How python is special in Today's world

Python supports Artificial Intelligence, Machine Learning, Data Science, Web Development, Scientific computing, GUI, etc.

Python is a general-purpose language, object-oriented programming language, Scripting language, high-level programming language, dynamic typing language, Interpreted language.

Every revision of Python enjoys performance improvements over the previous version. python recently python 3.9 released with new features. In Python 3.9, several Python built-in functions like range, tuple, set, frozenset, list, dictionary use the vector call to speed up execution. The next big performance enhancer is the more efficient parsing of Python source code. The new parser for the CPython runtime wasn’t designed to address performance issues, but rather to deal with internal inconsistencies in the original parser. However, an important fringe benefit is faster parsing, especially for large volumes of code.

New features in 3.9

Dictionary Merge & Update Operators

Merge (|) and update (|=) operators have been added to the built-in dict class. Those complement the existing dict.update and {**d1, **d2} methods of merging dictionaries.

Example a={1:’a’ ,2:’b’,3:’c’} b={3:’c’,4:’d’} a|b

Output:

{1:’a’,2:’b’,3:’c’,4:’d’}

Type Hinting Generics in Standard Collections

Example

def greet_all(names: list[str]) -> None:

for name in names:

print(“Hello”, name)

NEW MODULES

Zoneinfo

graphlib

Python supports multiple programming paradigms

Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a “batteries included” language due to its comprehensive standard library. Python also joins with other languages like C, Java also adds some features. Examples JPython, CPython.

Python object inferring

It gives better memory management in python. If two variables assigned to the same value then in the same memory location both will point not in different memory locations. This is called as an object inferring. Example.

DECORATOR

Decorators

Without changing the function definition we can change the functionality using decorators.

Example

import functools

def swap(func):

def inner(a,b):

if(a<b):

a,b=b,a

return func(a,b)

return inner

@swap

def sub(a,b):

return(a-b)

print(sub(2,5))

Run it

Output:

3