Dunder Methods: The Hidden Gems of Python

Real-world examples on how actively using special methods can simplify coding and improve readability.

Dunder methods, though possibly a basic topic in Python, are something I have often noticed being understood only superficially, even by people who have been coding for quite some time.

Disclaimer: This is a forgivable gap, as in most cases, actively using dunder methods “simply” speeds up and standardize tasks that can be done differently. Even when their use is essential, programmers are often unaware that they are writing special methods that belong to the broader category of dunder methods.

Anyway, if you code in Python and are not familiar with this topic, or if you happen to be a code geek intrigued by the more native aspects of a programming language like I am, this article might just be what you’re looking for.

Appearances can deceive… even in Python!

If there is one thing I learned in my life is that not everything is what it seems like at a first look, and Python is no exception.

Let us consider a seemingly simple example:

class EmptyClass:
    pass

This is the “emptiest” custom class we can define in Python, as we did not define attributes or methods. It is so empty you would think you can do nothing with it.

However, this is not the case. For example, Python will not complain if you try to create an instance of this class or even compare two instances for equality:

empty_instance = EmptyClass()
another_empty_instance = EmptyClass()

>>> empty_instance == another_empty_instance
False

Of course, this is not magic. Simply, leveraging a standard object interface, any object in Python inherits some default attributes and methods that allow the user to always have a minimal set of possible interactions with it.

While these methods may seem hidden, they are not invisible. To access the available methods, including the ones assigned by Python itself, just use the dir() built-in function. For our empty class, we get:

>>> dir(EmptyClass)
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', 
'__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', 
'__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', 
'__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', 
'__str__', '__subclasshook__', '__weakref__']

It is these methods that can explain the behaviour we observed earlier. For example, since the class actually has an __init__ method we should not be surprised that we can instantiate an object of the class.

Meet the Dunder Methods

All the methods shown in the last output belongs to the special group of — guess what — dunder methods. The term “dunder” is short for double underscore, referring to the double underscores at the beginning and end of these method names.

They are special for several reasons:

1. They are built into every object: every Python object is equipped with a specific set of dunder methods determined by its type.
2. They are invoked implicitly: many dunder methods are triggered automatically through interactions with Python’s native operators or built-in functions. For example, comparing two objects with == is equivalent to calling their __eq__ method.
3. They are customizable: you can override existing dunder methods or define new ones for your classes to give them custom behavior while preserving their implicit invocation.

For most Python developers, the first dunder they encounter is __init__, the constructor method. This method is automatically called when you create an instance of a class, using the familiar syntax MyClass(*args, **kwargs) as a shortcut for explicitly calling MyClass.__init__(*args, **kwargs).

Despite being the most commonly used, __init__ is also one of the most specialized dunder methods. It does not fully showcase the flexibility and power of dunder methods, which can allow you to redefine how your objects interact with native Python features.

Make an object pretty

Let us define a class representing an item for sale in a shop and create an instance of it by specifying the name and price.

class Item:
    def __init__(self, name: str, price: float) -> None:
        self.name = name
        self.price = price


item = Item(name="Milk (1L)", price=0.99)

What happens if we try to display the content of the item variable? Right now, the best Python can do is tell us what type of object it is and where it is allocated in memory:

>>> item
<__main__.Item at 0x00000226C614E870>

Let’s try to get a more informative and pretty output!

To do that, we can override the __repr__ dunder, which output will be exactly what gets printed when typing a class instance in the interactive Python console but also — as soon as the other dunder method __str__ is not override — when attempting a print() call.

Note: it is a common practice to have __repr__ provide the necessary syntax to recreate the printed instance. So in that latter case we expect the output to be Item(name=”Milk (1L)”, price=0.99).

class Item:
    def __init__(self, name: str, price: float) -> None:
        self.name = name
        self.price = price

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}('{self.name}', {self.price})"


item = Item(name="Milk (1L)", price=0.99)

>>> item # In this example it is equivalent also to the command: print(item)
Item('Milk (1L)', 0.99)

Nothing special, right? And you would be right: we could have implemented the same method and named it my_custom_repr without getting indo dunder methods. However, while anyone immediately understands what we mean with print(item) or just item, can we say the same for something like item.my_custom_repr()?

Define interaction between an object and Python’s native operators

Imagine we want to create a new class, Grocery, that allows us to build a collection of Item along with their quantities.

In this case, we can use dunder methods for allowing some standard operations like:

1. Adding a specific quantity of Item to the Grocery using the + operator
2. Iterating directly over the Grocery class using a for loop
3. Accessing a specific Item from the Grocery class using the bracket [] notation

To achieve this, we will define (we already see that a generic class do not have these methods by default) the dunder methods __add__, __iter__ and __getitem__ respectively.

from typing import Optional, Iterator
from typing_extensions import Self


class Grocery:

    def __init__(self, items: Optional[dict[Item, int]] = None):
        self.items = items or dict()

    def __add__(self, new_items: dict[Item, int]) -> Self:

        new_grocery = Grocery(items=self.items)

        for new_item, quantity in new_items.items():

            if new_item in new_grocery.items:
                new_grocery.items[new_item] += quantity
            else:
                new_grocery.items[new_item] = quantity

        return new_grocery

    def __iter__(self) -> Iterator[Item]:
        return iter(self.items)

    def __getitem__(self, item: Item) -> int:

        if self.items.get(item):
            return self.items.get(item)
        else:
            raise KeyError(f"Item {item} not in the grocery")

Let us initialize a Grocery instance and print the content of its main attribute, items.

item = Item(name="Milk (1L)", price=0.99)
grocery = Grocery(items={item: 3})

>>> print(grocery.items)
{Item('Milk (1L)', 0.99): 3}

Then, we use the + operator to add a new Item and verify the changes have taken effect.

new_item = Item(name="Soy Sauce (0.375L)", price=1.99)
grocery = grocery + {new_item: 1} + {item: 2}

>>> print(grocery.items)
{Item('Milk (1L)', 0.99): 5, Item('Soy Sauce (0.375L)', 1.99): 1}

Friendly and explicit, right?

The __iter__ method allows us to loop through a Grocery object following the logic implemented in the method (i.e., implicitly the loop will iterate over the elements contained in the iterable attribute items).

>>> print([item for item in grocery])
[Item('Milk (1L)', 0.99), Item('Soy Sauce (0.375L)', 1.99)]

Similarly, accessing elements is handled by defining the __getitem__ dunder:

>>> grocery[new_item]
1

fake_item = Item("Creamy Cheese (500g)", 2.99)
>>> grocery[fake_item]
KeyError: "Item Item('Creamy Cheese (500g)', 2.99) not in the grocery"

In essence, we assigned some standard dictionary-like behaviours to our Grocery class while also allowing some operations that would not be natively available for this data type.

Enhance functionality: make classes callable for simplicity and power.

Let us wrap up this deep-dive on dunder methods with a final eample showcasing how they can be a powerful tool in our arsenal.

Imagine we have implemented a function that performs deterministic and slow calculations based on a certain input. To keep things simple, as an example we will use an identity function with a built-in time.sleep of some seconds.

import time 


def expensive_function(input):
    time.sleep(5)
    return input

What happens if we run the function twice on the same input? Well, right now calculation would be executed twice, meaning that we twice get the same output waiting two time for the whole execution time (i.e., a total of 10 seconds).

start_time = time.time()

>>> print(expensive_function(2))
>>> print(expensive_function(2))
>>> print(f"Time for computation: {round(time.time()-start_time, 1)} seconds")
2
2
Time for computation: 10.0 seconds

Does this make sense? Why should we do the same calculation (which leads to the same output) for the same input, especially if it’s a slow process?

One possible solution is to “wrap” the execution of this function inside the __call__ dunder method of a class.

This makes instances of the class callable just like functions — meaning we can use the straightforward syntax my_class_instance(*args, **kwargs) — while also allowing us to use attributes as a cache to cut computation time.

With this approach we also have the flexibility to create multiple process (i.e., class instances), each with its own local cache.

class CachedExpensiveFunction:

    def __init__(self) -> None:
        self.cache = dict()

    def __call__(self, input):
        if input not in self.cache:
            output = expensive_function(input=input)
            self.cache[input] = output
            return output
        else:
            return self.cache.get(input)


start_time = time.time()
cached_exp_func = CachedExpensiveFunction()

>>> print(cached_exp_func(2))
>>> print(cached_exp_func(2))
>>> print(f"Time for computation: {round(time.time()-start_time, 1)} seconds")
2
2
Time for computation: 5.0 seconds

As expected, the function is cached after the first run, eliminating the need for the second computation and thus cutting the overall time in half.

As above mentioned, we can even create separate instances of the class, each with its own cache, if needed.

start_time = time.time()
another_cached_exp_func = CachedExpensiveFunction()

>>> print(cached_exp_func(3))
>>> print(another_cached_exp_func (3))
>>> print(f"Time for computation: {round(time.time()-start_time, 1)} seconds")
3
3
Time for computation: 10.0 seconds

Here we are! A simple yet powerful optimization trick made possible by dunder methods that not only reduces redundant calculations but also offers flexibility by allowing local, instance-specific caching.

My final considerations

Dunder methods are a broad and ever-evolving topic, and this writing does not aim to be an exhaustive resource on the subject (for this purpose, you can refer to the 3. Data model — Python 3.12.3 documentation).

My goal here was rather to explain clearly what they are and how they can be used effectively to handle some common use cases.

While they may not be mandatory for all programmers all the time, once I got a good grasp of how they work they have made a ton of difference for me and hopefully they may work for you as well.

Dunder methods indeed are a way to avoid reinventing the wheel. They also align closely with Python’s philosophy, leading to a more concise, readable and convention-friendly code. And that never hurts, right?

Dunder Methods: The Hidden Gems of Python was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.