Advanced Python Means Everything I know about Python

Advanced Python

Python OOPs Concepts:-

Everything Is An Object.
Object-Oriented Programming means and how it ties together with Python classes.
OOP is one of the most powerful tools of Python.
Python is a great programming language that supports OOP.

Major principles of object-oriented programming :-

Object

Class

Method

Inheritance

Polymorphism

Data Abstraction

Encapsulation

Object(Instances):- Object is a unique instance of a data structure that's defined by its class. An object can be anything like - a student while designing a school's record registry, or a pen in stationary's item management program, or a car in manufacture's car database program.
it is a real entity.

Three important characteristics by which it is identified:-

Identity:- Identity refers to some piece of information that can be used to identify the object of a class. It can be the name of the student, the company name of the car, etc.

Properties:- The attributes of that object are called properties. Like age, gender, DOB for a student; or type of engine, number of gears for a car.

Behavior:- Behavior of any object is equivalent to the functions that it can perform. In OOP it is possible to assign some functions to objects of a class. Taking the example forward, like a student can read/write, the car can accelerate and so on.

Class:- A class is a blueprint for any functional entity which defines its properties and its functions.

it is a collection of objects.

define a class in Python, you can use the "class" keyword, followed by the class name and a colon. Inside the class, an " init()" method has to be defined with "def". This is the initializer that you can later use to instantiate objects

Note:-The self in Python is equivalent to the this pointer in C++ and the this reference in Java and C#.

Example:-

class pyarun:

pass # An empty block

p = pyarun()

print(p)

Method:- A special kind of function that is defined in a class definition.

it is an object behavior.

Inheritance:- Inheritance is the process by which one class takes on the attributes and methods of another.
it is a process of deriving a new class from already existing class.
reuse code of existing object
inheritance is process of object re-usability.

Example:-

Polymorphism (Many forms):-When a message can processed in different ways is call polymorphism .
Polymorphism means one name many forms, one function behavior different forms.

Data Abstraction:- Hides the implementation details and only provides the functionality to the user.

you can achieve abstraction using abstract class and interface.

abstraction means, showcasing only the required things to the outside world while hiding the details.
abstraction is the process of hiding the working style of an object and showing the information of an object in understandable manner.

Encapsulation:- Binding the data and code together as a single unit.

securing data by hiding the implementation details to user.
it is process of binding the data members and member functions into a single unit.

This binding of the properties to functions is called Encapsulation.

Data and functionality to bound together with in a class.

controlling the access of the data.

both abstraction and encapsulation word hand in hand because abstraction

say what details to be mode visible and encapsulation provides the level of access right to that visible details.
encapsulation is like your bag in which you can keep your pen, book etc. it means this is the property of encapsulation members and functions.

Python libraries for Data Science:-

1. NumPy (Numeric Computation):- NumPy stands for Numerical Python, library consisting of multidimensional array objects and a collection of routines for processing those arrays.

2. SciPy (Scientific Computation):- SciPy is another Python library for researchers, developers and data scientists. It provides statistics, optimizations, integration and linear algebra packages for computation. It is based on NumPy concept to deal with complex mathematical problems.

3. PANDAS (Data Analysis Library):- PANDAS referred as Python Data Analysis Library. It contains DataFrame as its main data structure and DataFrame you can store and manage data from tables by performing manipulation over rows and columns. They are in multiple formats such as CSV, SQL, and HDFS or excel etc. Pandas contain many built-in methods for grouping, filtering, and combining data, as well as the time-series functionality.

Series: - one-dimensional

Data Frames: - two-dimensional

4. Scikit-Learn (Machine Learning):-Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python.

5. Matplotlib: - 2D plotting library of Python is very famous among data scientists for designing varieties of figures in multiple formats which is compatible across their respected platforms. One can easily use it in their Python code, IPython shells or Jupiter notebook, application servers. With Matplotlib, you can make histograms, plots, bar charts, scatter plots etc

6. Tensorflow (Deep Learning):- open source library was designed by Google to compute data low graphs with the empowered machine learning algorithms. It was designed to fulfill high demand for the training neural networks work.

7. Seaborn: - Seaborn was designed to visualize the complex statistical models. It has the potential to deliver accurate graphs such as heat maps. Seaborn was created on the concept of Matplotlib and somehow it is highly dependent on that. Seaborn is essentially a higher-level API based on the Matplotlib library. It contains more suitable default settings for processing charts. Also, there is a rich gallery of visualizations including some complex types like time series, joint plots, and violin diagrams.

8. NLTK: - NLTK libraries stand for Natural Language Toolkit. As per its name, this library is very helpful for accomplishing Natural language processing tasks. Initially, it was developed to promote the teaching models and other NLP enabled research such as the cognitive theory of artificial intelligence and linguistic models etc.

Pandas tips and tricks:-

For selecting data use loc and iloc

loc() is label-based, which means that you have to specify rows and columns based on their row and column labels.

iloc () is integer position-based, so you have to specify rows and columns by their integer position values (0-based integer position).