was the first language I used professionally and is the scale with which I measured other languages I learned afterward. It's an Object-Oriented Programming statically-typed language. Hence, Python feels a bit weird because of its dynamic typing approach. Java For example, offers methods , , and . Because all other classes inherit from , directly or indirectly, all objects have these methods by definition. Object equals() hashCode() toString() Object Conversely, was not initially built on OOP principles and is dynamically typed. Yet, any language needs cross-cutting features on unrelated objects. In Python, these are specially-named methods: methods that the runtime interprets in a certain way but that you need to know about. You can call them magic methods. Python The documentation is pretty exhaustive, but it needs examples for beginners. The goal of this post is to list most of these methods and provide these examples so that I can remember them. I've divided it into two parts to make it more digestible. Lifecycle methods Methods in this section are related to the lifecycle of new objects. object.__new__(cls[, ...]) The method is static, though it doesn't need to be explicitly marked as such. The method return a new object instance of type ; then, the runtime will call the (see below) method on the new instance. __new()__ must cls __init__() is meant to customize instance creation of subclasses of immutable classes. __new__() class FooStr(str):                                     #1
    
    def __new__(cls, value):
        return super().__new__(cls, f'{value}Foo')     #2

print(FooStr('Hello'))                                 #3 Inherit from str Create a new instance, whose value is the value passed to the constructor, suffixed with str Foo Print HelloFoo object.__init__(self[, ...]) is the regular initialization method, which you probably know if you've read any basic Python tutorial. The most significant difference with Java is that the superclass method has no implicit calling. One can only wonder how many bugs were introduced because somebody forgot to call the superclass method. __init__() __init__() differs from a constructor in that the object is already created. __init__() class Foo:

  def __init__(self, a, b, c):                         #1
    self.a = a                                         #2
    self.b = b                                         #2
    self.c = c                                         #2

foo = Foo('one', 'two', 'three')
print(f'a={foo.a}, b={foo.b}, c={foo.c}')              #3 The first parameter is the instance itself Initialize the instance Print a=one, b=two, c=three object.__del__(self) If is akin to an , then is its . As in Java, finalizers are unreliable, , there's no guarantee that the interpreter finalizes instances when it shuts down. __init()__ initializer __del__() finalizer e.g. Representation methods Python offers two main ways to represent objects: one "official" for debugging purposes and the other "informal". You can use the former to reconstruct the object. The official representation is expressed via the . The documentation states that the representation must be "information-rich and unambiguous". object.__repr__(self) class Foo:

  def __init__(self, a, b, c):
    self.a = a
    self.b = b
    self.c = c

  def __repr__(self):
    return f'Foo(a={foo.a}, b={foo.b}, c={foo.c})'

foo = Foo('one', 'two', 'three')
print(foo)                                             #1 Print Foo(a=one, b=two, c=three) My implementation returns a , though it's not required. Yet, you can reconstruct the object with the information displayed. string The handles the unofficial representation. As its name implies, it must return a . The default calls . object.__str__(self) string __repr__() Aside from the two methods above, the method returns a representation of the object. The second argument follows the rules of the . Note that the method return a . It's a bit involved so that I won't implement it. object.__format__(self, format_spec) string Format Specification Mini-Language must string Finally, the returns a byte representation of the object. object.__bytes__(self) from pickle import dumps                              #1

class Foo:

  def __init__(self, a, b, c):
    self.a = a
    self.b = b
    self.c = c

  def __repr__(self):
    return f'Foo(a={foo.a}, b={foo.b}, c={foo.c})'

  def __bytes__(self):
    return dumps(self)                                #2

foo = Foo('one', 'two', 'three')
print(bytes(foo))                                     #3 Use the serialization library pickle Delegate to the method dumps() Print the byte representation of foo Comparison methods Let's start with similarities with Java: Python has two methods and that work in the same way. If you define for a class, you define as well. Contrary to Java, if you don't define the former, you define the latter. object.__eq__(self, other) object.__hash__(self) __eq__() must __hash__() must not class Foo:

  def __init__(self, a, b):
    self.a = a
    self.b = b

  def __eq__(self, other):
    if not isinstance(other, Foo):                    #1
      return false
    return self.a == other.a and self.b == other.b    #2

  def __hash__(self):
      return hash(self.a + self.b)                    #3

foo1 = Foo('one', 'two')
foo2 = Foo('one', 'two')
foo3 = Foo('un', 'deux')

print(hash(foo1))
print(hash(foo2))
print(hash(foo3))

print(foo1 == foo2)                                   #4
print(foo2 == foo3)                                   #5 Objects that are not of the same type are not equal by definition Compare the equality of attributes The hash consists of the addition of the two attributes Print True Print False As in Java, and have plenty of gotchas. Some of them are the same, others not. I won't paraphrase the documentation; have a look at it. __eq__()__ __hash__() Other comparison methods are pretty self-explanatory: Method Operator object.__lt__(self, other) < object.__le__(self, other) <= object.__gt__(self, other) > object.__ge__(self, other) >= object.__ne__(self, other) != class Foo:

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

  def __ge__(self, other):
    return self.a >= other.a                          #1

  def __le__(self, other):
    return self.a <= other.a                          #1

foo1 = Foo(1)
foo1 = Foo(1)
foo2 = Foo(2)

print(foo1 >= foo1)                                   #2
print(foo1 >= foo2)                                   #3
print(foo1 <= foo1)                                   #4
print(foo2 <= foo2)                                   #5 Compare the single attribute Print True Print False Print True Print True Note that comparison methods may return something other than a boolean. In this case, Python will transform the value in a boolean using the function. I advise you not to use this implicit conversion. bool() Attribute access methods As seen above, Python allows accessing an object's attributes via the dot notation. If the attribute doesn't exist, Python complains: . However, it's possible to define accessors on a class, via the and methods. The rule is that they are fallbacks: if the attribute doesn't exist, Python calls the method. 'Foo' object has no attribute 'a' synthetic object.__getattr__(self, name) object.__setattr__(self, name, value) class Foo:

  def __init__(self, a):
    self.a = a
  
  def __getattr__(self, attr):
    if attr == 'a':
      return 'getattr a'                              #1
    if attr == 'b':
      return 'getattr b'                              #2

foo = Foo('a')

print(foo.a)                                          #3
print(foo.b)                                          #4
print(foo.c)                                          #5 Return the string if the requested attribute is a Return the string if the requested attribute is b Print a Print getattr b Print None For added fun, Python also offers the . The difference is that it's called , effectively shadowing it. object.__getattribute__(self, name) whether the attribute exists or not class Foo:

  def __init__(self, a):
    self.a = a
  
  def __getattribute__(self, attr):
    if attr == 'a':
      return 'getattr a'                              #1
    if attr == 'b':
      return 'getattr b'                              #2

foo = Foo('a')

print(foo.a)                                          #3
print(foo.b)                                          #4
print(foo.c)                                          #5 Return the string if the requested attribute is a Return the string if the requested attribute is b Print getattr a Print getattr b Print None The function allows returning an object's list of attributes and methods. You can set the list using the method. By default, the list is empty: you need to set it explicitly. Note that it's the developer's responsibility to ensure the list contains actual class members. dir() object.__dir__(self)__ class Foo:

  def __init__(self, a):
    self.a = 'a'
  
  def __dir__(self):                                  #1
    return ['a', 'foo']

foo = Foo('one')

print(dir(foo))                                       #2 Implement the method Display ; Python sorts the list. Note that there's no member, though. ['a', 'foo'] foo Descriptors Python descriptors are accessors delegates, akin to Kotlin's . The idea is to factor a behavior somewhere so other classes can reuse it. In this way, they are the direct consequence of favoring composition over inheritance. They are available for getters, setters, and finalizers, respectively: delegated properties object.__get__(self, instance, owner=None) object.__set__(self, instance, value) object.__delete__(self, instance) Let's implement a descriptor that caches the result of a compute-intensive operation. lazy class Lazy:                                           #1

  def __init__(self):
    self.cache = {}                                   #2
    
  def __get__(self, obj, objtype=None):
    if obj not in self.cache:
      self.cache[obj] = obj._intensiveComputation()   #3
    return self.cache[obj]

class Foo:

  lazy = Lazy()                                       #4

  def __init__(self, name):
    self.name = name
    self.count = 0                                    #5
  
  def _intensiveComputation(self):
    self.count = self.count + 1                       #6
    print(self.count)                                 #7
    return self.name

foo1 = Foo('foo1')
foo2 = Foo('foo2')

print(foo1.lazy)                                      #8
print(foo1.lazy)                                      #8
print(foo2.lazy)                                      #9
print(foo2.lazy)                                      #9 Define the descriptor Initialize the cache Call the intensive computation. Conclusion This concludes the first part of Python magic methods. The second part will focus on class, container, and number-related methods. Originally published at on October 15th, 2023 A Java Geek

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

Optimizing Web Images: On-the-Fly Resizing and Ahead-of-Time Resizing

Mastering Python's 'Magic' Methods: Part 1

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