Top 5 most useful Python libraries
Tags: python, beginners, django, programming
Published: 2022-08-13 15:29:35

> The joy of coding Python should be in seeing short, concise, readable classes that express a lot of action in a small amount of clear code — not in reams of trivial code that bores the reader to death.
> 
> - Guido van Rossum

The great majority of corporations officially use Python as their primary programming language, and it is a very well-liked computer language. Similar to this, many developers—both inexperienced and seasoned—can witness to Python's efficacy and efficiency. Python is more popular than the majority of other programming languages due to a number of factors.

With approximately 270 thousand packages now accessible, according to Python's Package Index, the language ranks fourth among those with the most widely available packages, after Node.js, Java, and PHP. What impact does this have on you as a Python developer, then?

As a result, there is a significant probability that everything you're attempting to construct already exists as a package that can facilitate your development. We've included our best recommendations for Python libraries that programmers should utilize in their work below. Read on!

## 1. QT library
QT is widgeting toolkit for creating graphical user interfaces as well as cross-platform applications that run on various software and hardware platforms like android, Linux, windows, or any IoT applications. 


![QT Application](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/m264dn3goz9rbcdk2t6t.png)

It is very powerful library to create GUI based applications. 

Get it now: https://www.qt.io/qt-for-python 

## 2. Django
High-level Python web framework Django promotes quick development and streamlined, practical design. It was created by professional programmers and handles a lot of the effort associated with web development, freeing you up to concentrate on building your app without having to invent the wheel. It is open source and free.

![Django Logo](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/crz1t8pg11t8y6py3af2.png) 

Django is the most popular choice among developers for creating highly scalable web applications with a user base that is continually expanding (such as content-based or news websites). The framework is easy to understand and use, dependable and quick, adaptable and expandable. Django is a sizable, devoted contributing community, and this framework enables you to complete your task with less code. It enables developers to concentrate on creating new components rather than spending time on built-in components.

Want to know more?? : https://www.djangoproject.com/

## 3. NumPy
Numerous mathematical operations may be carried out on arrays with NumPy. It provides a vast library of high-level mathematical functions that work on these arrays and matrices, as well as strong data structures that ensure fast calculations with arrays and matrices. It is the most used python library in data science and machine learning projects. 

![Numpy Logo](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6ohnnicwp58yb98z081p.png)

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called the locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also, it is optimized to work with the latest CPU architectures.

More documentation about NumPy: https://numpy.org/

## 4. Beautiful Soup
Beautiful soup is a python library to parse HTML and XML pages that are deployed on the internet or in easy terms it is being used for web scraping. The Internet has a humongous amount of rich textual data. To harvest these data for useful analysis and create analytics of interest. Beautiful soup allows users to interact with structured HTML or XML data being parsed from the internet the similar way user interact with websites. 
 
For more documentation: https://www.crummy.com/software/BeautifulSoup/bs4/doc/

## 5. Numba
A Python just-in-time compiler called Numba performs best with code that employs loops, NumPy arrays, and functions. The most popular approach to use Numba is to apply one of its many decorators to your functions to tell it to compile them. All or a portion of your code can thus run at native machine code speed when a call to a Numba-decorated function is made since it is "just-in-time" compiled to machine code for execution.

![Numba Logo](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mikg5wqcvvcu7nt18tu6.png)

Numba offers a range of options for parallelizing your code for CPUs and GPUs, often with only minor code changes. 

For more information: http://numba.pydata.org/

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