Ad-hoc objects in Python

Posted on Fri 06 July 2018 in Python

If you know one programming language, it's natural to look for parallels as you learn a new language. In my case, I've coded in JavaScript for many years while Python is much more recent for me. I regularly find myself thinking "in JavaScript, I'd do ..."

Take object handling. I really like JavaScript's dynamic handling of objects. With JS, there's no need to create a class template (though you can). Just create an ad-hoc object and start assigning it properties and methods. For example:

// JavaScript example
var js_obj = {};

// dynamically add a property = 'bar';

// and use it
console.log(; // bar

// you can even create methods
js_obj.sum = function(x, y) {
    return x + y;
// and use them
console.log(js_obj.sum(3, 4)); // 7

What wasn't obvious to me at first was that I could do similar things in Python. I saw the class ClassName() syntax and was fooled into thinking that I had to predefine my class with all its attributes and methods before I could instantiate objects from it. Not perfectly parallel, but in Python, we can do this:

# Python example
class Object(object):

py_obj = Object() = 'baz'

print(  # baz

# we can dynamically assign methods with lambdas
py_obj.sum = lambda x, y: x + y

print(py_obj.sum(3, 4))  # 7

# or even full functions
def exponential(num):
    if num < 0:
        return 'undefined'
    exp = num
    start = num - 1
    for step_down in range(start, 0, -1):
        exp = exp * step_down
    return exp

py_obj.exp = exponential

print(py_obj.exp(5))  # 120

That's great. But a leg-up that JS has is that objects are "associative arrays" meaning that in addition to the dot-notation, we can use array notation (e.g. Python's dict notation) to access properties of JS objects:

// this works in JS:

# but in Python, this wouldn't work

Why would that matter you ask? If you need to dynamically access properties of an object, say by looping through a list of keys returned by an API, the associative array notation comes in very handy.

Here I present a simple class that gives similar functionality in Python. The Object class will create an object that you can access with either dot or dict notation. It offers support for iteration via items() and could be used by methods expecting a __getitem__ accessor.

class Object(object):
    Creates an object for simple key/value storage; enables access via
    object or dictionary syntax (i.e. or obj['foo']).

    def __init__(self, **kwargs):
        for arg in kwargs:
            setattr(self, arg, kwargs[arg])

    def __getitem__(self, prop):
        Enables dict-like access, ie. foo['bar']
        return self.__getattribute__(prop)

    def __str__(self):
        String-representation as newline-separated string useful in print()
        state = [f'{attr}: {val}' for (attr, val) in self.__dict__.items()]
        return '\n'.join(state)

    def items(self):
        Enables enumeration via foo.items()
        return self.__dict__.items()

To use it:

generic_object = Object(foo='bar')

print(  # bar
print(generic_object['foo'])  # bar

// add attributes dynamically
generic_object.baz = 123
print(generic_object.baz)  # 123

# iterate over its attributes
for key, value in generic_object.items():
    print(key, value)

# even use its convenience string representation
print(generic_object)  # prints multi-line string of key/values

This simple generic object class uses a couple of the special attribute names common to all Python objects. By overriding __getitem__, __str__, and items we give custom functionality to objects created from this class.

Just because you can, doesn't mean you should. Many would argue that ad-hoc objects like this are not "Pythonic." There are other data structures in Python that can serve a similar purpose as generic containers of data:

  • There's the Dict (dictionary) and for pre-Python 3.7, OrderedDict. The disadvantage to dicts is the way you access their attributes — my_dict['some_attribute']. It's wordy, and to me inelegant compared to the my_object.some_attribute syntax of objects.
  • Named tuples offer similar functionality, though like "normal" tuples, they are immutable.
  • And starting with Python 3.7, you have data classes (also see Hackernoon's overview article). If you can use Python 3.7, data classes are superior to my simple class above (such as enforcing type hinting) so use them instead.

As always, use the data structure that best suits your needs and will most clearly communicate to future readers of your code its purpose and requirements. But don't be afraid to explore the ins and outs of the language too.