Adventures in Machine Learning

Mastering Type Checking in Python: Tips and Tricks

Python is a popular programming language, known for its simplicity, readability, and ease of learning. However, like any programming language, it has its quirks and challenges.

One particular area that can be tricky for new Python developers is type checking. Python has a dynamic type system, which means that variables can be reassigned to different types at runtime.

This can lead to unexpected behavior and bugs in your code. However, with the introduction of type hints in Python 3.5, developers now have a powerful tool to help catch these errors before they happen.

In this article, we’ll dive into the different types of type systems, explore the pros and cons of using type hints, and examine some common use cases for type checking in Python. Type Systems:

A type system is a set of rules that determine the types of values that can be assigned to variables in a programming language.

There are several types of type systems, but the three most common are dynamic typing, static typing, and duck typing. Dynamic Typing:

In a language with dynamic typing, the type of a variable is determined at runtime.

This means that a variable can be reassigned to a different type at any point in the program’s execution. For example, in Python, you can assign an integer to a variable, and later assign a string to the same variable without any errors.

Static Typing:

In contrast, languages with static typing require variables to be explicitly declared with a type. Once a variable is declared with a type, it cannot be reassigned to a different type.

This can make the code more rigid and less flexible, but can also catch errors early in the development process. Duck Typing:

Finally, duck typing is a concept in programming where the type of an object is less important than its behavior.

In other words, if an object walks like a duck, swims like a duck, and quacks like a duck, it can be treated like a duck, regardless of its actual type. This can lead to more flexible and dynamic code.

Hello Types:

Now that we’ve covered the basics of type systems, let’s take a look at how we can use type hints in Python to catch errors before they happen. Adding Type Hints to a Function:

Type hints allow you to specify the types of arguments and return values in a function declaration.

For example, here’s a function that takes two integers and returns their sum:


def add(x: int, y: int) -> int:

return x + y


In this example, we’ve used the `int` type hint to specify that both `x` and `y` should be integers, and the return value should also be an integer. If we try to pass a non-integer value as an argument to this function, we’ll get a type error at runtime.

Pros and Cons of Using Type Hints:

There are several advantages to using type hints in your code. First and foremost, they can catch errors early in the development process, before they cause problems down the line.

They also make the code more self-documenting, which can be particularly useful for larger projects or when working with other developers. However, there are also some downsides to using type hints.

They can make the code more verbose and harder to read, particularly for developers who are not familiar with the syntax. They can also be time-consuming to add, particularly for large, pre-existing codebases.

Use Cases for Type Checking:

So when should you use type checking in your Python code? There are several common use cases where type hints can be particularly useful:

– Catching errors early in the development process

– Improving code readability and documentation

– Making the code more maintainable and easier to work with for larger projects or teams

– Some libraries or frameworks require type hints in order to work properly

– Type checking can be particularly useful when working with third-party APIs or databases, which may have strict typing requirements


In conclusion, type checking is an important tool in any Python developer’s toolbox.

By understanding the different types of type systems and the pros and cons of using type hints, you can make informed decisions about when and where to include them in your code. With the right approach, type hints can help catch errors early, improve code readability and documentation, and make your code more maintainable and robust.

Type Systems:

Type systems are an integral part of programming languages, and they help ensure the correctness and safety of code. In Python, type systems can be either static or dynamic, meaning that they can be inferred at runtime or specified explicitly in code.


A subtype is a type that is derived from a parent type, often through inheritance or interface implementation. In Python, subtyping is achieved through class inheritance, where subclasses inherit the properties and methods of their parent class.

Subtyping can lead to more flexible and modular code, as well as improved code reuse. Covariant, Contravariant, and Invariant:

In a type system, the relationships between types can take on several different forms, including covariance, contravariance, and invariance.

Covariant types are types where the subtyping relationship follows the direction of the type constructor. In other words, if a type A is a subtype of a type B, then a List[A] is a subtype of a List[B].

Contravariant types are types where the subtyping relationship is reversed from the original type constructor. In other words, if a type A is a subtype of a type B, then a function that takes a B as input and returns an A is a subtype of a function that takes an A as input and returns a B.

Invariant types are types where the subtyping relationship does not change based on the type constructor. Invariant types can be helpful to ensure that the correct types are being used in a particular part of the code and can help avoid errors and bugs.

Gradual Typing and Consistent Types:

Gradual typing is a concept that allows functions or variables to be optionally typed. This means that a developer can choose to specify the types for some parts of the code but leave others untyped.

Gradual typing can help ensure that types are consistent throughout the codebase while avoiding the need for full type declarations throughout the code. Consistent types are a concept where type annotations for functions or variables are kept consistent throughout the entire codebase.

This allows for improved code readability and easier maintenance, as developers do not need to keep track of multiple different types when working with the same variable or function. Playing with Python Types, Part 1:

In order to illustrate some of these concepts, let’s take a look at an example using a deck of cards in Python.

The Deck of Cards:

In this example, we’ll create a simple deck of cards using Python. We’ll use the tuple data type to represent each card as a pair of values, with the first element representing the rank and the second element representing the suit.


from typing import List, Tuple

Card = Tuple[str, str]

Deck = List[Card]

suits = [‘Hearts’, ‘Diamonds’, ‘Clubs’, ‘Spades’]

ranks = [‘Ace’, ‘2’, ‘3’, ‘4’, ‘5’, ‘6’, ‘7’, ‘8’, ‘9’, ’10’, ‘Jack’, ‘Queen’, ‘King’]

def create_deck() -> Deck:

deck = []

for suit in suits:

for rank in ranks:

card = (rank, suit)


return deck


In this code, we’ve used type aliases to make the code more readable and self-documenting. We’ve defined a `Card` type as a tuple of two strings representing the rank and the suit, and a `Deck` type as a list of `Card` types.

Sequences and Mappings:

In Python, there are two main types of data structures: sequences and mappings. Sequences are ordered collections of objects, such as lists or tuples, while mappings are collections of objects that are accessed by a key, such as dictionaries.

In our deck of cards example, we’re using a list (`List`) to represent the deck, but we could also use a tuple (`Tuple`) or a set (`Set`). “`python

Deck = List[Card] # List of tuples

Deck = Tuple[Card, …] # Tuple of tuples

Deck = Set[Card] # Set of tuples


We’ve also used a list comprehension to create each card in the deck.

List comprehensions are a concise way to create lists in Python, and they’re particularly useful for generating sequences of objects. “`python

cards = [(rank, suit) for rank in ranks for suit in suits]


Type Aliases:

Finally, we’ve used a type alias (`Card`) to make the code more readable and self-documenting.

Type aliases allow developers to define new types that are equivalent to an existing type, making the code easier to read and understand. “`python

Card = Tuple[str, str]


By using a `Tuple` type alias with two `str` types, we’ve made it clear that a `Card` is a tuple of two strings representing the rank and the suit, respectively.


In conclusion, type systems are an essential part of any programming language, and they can help ensure that code is correct and safe. By understanding subtyping, covariance, contravariance, and invariance, as well as gradual typing and consistent types, developers can create more flexible and modular code that is easier to maintain and update.

In our example using a deck of cards, we’ve demonstrated how type aliases, sequences, and mappings can be used to create simple, readable, and easy-to-understand code. The Any Type:

The Any type is a special type in Python that represents any possible value.

This means that a variable or function that is annotated with the Any type can be assigned any value, regardless of its actual type. Using the Any type can be helpful in situations where the type of a variable or function is unknown or may change at runtime.

Type Theory:

Type theory is the study of how values are classified into different types and how these types interact with each other. In programming, type theory is concerned with the rules and constraints that govern how values can be used, assigned, and combined.

Example: The Object(ive) of the Game:

To illustrate the concept of the Any type, let’s imagine that we are designing a game. In this game, the player can collect various items, each of which has different properties and effects.

For example, a sword might increase the player’s attack power, while a potion might restore health. We might define an Item class to represent each item in the game, with different subclasses for different types of items:


class Item:


class Sword(Item):

def __init__(self, attack: int):

self.attack = attack

class Potion(Item):

def __init__(self, health: int): = health


In this example, we’ve used inheritance to create subclasses of the Item class for specific types of items.

We’ve also used type hints to specify the type of the `attack` and `health` properties. However, this design doesn’t account for situations where the player might collect items that we haven’t thought of yet.

To handle this scenario, we can use the Any type to represent the unknown properties of the item:


class Item:


class Sword(Item):

def __init__(self, attack: int):

self.attack = attack

class Potion(Item):

def __init__(self, health: int): = health

class Unknown(Item):

def __init__(self, **kwargs): = kwargs


In this updated version of our code, we’ve added an Unknown subclass of Item that takes any number of keyword arguments (`**kwargs`). This allows us to create an item with any properties we want, even if we haven’t defined a subclass for it yet.

Classes as Types:

In Python, classes can be used as types. This means that a type hint that specifies a class can also be used to indicate that an argument or return value should be an instance of that class.

For example:


from typing import List

class Person:

def __init__(self, name: str, age: int): = name

self.age = age

def sort_by_age(people: List[Person]) -> List[Person]:

return sorted(people, key=lambda x: x.age)


In this example, we’ve used a class (`Person`) as a type hint for both the `people` argument and the return value of the `sort_by_age` function. This ensures that the function only accepts lists of `Person` instances and always returns a list of `Person` instances.

Returning self or cls:

When working with classes, it is common to see methods that return either the instance of the class (`self`) or the class object itself (`cls`). When annotating such methods, it’s important to use the appropriate type hint to indicate that an instance or class object is being returned.


class Person:

def __init__(self, name: str, age: int): = name

self.age = age

def birthday(self) -> None:

self.age += 1


def from_birthyear(cls, name: str, birthyear: int) -> “Person”:

age = 2022 – birthyear

return cls(name, age)


In this example, we’ve used the `self` keyword to indicate that the `birthday` method returns nothing (`None`). We’ve also used the `@classmethod` decorator to indicate that the `from_birthyear` method returns an instance of the `Person` class.

Annotating *args and **kwargs:

Sometimes, a function or method may accept a variable number of arguments or keyword arguments. In these situations, we can use the `*args` and `**kwargs` syntax to specify that an arbitrary number of positional or keyword arguments can be passed in.


from typing import Tuple, Any

def foo(*args: Any, **kwargs: Any) -> Tuple:

return args, kwargs


In this example, we’ve used the `*args` and `**kwargs` syntax to indicate that the `foo` function can accept any number of arguments and keyword arguments. We’ve also used the `Any` type to specify that the type of the arguments and keyword arguments is unknown.


In Python, a callable is anything that can be called as if it were a function. This includes standard functions, lambda functions, and even classes that implement the `__call__’ method.


from typing import Callable

def apply_function(x: int, f: Callable[[int], int]) -> int:

return f(x)

apply_function(3, lambda x: x**2)


In this example, we’ve used the `Callable` type hint to specify that the `f` argument must be a callable that takes an `int` argument and returns an `int`. We’ve also used a lambda function to demonstrate how any callable can be passed into the `apply_function` function and called with the `x` argument.


In conclusion, type hints can be a powerful tool in Python for improving code readability, catch errors early on, and ensure consistency throughout the codebase. By understanding how to use the `Any` type, classes as types, `*args` and `**kwargs`, and callables in conjunction with type hints, developers can create more flexible, modular, and robust code that is easy to maintain and update.

Static Type Checking:

Static type checking is a technique for checking the types of values and expressions at compile-time, rather than runtime. In Python, static type checking can be achieved using third-party tools like