BASIC OF PYTHON

Basic of Python

Whenever anybody listens to the word python it seems to be a very difficult programming language but as you start to learn it, it is found much interesting and easy to learn programming language.

Introduction of Python

Python was first introduced by Guido Van Rossum in 1991. Python is a simple, general-purpose, high level, object-oriented, multi-paradigm, structured programming language. It is an interpreted language also. As a result, run time errors are usually encountered. It is often described as a "batteries included" language due to its comprehensive standard library.

Why Python?

Python is the most popular and high-rated programming language since it is easy to code and easy to understand.

It is a very less reliable language for developers.

It takes less time compared to other languages such as C#, JAVA.

There is no need to declare variable type, So it is a less time-consuming language to implement a Python application.

A large number of communities and books are available for Python developers.

How does Python Works?

The way Python works are as follows:

Step 1: A Python virtual machine is created where libraries are installed.

Step 2: The Python code is written in .py files.

Step 3: CPython compiles the Python code to bytecode.

Step 4: When you want to execute the bytecode then the code will be interpreted at runtime. The code will then be translated from the bytecode into the machine code.

In most of the programming languages, whenever a new version releases, it supports the features and syntax of the existing version of the language, therefore, it is easier for the projects to switch into the newer version. However, in the case of Python, the two versions Python 2 and python 3 are much different from each other.

Key Difference between Python 2 and Python 3

Basis of comparison	Python 3	Python 2
Release date	2008	2000
Function print	Print(“hello”)	Print “hello”
Syntax	The syntax is simpler and easily understandable.	The syntax of Python 2 was comparatively difficult to understand.
Iteration	The new range( ) function introduced to perform iterations.	In Python 2, the xrange( ) is used for iterations.
Exceptions	It should be enclosed in parenthesis.	It should be enclosed in the notations.

Python Features

1) Easy to Learn and Use

Python provides many useful features that make it popular and valuable from other programming languages. It supports object-oriented programming. Its syntax is straightforward and much the same as the semicolon or curly-bracket, the indentation defines the code block. It is the recommended programming language for beginners.

2) Expressive language

Python can perform complex tasks using a few lines of code. A simple example, the hello world program you simply type print("Hello World"). It will take only one line to execute, while Java or C takes multiple lines.

3) Interpreted Language

Python is an interpreted language; it means the Python program is executed one line at a time. The advantage of being interpreted language, it makes debugging easy and portable.

4) Cross-platform Language

Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh, etc. So, we can say that Python is a portable language.

5) Free and Open Source

python is freely available for everyone. it is freely available on its official website www.python.org. It has a large community across the world that is dedicatedly working towards making new python modules and functions.

6) Object-Oriented Language

Python supports object-oriented language and concepts of classes and objects come into existence. It supports inheritance, polymorphism, and encapsulation, etc. The object-oriented procedure helps to programmer to write reusable code and develop applications in less code.

7) Extensible

It implies that other languages such as C/C++ can be used further in our python code. It converts the program into byte code, and any platform can use that byte.

8) Large Standard Library

It provides a vast range of libraries for various fields such as machine learning, web developer, and also for scripting. There are various machine learning libraries, such as tensor flow, pandas, bumpy, Keras, and PyTorch, etc. Django, flask, pyramids are the popular framework for Python web development.

9) GUI Programming Support

Graphical User Interface is used for the developing Desktop application. PyQT5, Tkinter, Kivy are the libraries that are used for developing the web application.

10) Integrated

It can be easily integrated with languages like C, C++, and JAVA, etc. Python runs code line by line like C, C++ Java. It makes it easy to debug the code.

11) Embeddable

The code of the other programming language can use in the python source code. we can use python source code in another programming language as well. It can embed other languages into our code.

12) Dynamic Memory Allocation

In the python, we don't need to specify the data- type of the variable. When we assign some value to the variable, it automatically allocates the memory to the variable at run time. Suppose we are assigned integer value 15 to x, then we don't need to write int x =15. Just writes x=15.

Python Libraries

Python is an ocean of libraries that serve various purposes. You must have sound knowledge of the best ones. To help you in this, here are Top 10 python libraries for the machine learning which are:

TensorFlow:

                TensorFlow works like a computational library for writing new algorithms that involve a large number of tensor operations since neural networks can be easily expressed as computational graphs they can be implemented using TensorFlow as a series of operations on Tensors. Plus, tensors ae N-dimensional matrices which represent your data.

Features of TensorFlow

Responsive Construct

Flexible.

Easily Trainable

Parallel Neural Network Training

Large Community

Opensource 

Scikit-Learn:

It is a python library that is associated with NumPy and SciPy. It is considered as one of the best libraries for working with complex data.

There are a lot of changes being made in this library. One modification is the cross-validation feature, providing the ability to use more than one metric. A lot of training methods like logistics regression and nearest neighbors have received some little improvements.

Features of Scikit-Learn

Cross-validation

Unsupervised learning algorithms

Feature extraction

Numpy:

Numpy is considered as one of the most popular machine learning in Python.

TensorFlow and other libraries use Numpy internally for performing multiple operations on Tensors. Array interface is the best and the most important feature of NumPy.

Features of Numpy

Interactive

Mathematics

Intuitive

A lot of Interaction

Keras:

Keras is considered as one of the coolest machine learning libraries in Python. It provides an easier mechanism to express neural networks. Keras also provides some of the best utilities for compiling models, processing data-sets, visualization of graphs, and much more.

Features of Keras

It runs smoothly on both CPU and GPU.

Keras supports almost all the models of a neural network - fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.

Keras, being modular in nature, is incredibly expressive, flexible, and apt for innovative research.

Keras is a completely Python-based framework, which makes it easy to debug and explore.

PyTorch:

PyTorch is the largest machine learning library that allows developers to perform tensor computations wan ith acceleration of GPU, creates dynamic computational graphs, and calculate gradients automatically. Other than this, PyTorch offers rich APIs for solving application issues related to neural networks.

features of PyTorch

Hybrid Front-End

Distributes Training

Python First

Libraries And Tools

Pandas:

Pandas is a machine learning library in Python that provides data structures of high-level and a wide variety of tools for analysis. One of the great features of this library is the ability to translate complex operations with data using one or two commands.

Features of Pandas

Pandas make sure that the entire process of manipulating data will be easier. Support for operations such as Re-indexing, Iteration, Sorting, Aggregations, Concatenations, and Visualizations are among the feature highlights of Pandas.

Summary

This article outlined some of the important topics of Python in a short, relevant, and focused manner that is required to be understood. I genuinely hope that it has helped most of you to have a better understanding of Python.

Thank You.

~Jinal Menpara