AbstractContextManager class supports subscripting ([]), which allows you to access the context manager's state.

Real-World Applications:

Context managers have various applications, including:

Managing file I/O
Acquiring locks
Performing cleanup actions
Ensuring resource cleanup even when exceptions occur

Deprecated Alias: AsyncContextManager

The AsyncContextManager class from the typing module is a deprecated alias to the AbstractAsyncContextManager class from the contextlib module. It was introduced in Python 3.5.4 and 3.6.2, but is now deprecated in Python 3.9.

Deprecation Timeline of Major Features

The typing module includes a table summarizing major deprecations for convenience. Here's a simplified explanation of each:

Typing versions of standard collections:
- Deprecation: Python 3.9
- Potential Removal: Undecided
- Reason: To promote the use of standard collections and avoid confusion.
typing.ByteString:
- Deprecation: Python 3.9
- Projected Removal: Python 3.14
- Reason: It is no longer used in the standard library.
typing.Text:
- Deprecation: Python 3.11
- Projected Removal: Undecided
- Reason: To align with the removal of unicode from the standard library.
typing.Hashable and typing.Sized:
- Deprecation: Python 3.12
- Projected Removal: Undecided
- Reason: They are redundant with the built-in protocol checks.
typing.TypeAlias:
- Deprecation: Python 3.12
- Projected Removal: Undecided
- Reason: To align with the introduction of PEP 695, which provides a new type alias syntax.
@typing.no_type_check_decorator <no_type_check_decorator>:
- Deprecation: Python 3.13
- Projected Removal: Python 3.15
- Reason: It is no longer necessary due to improvements in type checking infrastructure.
typing.AnyStr:
- Deprecation: Python 3.13
- Projected Removal: Python 3.18
- Reason: It is no longer necessary due to the introduction of "string union" types.

Real-World Examples:

Using a context manager:

with open('file.txt') as f:
    # Do stuff with the file

Checking for type compatibility:

def my_function(a: list[int]) -> bool:
    # Do stuff with a list of integers

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