Big Data Data Science python Tutorials

Python for Spark Tutorial – Classes and Inheritence in Python

In the last tutorial, we’ve learned about Methods in Python. To further increase the re-usability of your code, it is also possible to use Classes in Python. With Classes, you can encapsulate your methods into better re-usability.


With a class, we can add several methods together. Imagine you write an app for selling different kind of vehicles – either a car, a bike or a motorcycle. There are several items in your vehicle, that are always the same – e.g. all would have a wheel. With a class, you can sum this up and create re-usability on each of them. Basically, a class in Python is also very similar to classes in C-like languages. However, there are several differences. The most striking one is that we don’t have any public-protected-private modifiers. This applies to the class itself and also to its methods and variables. Basically, a class is defined with the “class” keyword. The syntax of it looks like this:



def __init__(self):


As you can see, we have several possibilities with classes. First of all, defining a class is easy. You would then specify some variables within the class. The constructor – one thing you often use in C-like languages – is written wit “def__init__(“. You will then have the possibility to specify variables in the brackets. One mandatory variable in there is the “self”. This refers to the class itself and makes all objects in the class accessible. You will also need it in all further method definitions.

Let’s go back to the previous sample with the vehicles. In order to achieve that, we create a class “Vehicle” and add some variables and methods to it. The Class should look like the following. Create a new Notebook and name this notebook “vehicle”.

class Vehicle:
    vname = "Not Defined"
    curspeed = 0
    def name(self):
    def accelerate(self, speed):
        self.curspeed += speed

Now comes the tricky part: since we are working in a notebook application and not in a comprehensive IDE, we first need to download the file as “Python” and then re-upload it. If you would just rename the notebook, it would result in a json document that can’t be read. We re-upload the file and name it “”. Make sure that it is in the same folder as your other notebook. We then switch back to your original file and use the just created vehicle:

from vehicle import Vehicle

v = Vehicle()


Not Defined

Note that the output is as expected: we explicitly named the vehicle “Not Defined” – so it isn’t an error. I guess you can imagine what I want to do with the vehicle – I want to use inheritance in the next sample 🙂


Often, it is necessary to inherit from a class. This is helpful, when a sub-class is a specialisation of another class and thus has some common functions or behaviour. It is therefore easier to extend the behaviour without re-writing the code. To enable inheritance, we add brackets to the new class with the name of the parent-class. Also, it is important to import the parent class. Everything else stays the same.

In the following sample, we create a new class and name it “car”. Basically, we extend the behaviour of our vehicle and add a “start” method to it. This will print a statement that the car has started with a specific number of HP. Create a new file in Jupyter and name it “car”:

from vehicle import Vehicle

class Car(Vehicle):
    def __init__(self, brand, hp):
        self.hp = hp
        vname = brand
    def start(self):
        print("Engine of " + self.vname + " started @ " + str(self.hp))

Do the same steps as in the “vehicle” file and go back to your initial Notebook. We instantiate a new Car and give it a name and some HP:

from car import Car

c = Car("BMW", 150)




The function “hp” belongs to the “car”-class, whereas the function “name” belongs to the “vehicle” class. Let’s now use the “start” function:



Engine of Mercedes started @ 150

Also here we use the method of our “car” Class.

Since now the code got more complex, it is necessary to think about how to work with Errors – in our next tutorial, we will look at error handling.

I lead a team of Senior Experts in Data & Data Science as Head of Data & Analytics and AI at A1 Telekom Austria Group. I also teach this topic at various universities and frequently speak at various Conferences. In 2010 I wrote a book about Cloud Computing, which is often used at German & Austrian Universities. In my home country (Austria) I am part of several organisations on Big Data & Data Science.

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