Why Choose Python as a Career?

Python is an easy-to-use programming language that encourages you to get surefire accomplishment with the projects you make. Python is one of the top three programming languages in the world and is poised to become the most popular, according to ZDNet. In fact, according to the PYPL Index, Python is the most popular programming language worldwide, so if you want to work in a different country, you have a good chance of landing a job in, say, Switzerland or Australia. Where would you like to work? Adding Python to your skill set could be your ticket to anywhere.

 

Python Top modules

·        Data Science

·        Machine Learning

·        Artificial Intelligence

·        Python with Django

·        Rest API in Python

·        Python Advance

·        Business Analyst with Python

·        Data Analyst with Python

·        Data Engineer

 

Thus, after completing the Python Course, you’ll have Ample Job Opportunities and a Great package if you learn Python. If you want to learn a Complete Python Course, High Technologies Solutions is one of the Best Python Training Institute in Delhi.

 

Earning Potential

Python is a highly demandable computer language, and ample job opportunities according to INDEED. If you have one year of experience as a data scientist, your starting salary is ₹6,98,413. An entry-level data scientist can earn around ₹500,000 per annum.

Support from Renowned Corporate Sponsors

Programming languages grow faster when a corporate sponsor backs them. For example, PHP is backed by Facebook, Java by Oracle and Sun, and Visual Basic & C# by Microsoft. Python Programming language is heavily backed by Facebook, Amazon Web Services, and especially Google.

Google adopted the Python language way back in 2006 and has used it for many applications and platforms since then. Lots of Institutional effort and money have been devoted to the training and success of the Python language by Google. They have even created a dedicated portal only for Python. The list of support tools and documentation keeps on growing for python language in the developers' world.

 

Use of Libraries in Python Program

As we write large-size programs in Python, we want to maintain the code’s modularity. For the easy maintenance of the code, we split the code into different parts and we can use that code later whenever we need it. In Python, modules play that part. Instead of using the same code in different programs and making the code complex, we define mostly used functions in modules and we can just simply import them into a program wherever there is a requirement. We don't need to write that code but still, we can use its functionality by importing its module. Multiple interrelated modules are stored in a library. And whenever we need to use a module, we import it from its library. In Python, it’s a very simple job to do due to its easy syntax. We just need to use import.

 

TYPE OF PYTHON: -

There are two major Python versions- Python 2 and Python 3.

 • On 16 October 2000, Python 2.0 was released with many new features.

 • On 3rd December 2008, Python 3.0 was released with more testing and includes new features.

 

Comparison Parameter	Python 2	Python 3
Year of Release	Python 2 was released in the year 2000.	Python 3 was released in the year 2008.
“Print” Keyword	In Python 2, print is considered to be a statement and not a function.	In Python 3, print is considered to be a function and not a statement.
Storage of Strings	In Python 2, strings are stored as ASCII by default.	In Python 3, strings are stored as UNICODE by default.
Division of Integers	On the division of two integers, we get an integral value in Python 2. For instance, 7/2 yields 3 in Python 2.	On the division of two integers, we get a floating-point value in Python 3. For instance, 7/2 yields 3.5 in Python 3.
Exceptions	In Python 2, exceptions are enclosed in notations.	In Python 3, exceptions are enclosed in parentheses.
Variable leakage	The values of global variables do change in Python 2 if they are used inside a for-loop.	The value of variables never changes in Python 3.
Iteration	In Python 2, the xrange() function has been defined for iterations.	In Python 3, the new Range() function was introduced to perform iterations.
Ease of Syntax	Python 2 has a more complicated syntax than Python 3.	Python 3 has an easier syntax compared to Python 2.
Libraries	A lot of libraries of Python 2 are not forward-compatible.	A lot of libraries are created in Python 3 to be strictly used with Python 3.
Usage in today’s times	Python 2 is not longer in use since 2020.	Python 3 is more popular than Python 2 and is still in use in today’s times.
Backward compatibility	Python 2 codes can be ported to Python 3 with a lot of effort.	Python 3 is not backward compatible with Python 2.
Application	Python 2 was mostly used to become a DevOps Engineer. It is no longer in use after 2020.	Python 3 is used in a lot of fields like Software Engineering, Data Science, etc.

 

HISTORY OF PYTHON: -

It was initially designed by Guido van Rossum in 1991 in the Netherlands and developed by Python Software Foundation. Python was named after the BBC TV show Monty Python’s Flying Circus. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

 

 

PYTHON LIBRARIES

 

1.   TensorFlow: It is an open-source library used for high-level computations. It is also used in machine learning and deep learning algorithms. It contains a large number of tensor operations.

 

2.   Matplotlib: This library is responsible for plotting numerical data. And that’s why it is used in data analysis. It is also an open-source library and plots high-defined figures like pie charts, histograms, scatterplots, graphs, etc.

 

3.   Pandas: Pandas are an important library for data scientists. It is an open-source machine learning library that provides flexible high-level data structures and a variety of analysis tools. It eases data analysis, data manipulation, and cleaning of data.

 

4.   NumPy: The name "NumPy" stands for "Numerical Python". It is a commonly used library. It is a popular machine-learning library that supports large matrices and multi-dimensional data. It consists of in-built mathematical functions for easy computations.

 

5.   SciPy: The name "SciPy" stands for "Scientific Python". It is an open-source library used for high-level scientific computations. This library is built over an extension of NumPy. It works with NumPy to handle complex computations. While NumPy allows the sorting and indexing of array data, the numerical data code is stored in SciPy. It is also widely used by application developers and engineers.

 

6.   Scikit-learn: It is a famous Python library to work with complex data. Scikit-learn is an open-source library that supports machine learning. It supports various supervised and unsupervised algorithms like linear regression, classification, clustering, etc. This library works in association with NumPy and SciPy.

 

7.   PyTorch: PyTorch is the largest machine learning library that optimizes tensor computations. It has rich APIs to perform tensor computations with strong GPU acceleration. It also helps to solve application issues related to neural networks.

There are many more libraries in Python. We can use a suitable library for our purposes. Hence, Python libraries play a very crucial role and are very helpful to developers.