Java was the first language I used professionally and is the scale by which I measure other languages I learned afterward. It’s an OOP statically-typed language. Hence, Python feels a bit weird because of its dynamic typing approach.
For example, Object offers methods equals(), hashCode(), and toString().
Because all other classes inherit from Object, directly or indirectly, all objects have these methods by definition.
Conversely, Python 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.
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 __new()__ method is static, though it doesn’t need to be explicitly marked as such.
The method must return a new object instance of type cls;
then, the runtime will call the __init__() (see below) method on the new instance.
__new__() is meant to customize instance creation of subclasses of immutable classes.
class FooStr(str): (1)
def __new__(cls, value):
return super().__new__(cls, f'{value}Foo') (2)
print(FooStr('Hello')) (3)| 1 | Inherit from str |
| 2 | Create a new str instance, whose value is the value passed to the constructor, suffixed with Foo |
| 3 | Print HelloFoo |
object.__init__(self[, ...])
init() 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 __init__() method has no implicit calling.
One can only wonder how many bugs were introduced because somebody forgot to call the superclass method.
__init__() differs from a constructor in that the object is already created.
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)| 1 | The first parameter is the instance itself |
| 2 | Initialize the instance |
| 3 | Print a=one, b=two, c=three |
object.__del__(self)
If __init()__ is akin to an initializer, then __del__() is it’s finalizer.
As in Java, finalizers are unreliable, e.g., there’s no guarantee that the interpreter finalizes instances when it shuts down.
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 object.__repr__(self).
The documentation states that the representation must be "information-rich and unambiguous".
class Foo:
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c
def __repr__(self):
return f'Foo(a={self.a}, b={self.b}, c={self.c})'
foo = Foo('one', 'two', 'three')
print(foo) (1)| 1 | Print Foo(a=one, b=two, c=three) |
My implementation returns a string, though it’s not required.
Yet, you can reconstruct the object with the information displayed.
The object.__str__(self) handles the unofficial representation.
As its name implies, it must return a string.
The default calls __repr__().
Aside from the two methods above, the object.__format__(self, format_spec) method returns a string representation of the object.
The second argument follows the rules of the Format Specification Mini-Language.
Note that the method must return a string.
It’s a bit involved, so that I won’t implement it.
Finally, the object.__bytes__(self) returns a byte representation of the object.
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)Comparison methods
Let’s start with similarities with Java:
Python has two methods object.__eq__(self, other) and object.__hash__(self) that work in the same way.
If you define __eq__() for a class, you must define __hash__() as well.
Contrary to Java, if you don’t define the former, you must not define the latter.
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)| 1 | Objects that are not of the same type are not equal by definition |
| 2 | Compare the equality of attributes |
| 3 | The hash consists of the addition of the two attributes |
| 4 | Print True |
| 5 | Print False |
As in Java, __eq__()__ and __hash__() have plenty of gotchas.
Some of them are the same, others not.
I won’t paraphrase the documentation;
have a look at it.
Other comparison methods are pretty self-explanatory:
| Method | Operator |
|---|---|
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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)| 1 | Compare the single attribute |
| 2 | Print True |
| 3 | Print False |
| 4 | Print True |
| 5 | 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 bool() function.
I advise you not to use this implicit conversion.
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: 'Foo' object has no attribute 'a'.
However, it’s possible to define synthetic accessors on a class, via the object.__getattr__(self, name) and object.__setattr__(self, name, value) methods.
The rule is that they are fallbacks:
if the attribute doesn’t exist, Python calls the method.
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)| 1 | Return the string if the requested attribute is a |
| 2 | Return the string if the requested attribute is b |
| 3 | Print a |
| 4 | Print getattr b |
| 5 | Print None |
For added fun, Python also offers the object.__getattribute__(self, name).
The difference is that it’s called whether the attribute exists or not, effectively shadowing it.
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)| 1 | Return the string if the requested attribute is a |
| 2 | Return the string if the requested attribute is b |
| 3 | Print getattr a |
| 4 | Print getattr b |
| 5 | Print None |
The dir() function allows returning an object’s list of attributes and methods.
You can set the list using the object.__dir__(self)__ 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.
class Foo:
def __init__(self, a):
self.a = 'a'
def __dir__(self): (1)
return ['a', 'foo']
foo = Foo('one')
print(dir(foo)) (2)| 1 | Implement the method |
| 2 | Display ['a', 'foo']; Python sorts the list.
Note that there’s no foo member, though. |
Descriptors
Python descriptors are accessors delegates, akin to Kotlin’s delegated properties. 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:
object.__get__(self, instance, owner=None)object.__set__(self, instance, value)object.__delete__(self, instance)
Let’s implement a lazy descriptor that caches the result of a compute-intensive operation.
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)| 1 | Define the descriptor |
| 2 | Initialize the cache |
| 3 | 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.