We have seen the basic functions of Python for Data Science; so, now will look at some of the advance features of Python.
We can devide the article into five different sections. So, now we will look at the sections in detail.
Similar to other object-orient programming languages Python too supports classes. So, let’s look at some important points about Python classes.
- Keyword class is there to create classes.
- Variables belonging to a class are known as attributes.
- Attributes are public in nature, and we can access them using dot (.)
Let’s look at an example to make things clear.
It is a bunch of codes that we can use to perform a specific function in the Python code. So, let us look at some important points of methods.
- Functions belonging to a class are known as methods.
- All functions require ‘self’ parameter.
- We can write new method using ‘def’
The way in which a particular class inherits features from its base class is known as inheritance. Base class is also known as ‘Superclass’. So, when a class inherits from the Superclass, it goes with the name ‘Subclass’.
As we can see from the figure, the derive class is able to inherit features both from the base class and itself.
We can either iterate objects upon are known as iterators. Below are some important points.
- The _iter_() method is there to return an iterator object of class.
- The _next_() method is there for obtaining the next object.
- In case StopIteration Exception is raise the for loop stops.
- It is there for creating iterators.
- It uses a function instead of a separate class.
- Responsible for generation of background code for iter() and next() methods.
- Yield statement is there, which saves the state of the generator, and it generates a resume point for next() function.
If you want to learn some more important functions in python for data science then you can check this and this as well. These blogs will help you in understandinig the data industry better. They’ll provide you the approach which you can use to build your career path.