Top 10 MOST USED PYTHON LIBRARIES

 

Python is high level and most popular programming language currently in the market, choosing python as a language to learn will give you vast number of opportunities in the field.it is popular because of its libraries, it consists more than 250 libraries for different purposes.as it is a open source language anyone can contribute to the language.

What is a Library?

A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python. 

What is a Python Library?

Python library is a collection of modules that contain functions and classes that can be used by other programs to perform various tasks.

I am going to give a short introduction to the top ten most used python libraries by the developers in this article.

1.TENSOR FLOW:

The most popular deep learning framework, Tensorflow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for Python in machine learning and deep learning. Tensorflow was developed by the researchers at the Google Brain team within Google AI organization, and today it is being used by researchers for machine learning algorithms Tensorflow is  undoubtedly the most popular python Package used by the python developers in machine learning and deep learning. If you are building a machine learning applicati0on then TENSORFLOW is the package you ultimately go for.

2.NUMPY:

Numpy stands for Numerical python, if some one starts their career in data analytics they might have heard the word numpy. Numpy makes mathematics in python to be easy. With the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning. It is particularly useful for linear algebra, Fourier transform, and random number capabilities. High-end libraries like tensorflow uses numpy internally for manipulation of Tensors.

3. PANDAS:

Pandas is a open source python library ,It is mainly used for analysing the data.it is mainly used for handling and managing the data. If you are working in the data science and working with CSV or EXEL Files then PANDAS is mandatory. Pandas have so many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality. Pandas make sure that the entire process of manipulating data will be easier. Support for operations such as Re-indexing, Iteration, Sorting, Aggregations, Concatenations and Visualizations are among the feature highlights of Pandas.

 

4.PYTORCH:

It is a alternative for tensor flow Introduced by Facebook in 2017, pytorch is a Python package that gives the user a blend of 2 high-level features – Tensor computation (like numpy) with strong GPU acceleration and the development of Deep Neural Networks on a tape-based auto diff system. Pytorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

 

5.TKINTER:

Python offers an easy and fast way for creating GUI applications. Tkinter is the standard GUI library for the Python programming language. It offers a powerful object-oriented interface for the Tk GUI toolkit.

Creating a GUI application using Tkinter is very easy. Tkinter offers over 15 types of widgets, including buttons, labels, and text boxes. Each of them has access to some specific geometry management methods that serve the purpose of organizing widgets throughout the parent widget area and supports effective object oriented interface.

 

6.REQUESTS:

One of the most popular general Python libraries is Requests that aims to make HTTP request simpler and more human-friendly. Licensed under the Apache2 license and written in Python, Requests is the de facto standard used by developers for making HTTP requests in Python.in addition to using the Requests library for sending HTTP requests to a server, it also allows adding form data, content, header, multi-part files, etc. With them. With the library, developers need not to add a query to the URL or form-encode the POST data manually.

 

7.BEAUTIFUL SOUP:

It is the most popular library used for webscaping in python .Beautiful Soup is a powerful tool that can save you hours of work. The library makes it easy to scrape information from web pages. It pulls data out of HTML and XML files and works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree.

 

8.MATPLOTLIB:

Matplotlib is by far the most common library in the Python community for exploration and data visualization. This library is the foundation of every other library. It provides countless charts and customization, from histograms to scatter plots, to customize and configure your plots, matplotlib sets down a variety of colors, themes, palettes, and other possibilities. If you are doing data analysis for a machine learning project or producing a report for stakeholders, matplotlib is certainly the most functional library.

 

9.PILLOW:

Python Imaging Library or PIL is a free Python library that adds an image processing ability to the Python interpreter. In simple terms, PIL allows manipulating, opening, and saving various image file formats in Python. Created by Alex Clark and Contributors, Pillow is a fork of the PIL library.

In addition to offering powerful image processing capabilities, Pillow offers an effective internal representation and extensive file format support. The core Python library is designed for offering fast access to data stored in a few basic pixel formats.

 

10.OPENCV

Open Source Computer Vision or opencv is used for image processing. It is a Python package that monitors overall functions focused on instant computer vision. Opencv provides several inbuilt functions, with the help of this you can learn Computer Vision. It allows both read and write images at the same time. Objects such as faces, trees, etc., can be diagnosed in any video or image. It is compatible with Windows, OS-X, and other operating systems.