Improve Your Python: Python Classes and Object Oriented Programming
The class is a fundamental building block in Python. It is the underpinning for not only many popular programs and libraries, but the Python standard library as well. Understanding what classes are, when to use them, and how they can be useful is essential, and the goal of this article. In the process, we’ll explore what the term Object-Oriented Programming means and how it ties together with Python classes.

Everything Is An Object…
What is the class keyword used for, exactly? Like its function-based cousin def, it concerns the definition of things. While def is used to define a function, class is used to define a class. And what is a class? Simply a logical grouping of data and functions (the latter of which are frequently referred to as “methods” when defined within a class).

What do we mean by “logical grouping”? Well, a class can contain any data we’d like it to, and can have any functions (methods) attached to it that we please. Rather than just throwing random things together under the name “class”, we try to create classes where there is a logical connection between things. Many times, classes are based on objects in the real world (like Customer or Product). Other times, classes are based on concepts in our system, like HTTPRequest or Owner.

Regardless, classes are a modeling technique; a way of thinking about programs. When you think about and implement your system in this way, you’re said to be performing Object-Oriented Programming. “Classes” and “objects” are words that are often used interchangeably, but they’re not really the same thing. Understanding what makes them different is the key to understanding what they are and how they work.

..So Everything Has A Class?
Classes can be thought of as blueprints for creating objects. When I define a Customer class using the class keyword, I haven’t actually created a customer. Instead, what I’ve created is a sort of instruction manual for constructing “customer” objects. Let’s look at the following example code:

class Customer(object):
“””A customer of ABC Bank with a checking account. Customers have the
following properties:

Attributes:
name: A string representing the customer's name.
balance: A float tracking the current balance of the customer's account.
"""

def __init__(self, name, balance=0.0):
"""Return a Customer object whose name is *name* and starting
balance is *balance*."""
self.name = name
self.balance = balance

def withdraw(self, amount):
"""Return the balance remaining after withdrawing *amount*
dollars."""
if amount > self.balance:
raise RuntimeError('Amount greater than available balance.')
self.balance -= amount
return self.balance

def deposit(self, amount):
"""Return the balance remaining after depositing *amount*
dollars."""
self.balance += amount
return self.balance


The class Customer(object) line does not create a new customer. That is, just because we’ve defined a Customer doesn’t mean we’ve created one; we’ve merely outlined the blueprint to create a Customer object. To do so, we call the class’s init method with the proper number of arguments (minus self, which we’ll get to in a moment).

So, to use the “blueprint” that we created by defining the class Customer (which is used to create Customer objects), we call the class name almost as if it were a function: jeff = Customer(‘Jeff Knupp’, 1000.0). This line simply says “use the Customer blueprint to create me a new object, which I’ll refer to as jeff.”

The jeff object, known as an instance, is the realized version of the Customer class. Before we called Customer(), no Customer object existed. We can, of course, create as many Customer objects as we’d like. There is still, however, only one Customer class, regardless of how many instances of the class we create.

self?
So what’s with that self parameter to all of the Customer methods? What is it? Why, it’s the instance, of course! Put another way, a method like withdraw defines the instructions for withdrawing money from some abstract customer’s account. Calling jeff.withdraw(100.0) puts those instructions to use on the jeff instance.

So when we say def withdraw(self, amount):, we’re saying, “here’s how you withdraw money from a Customer object (which we’ll call self) and a dollar figure (which we’ll call amount). self is the instance of the Customer that withdraw is being called on. That’s not me making analogies, either. jeff.withdraw(100.0) is just shorthand for Customer.withdraw(jeff, 100.0), which is perfectly valid (if not often seen) code.

init
self may make sense for other methods, but what about init? When we call init, we’re in the process of creating an object, so how can there already be a self? Python allows us to extend the self pattern to when objects are constructed as well, even though it doesn’t exactly fit. Just imagine that jeff = Customer(‘Jeff Knupp’, 1000.0) is the same as calling jeff = Customer(jeff, ‘Jeff Knupp’, 1000.0); the jeff that’s passed in is also made the result.

This is why when we call init, we initialize objects by saying things like self.name = name. Remember, since self is the instance, this is equivalent to saying jeff.name = name, which is the same as jeff.name = ‘Jeff Knupp. Similarly, self.balance = balance is the same as jeff.balance = 1000.0. After these two lines, we consider the Customer object “initialized” and ready for use.

Be careful what you init

After init has finished, the caller can rightly assume that the object is ready to use. That is, after jeff = Customer(‘Jeff Knupp’, 1000.0), we can start making deposit and withdraw calls on jeff; jeff is a fully-initialized object.

Imagine for a moment we had defined the Customer class slightly differently:

class Customer(object):
“””A customer of ABC Bank with a checking account. Customers have the
following properties:

Attributes:
name: A string representing the customer's name.
balance: A float tracking the current balance of the customer's account.
"""

def __init__(self, name):
"""Return a Customer object whose name is *name*."""
self.name = name

def set_balance(self, balance=0.0):
"""Set the customer's starting balance."""
self.balance = balance

def withdraw(self, amount):
"""Return the balance remaining after withdrawing *amount*
dollars."""
if amount > self.balance:
raise RuntimeError('Amount greater than available balance.')
self.balance -= amount
return self.balance

def deposit(self, amount):
"""Return the balance remaining after depositing *amount*
dollars."""
self.balance += amount
return self.balance


This may look like a reasonable alternative; we simply need to call set_balance before we begin using the instance. There’s no way, however, to communicate this to the caller. Even if we document it extensively, we can’t force the caller to call jeff.set_balance(1000.0) before calling jeff.withdraw(100.0). Since the jeff instance doesn’t even have a balance attribute until jeff.set_balance is called, this means that the object hasn’t been “fully” initialized.

The rule of thumb is, don’t introduce a new attribute outside of the init method, otherwise you’ve given the caller an object that isn’t fully initialized. There are exceptions, of course, but it’s a good principle to keep in mind. This is part of a larger concept of object consistency: there shouldn’t be any series of method calls that can result in the object entering a state that doesn’t make sense.

Invariants (like, “balance should always be a non-negative number”) should hold both when a method is entered and when it is exited. It should be impossible for an object to get into an invalid state just by calling its methods. It goes without saying, then, that an object should start in a valid state as well, which is why it’s important to initialize everything in the init method.

Instance Attributes and Methods
An function defined in a class is called a “method”. Methods have access to all the data contained on the instance of the object; they can access and modify anything previously set on self. Because they use self, they require an instance of the class in order to be used. For this reason, they’re often referred to as “instance methods”.

If there are “instance methods”, then surely there are other types of methods as well, right? Yes, there are, but these methods are a bit more esoteric. We’ll cover them briefly here, but feel free to research these topics in more depth.

Static Methods
Class attributes are attributes that are set at the class-level, as opposed to the instance-level. Normal attributes are introduced in the init method, but some attributes of a class hold for all instances in all cases. For example, consider the following definition of a Car object:

class Car(object):

wheels = 4

def __init__(self, make, model):
self.make = make
self.model = model


mustang = Car(‘Ford’, ‘Mustang’)
print mustang.wheels

print Car.wheels

# 4

A Car always has four wheels, regardless of the make or model. Instance methods can access these attributes in the same way they access regular attributes: through self (i.e. self.wheels).

There is a class of methods, though, called static methods, that don’t have access to self. Just like class attributes, they are methods that work without requiring an instance to be present. Since instances are always referenced through self, static methods have no self parameter.

The following would be a valid static method on the Car class:

class Car(object):

def make_car_sound():
print ‘VRooooommmm!’
No matter what kind of car we have, it always makes the same sound (or so I tell my ten month old daughter). To make it clear that this method should not receive the instance as the first parameter (i.e. self on “normal” methods), the @staticmethod decorator is used, turning our definition into:

class Car(object):

@staticmethod
def make_car_sound():
print ‘VRooooommmm!’
Class Methods
A variant of the static method is the class method. Instead of receiving the instance as the first parameter, it is passed the class. It, too, is defined using a decorator:

class Vehicle(object):

@classmethod
def is_motorcycle(cls):
return cls.wheels == 2
Class methods may not make much sense right now, but that’s because they’re used most often in connection with our next topic: inheritance.

Inheritance
While Object-oriented Programming is useful as a modeling tool, it truly gains power when the concept of inheritance is introduced. Inherticance is the process by which a “child” class derives the data and behavior of a “parent” class. An example will definitely help us here.

Imagine we run a car dealership. We sell all types of vehicles, from motorcycles to trucks. We set ourselves apart from the competition by our prices. Specifically, how we determine the price of a vehicle on our lot: $5,000 x number of wheels a vehicle has. We love buying back our vehicles as well. We offer a flat rate - 10% of the miles driven on the vehicle. For trucks, that rate is$10,000. For cars, $8,000. For motorcycles,$4,000.

If we wanted to create a sales system for our dealership using Object-oriented techniques, how would we do so? What would the objects be? We might have a Sale class, a Customer class, an Inventory class, and so forth, but we’d almost certainly have a Car, Truck, and Motorcycle class.

What would these classes look like? Using what we’ve learned, here’s a possible implementation of the Car class:

class Car(object):
“””A car for sale by Jeffco Car Dealership.

Attributes:
wheels: An integer representing the number of wheels the car has.
miles: The integral number of miles driven on the car.
make: The make of the car as a string.
model: The model of the car as a string.
year: The integral year the car was built.
sold_on: The date the vehicle was sold.
"""

def __init__(self, wheels, miles, make, model, year, sold_on):
"""Return a new Car object."""
self.wheels = wheels
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on

def sale_price(self):
"""Return the sale price for this car as a float amount."""
if self.sold_on is not None:
return 5000.0 * self.wheels

def purchase_price(self):
"""Return the price for which we would pay to purchase the car."""
if self.sold_on is None:
return 0.0  # Not yet sold
return 8000 - (.10 * self.miles)

...


OK, that looks pretty reasonable. Of course, we would likely have a number of other methods on the class, but I’ve shown two of particular interest to us: sale_price and purchase_price. We’ll see why these are important in a bit.

Now that we’ve got the Car class, perhaps we should crate a Truck class? Let’s follow the same pattern we did for car:

class Truck(object):
“””A truck for sale by Jeffco Car Dealership.

Attributes:
wheels: An integer representing the number of wheels the truck has.
miles: The integral number of miles driven on the truck.
make: The make of the truck as a string.
model: The model of the truck as a string.
year: The integral year the truck was built.
sold_on: The date the vehicle was sold.
"""

def __init__(self, wheels, miles, make, model, year, sold_on):
"""Return a new Truck object."""
self.wheels = wheels
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on

def sale_price(self):
"""Return the sale price for this truck as a float amount."""
if self.sold_on is not None:
return 5000.0 * self.wheels

def purchase_price(self):
"""Return the price for which we would pay to purchase the truck."""
if self.sold_on is None:
return 0.0  # Not yet sold
return 10000 - (.10 * self.miles)

...


Wow. That’s almost identical to the car class. One of the most important rules of programming (in general, not just when dealing with objects) is “DRY” or “Don’t Repeat Yourself. We’ve definitely repeated ourselves here. In fact, the Car and Truck classes differ only by a single character (aside from comments).

So what gives? Where did we go wrong? Our main problem is that we raced straight to the concrete: Cars and Trucks are real things, tangible objects that make intuitive sense as classes. However, they share so much data and functionality in common that it seems there must be an abstraction we can introduce here. Indeed there is: the notion of Vehicles.

Abstract Classes
A Vehicle is not a real-world object. Rather, it is a concept that some real-world objects (like cars, trucks, and motorcycles) embody. We would like to use the fact that each of these objects can be considered a vehicle to remove repeated code. We can do that by creating a Vehicle class:

class Vehicle(object):
“””A vehicle for sale by Jeffco Car Dealership.

Attributes:
wheels: An integer representing the number of wheels the vehicle has.
miles: The integral number of miles driven on the vehicle.
make: The make of the vehicle as a string.
model: The model of the vehicle as a string.
year: The integral year the vehicle was built.
sold_on: The date the vehicle was sold.
"""

base_sale_price = 0

def __init__(self, wheels, miles, make, model, year, sold_on):
"""Return a new Vehicle object."""
self.wheels = wheels
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on

def sale_price(self):
"""Return the sale price for this vehicle as a float amount."""
if self.sold_on is not None:
return 5000.0 * self.wheels

def purchase_price(self):
"""Return the price for which we would pay to purchase the vehicle."""
if self.sold_on is None:
return 0.0  # Not yet sold
return self.base_sale_price - (.10 * self.miles)


Now we can make the Car and Truck class inherit from the Vehicle class by replacing object in the line class Car(object). The class in parenthesis is the class that is inherited from (object essentially means “no inheritance”. We’ll discuss exactly why we write that in a bit).

We can now define Car and Truck in a very straightforward way:

class Car(Vehicle):

def __init__(self, wheels, miles, make, model, year, sold_on):
"""Return a new Car object."""
self.wheels = wheels
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on
self.base_sale_price = 8000


class Truck(Vehicle):

def __init__(self, wheels, miles, make, model, year, sold_on):
"""Return a new Truck object."""
self.wheels = wheels
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on
self.base_sale_price = 10000


This works, but has a few problems. First, we’re still repeating a lot of code. We’d ultimately like to get rid of all repetition. Second, and more problematically, we’ve introduced the Vehicle class, but should we really allow people to create Vehicle objects (as opposed to Cars or Trucks)? A Vehicle is just a concept, not a real thing, so what does it mean to say the following:

v = Vehicle(4, 0, ‘Honda’, ‘Accord’, 2014, None)
print v.purchase_price()
A Vehicle doesn’t have a base_sale_price, only the individual child classes like Car and Truck do. The issue is that Vehicle should really be an Abstract Base Class. Abstract Base Classes are classes that are only meant to be inherited from; you can’t create instance of an ABC. That means that, if Vehicle is an ABC, the following is illegal:

v = Vehicle(4, 0, ‘Honda’, ‘Accord’, 2014, None)
It makes sense to disallow this, as we never meant for vehicles to be used directly. We just wanted to use it to abstract away some common data and behavior. So how do we make a class an ABC? Simple! The abc module contains a metaclass called ABCMeta (metaclasses are a bit outside the scope of this article). Setting a class’s metaclass to ABCMeta and making one of its methods virtual makes it an ABC. A virtual method is one that the ABC says must exist in child classes, but doesn’t necessarily actually implement. For example, the Vehicle class may be defined as follows:

from abc import ABCMeta, abstractmethod

class Vehicle(object):
“””A vehicle for sale by Jeffco Car Dealership.

Attributes:
wheels: An integer representing the number of wheels the vehicle has.
miles: The integral number of miles driven on the vehicle.
make: The make of the vehicle as a string.
model: The model of the vehicle as a string.
year: The integral year the vehicle was built.
sold_on: The date the vehicle was sold.
"""

__metaclass__ = ABCMeta

base_sale_price = 0

def sale_price(self):
"""Return the sale price for this vehicle as a float amount."""
if self.sold_on is not None:
return 5000.0 * self.wheels

def purchase_price(self):
"""Return the price for which we would pay to purchase the vehicle."""
if self.sold_on is None:
return 0.0  # Not yet sold
return self.base_sale_price - (.10 * self.miles)

@abstractmethod
def vehicle_type():
""""Return a string representing the type of vehicle this is."""
pass


Now, since vehicle_type is an abstractmethod, we can’t directly create an instance of Vehicle. As long as Car and Truck inherit from Vehicle and define vehicle_type, we can instantiate those classes just fine.

Returning to the repetition in our Car and Truck classes, let see if we can’t remove that by hoisting up common functionality to the base class, Vehicle:

from abc import ABCMeta, abstractmethod
class Vehicle(object):
“””A vehicle for sale by Jeffco Car Dealership.

Attributes:
wheels: An integer representing the number of wheels the vehicle has.
miles: The integral number of miles driven on the vehicle.
make: The make of the vehicle as a string.
model: The model of the vehicle as a string.
year: The integral year the vehicle was built.
sold_on: The date the vehicle was sold.
"""

__metaclass__ = ABCMeta

base_sale_price = 0
wheels = 0

def __init__(self, miles, make, model, year, sold_on):
self.miles = miles
self.make = make
self.model = model
self.year = year
self.sold_on = sold_on

def sale_price(self):
"""Return the sale price for this vehicle as a float amount."""
if self.sold_on is not None:
return 5000.0 * self.wheels

def purchase_price(self):
"""Return the price for which we would pay to purchase the vehicle."""
if self.sold_on is None:
return 0.0  # Not yet sold
return self.base_sale_price - (.10 * self.miles)

@abstractmethod
def vehicle_type(self):
""""Return a string representing the type of vehicle this is."""
pass


Now the Car and Truck classes become:

class Car(Vehicle):
“””A car for sale by Jeffco Car Dealership.”””

base_sale_price = 8000
wheels = 4

def vehicle_type(self):
""""Return a string representing the type of vehicle this is."""
return 'car'


class Truck(Vehicle):
“””A truck for sale by Jeffco Car Dealership.”””

base_sale_price = 10000
wheels = 4

def vehicle_type(self):
""""Return a string representing the type of vehicle this is."""
return 'truck'


This fits perfectly with our intuition: as far as our system is concerned, the only difference between a car and truck is the base sale price. Defining a Motorcycle class, then, is similarly simple:

class Motorcycle(Vehicle):
“””A motorcycle for sale by Jeffco Car Dealership.”””

base_sale_price = 4000
wheels = 2

def vehicle_type(self):
""""Return a string representing the type of vehicle this is."""
return 'motorcycle'


Inheritance and the LSP
Even though it seems like we used inheritance to get rid of duplication, what we were really doing was simply providing the proper level of abstraction. And abstraction is the key to understanding inheritance. We’ve seen how one side-effect of using inheritance is that we reduce duplicated code, but what about from the caller’s perspective. How does using inheritance change that code?

Quite a bit, it turns out. Imagine we have two classes, Dog and Person, and we want to write a function that takes either type of object and prints out whether or not the instance in question can speak (a dog can’t, a person can). We might write code like the following:

def can_speak(animal):
if isinstance(animal, Person):
return True
elif isinstance(animal, Dog):
return False
else:
raise RuntimeError(‘Unknown animal!’)
That works when we only have two types of animals, but what if we have twenty, or two hundred? That if…elif chain is going to get quite long.

The key insight here is that can_speak shouldn’t care what type of animal it’s dealing with, the animal class itself should tell us if it can speak. By introducing a common base class, Animal, that defines can_speak, we relieve the function of it’s type-checking burden. Now, as long as it knows it was an Animal that was passed in, determining if it can speak is trivial:

def can_speak(animal):
return animal.can_speak()
This works because Person and Dog (and whatever other classes we crate to derive from Animal) follow the Liskov Substitution Principle. This states that we should be able to use a child class (like Person or Dog) wherever a parent class (Animal) is expected an everything will work fine. This sounds simple, but it is the basis for a powerful concept we’ll discuss in a future article: interfaces.

Summary
Hopefully, you’ve learned a lot about what Python classes are, why they’re useful, and how to use them. The topic of classes and Object-oriented Programming are insanely deep. Indeed, they reach to the core of computer science. This article is not meant to be an exhaustive study of classes, nor should it be your only reference. There are literally thousands of explanations of OOP and classes available online, so if you didn’t find this one suitable, certainly a bit of searching will reveal one better suited to you.

As always, corrections and arguments are welcome in the comments. Just try to keep it civil.

Lastly, it’s not too late to see me speak at the upcoming Wharton Web Conference at UPenn! Check the site for info and tickets.