Python "magic" methods - part 2

Let’s continue our exploration of Python’s magic methods in this second part of the series. This part will focus on numbers and containers, i.e., collections. You can read the first part here.

Python provides the usual containers, e.g., lists, sets, and dictionaries. You can use the following methods when you want to implement your own.

Common methods

Containers have a size. Python defines two methods to implement to return the number of items in a container: object.__len__(self) for the exact size and object.__length_hint__(self) for an approximation. You should use the latter when getting the exact size is computationally expensive.

Containers contain objects. Some containers offer index-based access, e.g., list(1), while others offer key-based access, e.g., dict('mykey'). In both cases, here are the methods to implement:

Method Functionality

object.__getitem__(self, key)

Get the object

object.__setitem__(self, key, value)

Set the object

object.__delitem__(self, key)

Remove the object

object.__missing__(self, key)

Called when the key is not found by the default get(key) implementation


Return an iterator over items (or keys) in the container


Reverse the objects in the container

object.__contains__(self, item)

Check whether an item is part of the container

Let’s create a simple hash-map-like container for illustration purposes:

class Container:

  def __init__(self):
      self.items = {}

  def __getattribute__(self, name):
      raise AttributeError()

  def __len__(self):
      return len(self.items)                           (1)

  def __setitem__(self, key, value):
      self.items[key] = value                          (1)

  def __getitem__(self, key):
      return self.items[key]                           (1)

  def __delitem__(self, key):
      return self.items.pop(key)                       (1)

  def __contains__(self, key):
      return key in self.items                         (2)

  def __iter__(self):
      return iter(self.items.keys())                   (3)

  def __reversed__(self):
      return iter(reversed(self.items.keys()))         (4)

container = Container()
container['foo'] = 'foo'
container['bar'] = 'bar'
print(len(container))                                  (5)
for x in container:                                    (6)
    print(f'{x}: {container[x]}')
for x in reversed(container):                          (7)
    print(f'{x}: {container[x]}')
del container['foo']
for x in container:                                    (8)
    print(f'{x}: {container[x]}')
print('foo' in container)                              (9)
1 Delegate on the items dictionary
2 Check if the key belongs to items
3 Get the keys' iterator
4 Get the reversed key’s iterator
5 Print 2 as the container has two items at this point
6 Implicitly calls the __iter__() method
7 Implicitly calls the __reversed__() method
8 Print bar: bar since the foo key has been deleted
9 Implicitly calls the __contains__() method

Just as we can emulate containers, we can emulate numbers as well.

Arithmetic methods

Arithmetic methods abound; it’s easier to summarize them in a table:

Kind Method  Operator/function Comment


object.__add__(self, other)


object.__sub__(self, other)


object.__mul__(self, other)


object.__matmul__(self, other)


Matrix multiplication

object.__truediv__(self, other)


Regular division

object.__floordiv__(self, other)


Division without the reminder

object.__mod__(self, other)


Reminder of the division

object.__divmod__(self, other)


object.__pow__(self, other[, modulo])


object.__lshift__(self, other)


object.__rshift__(self, other)


object.__and__(self, other)


object.__xor__(self, other)


Exclusive OR

object.__or__(self, other)


Inclusive OR


object.__radd__(self, other)


object.__rsub__(self, other)


object.__rmul__(self, other)


object.__rmatmul__(self, other)


object.__rtruediv__(self, other)


object.__rfloordiv__(self, other)


object.__rmod__(self, other)


object.__rdivmod__(self, other)


object.__rpow__(self, other[, modulo])


object.__rlshift__(self, other)


object.__rrshift__(self, other)


object.__rand__(self, other)


object.__rxor__(self, other)


object.__ror__(self, other)



object.__iadd__(self, other)


object.__isub__(self, other)


object.__imul__(self, other)


object.__imatmul__(self, other)


object.__itruediv__(self, other)


object.__ifloordiv__(self, other)


object.__imod__(self, other)


object.__ipow__(self, other[, modulo])


object.__ilshift__(self, other)


object.__irshift__(self, other)


object.__iand__(self, other)


object.__ixor__(self, other)


object.__ior__(self, other)









Absolute value



Bitwise NOT

Imagine an e-commerce site with products and stocks of them dispatched in warehouses. We need to subtract stock levels when someone orders and add stock levels when the stock is replenished. Let’s implement the latter with some of the methods we’ve seen so far:

class Warehouse:                                       (1)

  def __init__(self, id):
    self.id = id

  def __eq__(self, other):                             (2)
    if not isinstance(other, Warehouse):
      return False
    return self.id == other.id

  def __repr__(self):                                  (3)
    return f'Warehouse(id={self.id})'

class Product:                                         (1)

  def __init__(self, id):
    self.id = id

  def __eq__(self, other):                             (2)
    if not isinstance(other, Product):
      return False
    return self.id == other.id

  def __repr__(self):                                  (3)
    return f'Product(id={self.id})'

class StockLevel:

  def __init__(self, product, warehouse, quantity):
    self.product = product
    self.warehouse = warehouse
    self.quantity = quantity

  def __add__(self, other):                            (4)
    if not isinstance(other, StockLevel):
      raise Exception(f'{other} is not a StockLevel')
    if self.warehouse != other.warehouse:
      raise Exception(f'Warehouse are not the same {other.warehouse}')
    if self.product != other.product:
      raise Exception(f'Product are not the same {other.product}')
    return StockLevel(self.product, self.warehouse,\
                      self.quantity + other.quantity)  (5)

  def __repr__(self):
    return f'StockLevel(warehouse={self.warehouse},\

warehouse1 = Warehouse(1)
warehouse2 = Warehouse(2)
product = Product(1)                                   (6)
product1 = Product(1)                                  (6)
stocklevel111 = StockLevel(product, warehouse1, 1)     (7)
stocklevel112 = StockLevel(product, warehouse1, 2)     (7)
stocklevel121 = StockLevel(product1, warehouse2, 1)    (7)

print(stocklevel111 + stocklevel112)                   (8)

stocklevel111 + stocklevel121                          (9)
1 Define necessary classes
2 Override equality to compare ids
3 Override representation
4 Implement addition. If the warehouse and product don’t match, raise an exception.
5 Create a new StockLevel with the same product and warehouse and the quantity as the sum of both quantities
6 Define two products that point to the same id; it’s the same product for equality purposes
7 Create new stock-level objects
8 Print StockLevel(warehouse=Warehouse(id=1),product=Product(id=1),quantity=3)
9 Raise an exception as warehouses are different, though products are the same

Conversion methods

Conversion methods allow changing an instance to a numeric type, i.e., int, float, or complex.

Method  Built-in function







If no such method is implemented, Python falls back to the object.__index__(self), for example, when using the instance as an index.

The following sample, however irrelevant it is, highlights the above:

class Foo:

  def __init__(self, id):
    self.id = id

  def __index__(self):                                 (1)
    return self.id

foo = Foo(1)
array = ['a', 'b', 'c']
what = array[foo]                                      (2)
print(what)                                            (3)
1 Define the fallback method
2 Coerce foo into an int. We didn’t implement any conversion method; Python falls back to index()
3 Print b

Other methods

Finally, Python delegates to a magic method when your code calls a specific number-related function.

Method  Built-in function

object.__round__(self[, ndigits])








Context managers' methods

Python’s context managers allow fine-grained control over resources that must be acquired and released. It works with the with keyword. For example, here’s how you open a file to write to:

with open('file', 'w') as f:                           (1)
    f.write('Hello world!')
1 Open the file
2 At this point, Python has closed the file

A context manager is syntactic sugar. The following code is equivalent to the one from above:

f = open('file', 'w')
  f.write('Hello world!')

To write your context manager requires to implement two methods: one for opening the context and one for closing it, respectively, object.__enter__(self) and object.__exit__(self, exc_type, exc_value, traceback).

Let’s write a context manager to manage a pseudo-connection.

import traceback

class Connection:

  def __enter__(self):
    self.connection = Connection()
    return self.connection

  def __exit__(self, exc_type, exc_value, exc_traceback):
    self.connection = None
    if exc_type is not None:
      print('An exception happened')
      print(traceback.format_exception(exc_type, exc_value, exc_traceback))
    return True

  def do_something(self):

with Connection() as connection:

Callable objects

I was first exposed to callable objects in Kotlin. A callable object looks like a function but is an object:

hello = Hello()

The method to implement to make the above code run is object.__call__(self[, args...]).

class Hello:

  def __call__(self, who):
    print(f'Hello {who}!')


The post concludes our 2-part series on Python "magic" methods. I didn’t mention some of them, though, as they are so many. However, they cover the majority of them.

Happy Python!

/user/bdaene did a review of the code on Reddit. Please have a look, it contains lots of interesting comments.
Nicolas Fränkel

Nicolas Fränkel

Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps. Also double as a trainer and triples as a book author.

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Python "magic" methods - part 2
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