Python 3 - Hight Level Programming Lang.

 Python

           Python is an interpreted high-level general-purpose programming language. By using python we can build Web Applications as well as Computer Applications, There are many technical uses of Python ie.

1. AI and machine learning. Because Python is such a stable, flexible, and simple programming language, it's perfect for various machine learning (ML) and artificial intelligence (AI) projects.
2. Data analytics.
3. Data visualization.
4. Programming applications.
5. Web development.
6. Game development.
7. Language development.
8. Finance.

By the data of statisticstimes.com Pythonis now become the world most popular programing language 

History:- 

                    Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989.

Old Python Logo Till 2006

1. Python 1.0 released   in January 1994
2. Python 1.2. released  In 1995 (Somany Minor Update such as 1.4,1.6,1.6.1 etc)    
3. Python 2.0, released October 2000
4. Python 2.1 released in 2000
5. Python 2.2  released in December 2001
6. Python 2.5 was released in September 2006
7. Python 2.6 was released to coincide with Python 3.0 In 2007
8. A final release, 2.7.18, occurred on April 20, 2020

         In November 2014, it was announced that Python 2.7 would be supported until 2020, but users were encouraged to move to Python 3 as soon as possible. and this is the time were Python popularity decreased because this is a tough task to move between programming languages for the smaller companies. but after seeing the feature of python 3 Python come back to the race.

        Python 3.0 (also called "Python 3000" or "Py3K") was released on December 3, 2008. It was designed to rectify fundamental design flaws in the language—the changes required could not be implemented while retaining full backward compatibility with the 2.x series, which necessitated a new major version number. The guiding principle of Python 3 was: "reduce feature duplication by removing old ways of doing things". and we can not run a python 2.x code python 3.x without modification. Some of the major changes included for Python 3.0 were:

• Changing print so that it is a built-in function, not a statement. This made it easier to change a module to use a different print function, as well as making the syntax more regular. In Python 2.6 and 2.7 print() is available as a builtin but is masked by the print statement syntax, which can be disabled by entering from __future__ import print_function at the top of the file
• Removal of the Python 2 input function, and the renaming of the raw_input function to input. Python 3's input function behaves like Python 2's raw_input function, in that the input is always returned as a string rather than being evaluated as an expression
• Moving reduce (but not map or filter) out of the built-in namespace and into functools (the rationale being code that uses reduce is less readable than code that uses a for loop and accumulator variable)
• Adding support for optional function annotations that can be used for informal type declarations or other purposes.
• Unifying the str/unicode types, representing text, and introducing a separate immutable bytes type; and a mostly corresponding mutable bytearray type, both of which represent arrays of bytes
• Removing backward-compatibility features, including old-style classes, string exceptions, and implicit relative imports
• A change in integer division functionality: in Python 2, 5 / 2 is 2; in Python 3, 5 / 2 is 2.5. (In both Python 2 (2.2 onwards) and Python 3, a separate operator exists to provide the old behavior: 5 // 2 is 2)

Subsequent releases in the Python 3.x series have included additional, substantial new features; all ongoing development of the language is done in the 3.x series.

Why Python:-

                            Python is a preferred high-level, server-side programming language for websites and mobile apps. For both, new and old developers, Python has managed to stay a language of choice with ease. Due to its readability and dense syntax, developers can express a concept with more ease than they can, using other languages. It powers the web apps for Instagram, Pinterest, and Rdio through its associated web framework, Django, and is also used by Google, Yahoo and NASA.

         Python is ranked third according to the RedMonk programming language rankings. It has moved two points up with reference to the rankings released in the year 2021.

RedMonk Ranking 2021

                        One of the biggest benefits of learning Python for big data certification is the added efficiency of using one programming language across different applications. Python can be used across functions, making a data professional adept at handling any data-related query. As a Big Data architect, it is important that you are versatile. The platforms designed should be compatible with multiple platforms like Python, Hadoop, Storm, NoSQL and Map Reduce. Big Data architects cannot work in isolation. Python is slowly foraying into Big Data in a very significant way. Experts on Dice state that Python for Big Data certification is definitely the combination, which is being sought for. Python and big data features are among the skills required by Fortune 500 companies. The gaming industry is one such example. A software engineer in the gaming industry is warranted to know a programming language, along with the data screening expertise. Across industries, it is now becoming imperative that a Big Data professional is a programming expert at the same time. There is also an increase in the interest of companies to crunch figures to assess consumer behavior and predict purchase patterns. Not just predictive analytics, but Big Data is slowly foraying into various avenues, be it communication, or performance metrics.

                    As the hiring for Big Data Professional increases, so is the demand for Python Professionals. Organizations are looking for a large talent pool that can understand the simplest of languages, namely Python in order to tackle their Big Data challenges

Currently, the job trends are at an all-time high: there is an upward trend noticed in the job postings for the following professionals (Source: www.indeed.com and LinkedIn).

• NoSQL (54%)
• Big Data (46%)
• Hadoop (43%)
• Python (16%)

The blend of Python with big data is a matter of versatility enabling flexibility to work across platforms. Python’s agility and user experience is charismatic. So Python and big data certainly become an irresistible combination.

                Python is easy for analysts to learn and use, but powerful enough to tackle even the most difficult problems in virtually any domain. It integrates well with existing IT infrastructure and is independent of the platform. With the advent of so many modern languages, Python-based solutions are legendary in terms of performance. The TIOBE index states that Python is one of the most preferred and popular languages in the world, featuring above Perl, Ruby, and JavaScript by a wide margin.

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Blog By Patel Aasif Khan Official