Intermediate Python: Dunder-Methods, Decorators, and Context Managers


In this lesson we learn about dunder methods, decorators, and context managers in Python. These tools, when used properly, can help us to write concise, readable, and expressive code when working with complex objects, higher-order functions, or scoped side-effects.

Learning Material

Review the introduction to the Python data model, section 3.1.

Read through the “Special method names” section of the Python Data Model. Specifically, focus on sections 3.3.1, 3.3.2, 3.3.6, 3.3.7, and 3.3.8.

Read through the definition of a decorator in the Python glossary.

Read through the Python documentation for with-statements, the context manager type, and the contextlib module.

Read this informational article on understanding decorators.

Finally, read this blog post for additional information on decorators and context managers.


To learn as much as possible from these exercises, write your responses before revealing the provided answers. If any exercises seem irrelevant, you can skip them and instead write a justification as to why they are unimportant. These justifications will help us improve the lesson for future students.

Exercise 1

What is operator overloading, and how is it achievable in Python? When is it appropriate to use operator overloading?


From Wikipedia:

In computer programming, operator overloading, sometimes termed operator ad hoc polymorphism, is a specific case of polymorphism, where different operators have different implementations depending on their arguments

More specifically to Python, operator overloading allows the programmer to hook custom classes into fundamental operators of the language like “+” or “==”. Some specific examples of this are accessing NumPy arrays using slice notation (via the dunder method __getitem__), or the fact that string equality means an element-wise comparison of the code points in a unicode string while object equality means comparison of each object’s id() (via the dunder method __eq__). Generally speaking, operator overloading in Python is performed by implementing dunder methods for a class, which define how the class interacts with various operators.

Dunder methods are a powerful way to make certain operations more “pythonic”; however, they should be used with care. A warning from the Python data model:

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense.

A good rule of thumb for when to use operator overloading: someone who does not have access to the class definition should be able to guess what is happening when they see the class used with an operator. If it is not clear what “+” means for class MyAvatar, then it is probably not a good use of __add__. However, it may be obvious what “+” means for class ComplexNumber. Operator overloading should always be used to make operations simpler to understand, not just more concise.

(As a side note, don’t forget that Python has built-in support for complex numbers, so there’s no need to write a class ComplexNumber!)

Exercise 2

In a Python shell, observe the output of dir(list()). Explain what the output of this call is, and explain the purpose of each printed “dunder” method.


dir, when called with an object as its argument, “returns an alphabetized list of names comprising (some of) the attributes of the given objects, and of attributes reachble from it.” A more in-depth description of dir can be found in the function’s docstring, accessible via dir.__doc__.

  • __add__: Implements the behavior of this object for the + operator.
  • __class__: Returns the class of which this object is an instance.
  • __contains__: Implements the boolean check somevar in self (i.e. is some value contained in the collection)
  • __delattr__: Implements the behavior for the delattr() function, which as the name implies, deletes an attribute from an object.
  • __delitem__: Implements behavior for del self[x], which deletes the item at position x within self.
  • __dir__: Describes what is listed out when dir() is called. Note that we used dir to get this list of dunder methods in the first place; objects can override what is shown to dir, so some attributes may not be listed by using this method.
  • __doc__: Returns the docstring for the object.
  • __eq__: Implements the boolean check self == some_other.
  • __format__:
  • __ge__: Implements “greater than or equal” comparison
  • __getattribute__: Implements behavior when getattribute(self, 'someattribute') is called
  • __getitem__: Implements indexing behavior, i.e. self[x].
  • __gt__: Implementa “greater than” comparison.
  • __hash__: Returns a unique hash of the object. Some objects cannot, by nature, have a unique hash that does not change through the lifetime of the object. list is one of these; you’ll notice that [].__hash__ is actually None.
  • __iadd__: Implements +=, which performs in-place addition.
  • __imul__ Implements *=, in-place multiplication
  • __init__: The method called when an object is first created.
  • __init_subclass__: A method called when a class is subclassed.
  • __iter__: Implements iterator behavior for self, allowing the object to be used in for loops.
  • __le__: Implementation of “less than or equal”
  • __len__: Returns the object length when accessed via len(self).
  • __lt__: Implements “less than”
  • __mul__: Implements multiplication *.
  • __ne__: Implements the not-equal check self != someval. Usually can just be the logical not of __eq__.
  • __new__: A class method called when creating a new instance of that class.
  • __repr__: Returns the official string representation of the object (i.e. for printing with print(self))
  • __reversed__: Returns a reverse iterator.
  • __rmul__: “Reflected” multiplication, where the right-hand operator is first. This is a fallback method called if the left-hand operator cannot perform normal multiplication.
  • __setattr__: Implements behavior of setattr(self, 'attr_name', 'attr_value')
  • __setitem__: Implements assigning to an index (i.e. self[x] = 5)
  • __sizeof__: Specific to lists; returns the size of the list in memory (bytes).
  • __str__: Returns a string form of the object when called as str(self).
  • __subclasshook__: A method that can be used to alter the result of issubclass(self, SomeClass).

Dunder methods allow customization of essentially every aspect of an object in Python. They should be used carefully in order to preserve readability and minimize unnecessary complexity.

Exercise 3

Write the definition of the repeatme decorator in the following example:

def some_function():
    print("Function has been called")

# prints "Function has been called" 5 times
def repeatme(number_of_repeats):
    def wrapper_function(f, *args, **kwargs):
        def inner():
            for i in range(number_of_repeats):
                f(*args, **kwargs)
        return inner
    return wrapper_function

Exercise 4

What is the purpose of the context manager, and why is it useful in Python’s data model?


The context manager allows the explicit opening and closing of external resources. This is particularly important in Python since the timing of garbage collection of a reference is implementation-specific and not guaranteed. From the Python data model:

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The ‘try…finally’ statement and the ‘with’ statement provide convenient ways to do this.

Context managers are also more generally useful for any kind of operation that requires defined setup and teardown steps.

Exercise 5

What are two different ways you can create a context manager?


A context manager can be created by defining a class with __enter__ and __exit__ methods. Alternatively, it can be created with a generator function decorated with the @contextmanager decorator. The latter approach is usually preferred since it is visually simpler to understand.

Exercise 6

Write the definition of the context manager in this example:

    'timeout': 42,

# TODO: Write definition of temporary_override

assert GLOBAL_CONFIG['timeout'] == 42
with temporary_override('timeout', 23):
    assert GLOBAL_CONFIG['timeout'] == 23
assert GLOBAL_CONFIG['timeout'] == 42
from contextlib import contextmanager

def temporary_override(key, newValue):
    oldValue = GLOBAL_CONFIG[key]
    GLOBAL_CONFIG[key] = newValue
    GLOBAL_CONFIG[key] = oldValue

Exercise 7

Extend the previous context manager so that it restores the global configuration value in the event of an exception inside the with block.

# ...continued from previous question

    assert GLOBAL_CONFIG['timeout'] == 42
    with temporary_override('timeout', 27):
        assert GLOBAL_CONFIG['timeout'] == 27
        raise Exception('Testing exception handling in context managers.')
except Exception:
    assert GLOBAL_CONFIG['timeout'] == 42

The new definition of temporary_override should be:

def temporary_override(key, newValue):
    oldValue = GLOBAL_CONFIG[key]
    GLOBAL_CONFIG[key] = newValue
    GLOBAL_CONFIG[key] = oldValue

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