An objected-oriented approach is most useful when your code involves complex interactions of many objects. In real production code, classes can have dozens of attributes & methods with complicated logic, but the underlying structure is the same as with the most simple class.
Classes are like a blueprint for objects outlining possible behaviors & states that every object of a certain type could have. For example, if you say, “Every customer will have a phone number & an e-mail, & will be able to place and cancel orders”, you just defined what a class really is. This way, you can talk about customers in a unified way. Then a specific customer object is just a realization of this class with a particular state value.
Finding Python Classes
In Python, everything is an object. Numbers, strings, DataFrames, even functions are objects. In particular, everything you deal with in Python has a class, a blueprint associated with it under the hood. The existence of these unified interfaces is why you can use any DataFrame in the same way.
You can call
type() on any Python object to find out its class. For example, the class of a
numpy array is actually called
ndarray (for n-dimensional array).
Classes incorporate information about state & behavior. State information in Python is contained in attributes & behavior information in methods.
Attributes & Methods
numpy array: you have already been using some of its methods and attributes! For example, every
numpy array has an attribute "shape" that you can access by specifying the array's name followed by a dot and shape.
It also has methods like
reshape which are also accessible via dot.
Creating Your First Class
In this example, you will create an empty class
Employee. Then you will create an object
emp of the class
Employee by calling