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, , collections. You can read the first part . i.e. here Container-related methods provides the usual containers, , lists, sets, and dictionaries. You can use the following methods when you want to implement your own. Python e.g. Common methods Containers have a size. Python defines two methods to implement to return the number of items in a container: for the exact size and for an approximation. You should use the latter when getting the exact size is computationally expensive. object.__len__(self) object.__length_hint__(self) Item-related methods Containers contain objects. Some containers offer index-based access, , , while others offer key-based access, , . In both cases, here are the methods to implement: e.g. list(1) e.g. dict('mykey') 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 implementation get(key) object.__iter__(self) Return an iterator over items (or keys) in the container object.__reversed__(self) 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]}')
print('---')
for x in reversed(container):                          #7
    print(f'{x}: {container[x]}')
print('---')
del container['foo']
for x in container:                                    #8
    print(f'{x}: {container[x]}')
print('---')
print('foo' in container)                              #9 Delegate on the dictionary items Check if the key belongs to items Get the keys' iterator Get the reversed key’s iterator Print 2 as the container has two items at this point Implicitly calls the method __iter__() Implicitly calls the method __reversed__() Print since the key has been deleted bar: bar foo Implicitly calls the method __contains__() Number-related methods 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: Method Operator/function Comment All 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) divmod() object.__pow__(self, other[, modulo]) pow() object.__lshift__(self, other) << object.__rshift__(self, other) >> object.__and__(self, other) & object.__xor__(self, other) ^ Exclusive OR object.__or__(self, other) ` ` Binary 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) divmod() object.__rpow__(self, other[, modulo]) pow() object.__rlshift__(self, other) << object.__rrshift__(self, other) >> object.__rand__(self, other) & object.__rxor__(self, other) ^ object.__ror__(self, other) ` ` Assignment 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]) pow()= object.__ilshift__(self, other) <⇐ object.__irshift__(self, other) >>= object.__iand__(self, other) &= object.__ixor__(self, other) ^= object.__ior__(self, other) ` =` Unary object.__neg__(self) - object.__pos__(self) + object.__abs__(self) abs() Absolute value object.__invert__(self) ~ 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},\
             product={self.product},quantity={self.quantity})'


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 Define necessary classes Override equality to compare ids Override representation Implement addition. If the warehouse and product don’t match, raise an exception. Create a new with the same product and warehouse and the quantity as the sum of both quantities StockLevel Define two products that point to the same id; it’s the same product for equality purposes Create new stock-level objects Print StockLevel(warehouse=Warehouse(id=1),product=Product(id=1),quantity=3) Raise an exception as warehouses are different, though products are the same Conversion methods Conversion methods allow changing an instance to a numeric type, , , , or . i.e. int float complex Method Built-in function object.__complex__(self) complex() object.__int__(self) int() object.__float__(self) float() If no such method is implemented, Python falls back to the , for example, when using the instance as an index. object.__index__(self) 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 Define the fallback method Coerce into an . We didn’t implement any conversion method; Python falls back to foo int index() 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]) round() object.__trunc__(self) trunc() object.__floor__(self) floor() object.__ceil__(self) ceil() Context managers' methods Python’s context managers allow fine-grained control over resources that must be acquired and released. It works with the keyword. For example, here’s how you open a file to write to: with with open('file', 'w') as f:                           #1
    f.write('Hello world!')
                                                       #2 Open the file 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')
try:
  f.write('Hello world!')
finally:
  f.close() To write your context manager requires to implement two methods: one for opening the context and one for closing it, respectively, and . object.__enter__(self) 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):
    pass


with Connection() as connection:
  connection.do_something() Callable objects I was first exposed to callable objects in Kotlin. A callable object looks like a function but is an object: hello = Hello()
hello('world') 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}!') Conclusion 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! did a review of the code on . Please have a look, it contains lots of interesting comments. /user/bdaene Reddit To go further: Special method names PEP 560 – Core support for typing module and generic types Originally published at on October 22nd, 2023 A Java Geek

Calling Rust from Python with pyo3

Mastering Python's 'Magic' Methods: Part 1 

Mastering Python's "Magic" Methods—Part 2: Numbers and Containers

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