What is Python Programming Language?

 What is Python?

Python is a general-purpose, cross-platform, and object-oriented scripting language.

Python is a high-level, interpreted language where the program’s interpretation of

machine language happens at runtime.

Python was created in the early 1980s by Guido van Rossum at the National Research

Institute for Mathematics and Computer Science inside the Netherlands. Guido

named it after ‘Monty Python Flying Circus,’ a British comedy series, the scripts he

became reading when he began enforcing Python.

Python was released to the public in 1991 and was introduced as a successor of the

ABC (All Basic Code) language.

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Features of Python

The features of Python are as follows:

• Open-source

A python is open-source software. The source code of Python is available to every

the person at no cost.

• Object-Oriented

Python is an object-oriented programming language that includes functions,

including classes, inheritance, objects, and overloading.

• Ease of learning

Python has easy-to-learn syntax (more English like syntax) and has an extensive

a standard library that makes it a beginner’s language.

• Interactive

Python is interactive mode makes it simple to test short snippets of the program.

There is also a bundled development environment known as IDLE.

• Extendable

Python is frequently called a ‘glue’ language. It is easily extended through including

new modules implemented in a compiled language consisting of C or C++.

• Standard Libraries

Python comes with a vast standard library that provides routine programming

functions linked to internet servers, searching text with regular expressions, reading,

and changing files.

Uses of Python

Python is a commonly-used language for scripting within the following cases:

 System utilities (system admin tools, command-line programs).

 Web development

 Graphical User Interfaces (Tkinter, gtk, Qt).

 Internet scripting

 Embedded scripting

 Database access and programming

 Game programming

 Rapid prototyping and development

 Distributed programming

Let us take a look at the role of python in the following domains:

Python on the Desktop: We can write desktop applications in Python, using

frameworks that include WxPython, Tkinter, or PyGTK. Many desktop applications

are as yet written in compiled languages, including C, C++, or C#. The frameworks for

these languages influence to have more refined development tools. The subsequent

applications are frequently simpler to distribute, as they do not need the client to

have Python introduced.

Python has proper graphical development tools, including Wing IDE and the Eclipse

PyDev extensions. But, most Python engineers work "Unix style" with standalone text

editors and terminals. Platforms like Java or .Net, environments including Microsoft's

Visual Studio, will consistently provide tighter integration with the programming

language.

Python for the Web: Several successful quick Web application structures are

accessible for Python, each with its inclination. The most famous framework include

Django, Pylons, TurboGears, CherryPy, Zope, and Grok (because of Zope).

The major drawback of utilizing Python in an undertaking setting is that Python

software engineers can be harder to discover than Java developers. Python is simple

to pick up for a professional programmer, but Python cannot coordinate the plethora

of books, education courses, and certifications in the Java world.

Besides, the power and expressivity that Python provides imply that it can need

more skilled developers. Java or C# are more restrictive by design, imposing

programmers to comply with stricter guidelines around type safety and interface

consistency.

Python is a popular scientific language and a developing star for machine learning. It

is often compared with R. The matrix handling in NumPy can challenge MATLAB, and

specialized tools like IPython are appealing and a phase into the future of

reproducibility.

A SciPy stack for machine learning and data analysis can be utilized for one-off tasks,

and frameworks like sci-kit-learn are sophisticated adequate to be used in

production systems.

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