libraries in python

Best 15 libraries in Python Programming. Get a complete with features, courses and books in 2021

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Libraries in Python

Python has a huge community with countless libraries for various fields that are easy, efficient, and convenient in use.

There are over 137,000 + libraries in Python that can be learned by python programmers

So, here is a list of the best libraries in Python that are highly preferred fields for different fields in programming.

Introduction to Python programming language

 

Python is one of the most popular programming languages these days; it is a high-level and general-purpose programming language with a wide range of applications.

Python was created in 1980’s by Guido Van Rossum at Centrum Wiskunde & Informatica in Netherlands.

Learning Python programming is very beneficial for your career in computer science and information technology as the use of Python is highly preferred in many fields of programming like Data Science and Machine Learning.

What is a Library?

 

A Library is a compilation of various reusable functions used in a computer program.

A programmer can access different operations in a library by call functions whenever needed.

Best 15 libraries in Python Programming

Data Science Libraries:

 

Data science is a field of computer science and information technology that has to deal with managing, processing of big data and operating data.

Python is highly preferred programming language for Data Science field. Here are some of the popular libraries in Python for Data Science.

1. Pandas :

 

 

Pandas is a free, open-source library in Python built by Wes McKinney in 2008.

Pandas is the highly preferred library in python that is used in Data Science and Machine learning.

Pandas work with huge amounts of data to analyze and extract essential data.

 
Features of Pandas:
  • Open source library in Python
  • Simple yet powerful library
  • Preferred library for Data manipulation and analysis
  • Supports multiple file formats
  • Fast and efficient
  • Built-in ability for data visualization
  • Helps you implement various operations on data

Tutorial to learn Pandas:

 

Book to refer for Pandas :

2. Matplotlib :

 

 

Matplotlib is a free, open source library in Python that focuses on data visualization. Matplotlib was developed by Michael Droettboom in 1985.

This library is used for plotting 2 dimensional and 3 dimensional graphs of arrays and plays an important role in data science and business analytics

It offers countless variety in graphs with customizable colors, themes and pallets.

 
Features of Matplotlib:
  • Sematic way to generate complex graphs.
  • Open source library.
  • Fast and efficient way for data visualization.
  • Huge variety in graphs to choice.
  • Provides many Customizations for graphs.

Tutorial for Matplotlib :

 

 

Book to refer :

 

3. Numpy :

 

 

Numpy is one of the most popular libraries in Python with essential applications in Data Science and machine learning.

Well-known libraries like TensorFlow, Scipy and Scikit Learn use Numpy internally for performing various operations on Tensors (n-dimensional data).

It is fast, efficient and highly preferred library for managing and operating with matrices and arrays.

 
Features of Numpy:
  • Easy to learn and use.
  • Open source library.
  • Solves complex mathematical problems efficiently.
  • High accuracy.
  • Large community.
  • Wide range of applications in Data science and Machine learning.

Tutorial for Numpy :

 

Book to refer :

4. Scipy :

 

 

Scipy is a free, open source library in python that focuses on scientific computation of complex mathematical operations.

It is used in solving problems on integration, differential equation and many more.

Scipy is uses Numpy internally to solve problems. Scipy is used in the field of Data Science and Machine Learning.

 
Features in Scipy:
  • Open source library in Python.
  • Solves scientific and mathematical problems efficiently.
  • Allows data manipulation and visualization.
  • Built on Numpy.
  • Wide range of applications in Data science and machine learning.
  • Scipy contains various pakages to perform different mathematical operations.

Tutorial to learn Scipy :

 

Book to refer :

Machine Learning Libraries:

 

Machine Learning is a branch of Artificial Intelligence in computer science and information technology that deals with automatic development in machines through previous experiences and data collection.

Python is highly preferred programming language for Machine Learning field. Here are some of the popular libraries in Python for Machine Learning

1. Tensorflow :

 

If you are learning machine learning and neutral networks you might have come across this famous library ‘TensorFlow’.

TensorFlow is a free, open source library developed by Google. It was fundamentally created for machine learning and deep learning applications.

TensorFlow was published in the year 2015. TensorFlow is used for high-performance numerical computation.

 
Features:
  • Accessible construct.
  • Flexible for use.
  • Easily Trainable and designable.
  • Parallel Neutral Network training.
  • Large Community.
  • Availability of statistical distribution.
  • Layered components.
  • Open source.

Tutorials to learn tensorflow :

 

Books to refer :

2. Keras :

 

Keras is an open source library in Python that focuses on neural networking and machine learning.

Keras uses Theano and TensorFlow in backend for compiling models, processing data sets and graphical visualization and much more.

 
Features:
  • Keras is a high level library that uses Theno and TensorFlow in its backend.
  • Large Community.
  • Flexible for use.
  • Open Source.
  • Runs smoothly on both CPU and GPU.
  • Easy to debug and explore.

Tutorials to learn keras :

 

Books to refer :

3. PyTorch :

 

PyTorch is a free, open source library in Python developed by Facebook AI Research Lab (FAIR) in 2016.

PyTorch is used in applications of Computer Vision and Natural language Processing.

 

Features of Pytorch:
  • Open source library in Python.
  • Easy and flexible.
  • It supports auto-differentiation.
  • It simplifies complex calculations.
  • Applications in computer vision and natural language processing.

Tutorial to learn Pytorch :

 

Books to refer :

4. OpenCV :

 

 

If you are interested in machine learning and robotics, you will surely come across this library.

OpenCV is a free, open source library in Python developed by Intel in 2000. 

OpenCV focuses on real-time Computer vision also features GPU acceleration for real-time operations.

 

Features of OpenCV:
  • Open source library in Python.
  • Read and write images.
  • Used for wide range of applications.
  • Performs feature detection.
  • Detects specific objects.

Tutorial to learn OpenCV :

 

Books to refer :

5. Theano :

 

 

Theano is a free, open source library in Python developed in University of Montreal in the year 2017.

Theano is a library and optimizing compiler for computing and manipulating multidimensional arrays in machine learning.

Theano is similar to TensorFlow and also used in parallel networking environment.

 

Features of Theano:
  • Solves mathematical expressions involving complex multi-dimensional arrays.
  • It supports GPU’s.
  • Integrates with Numpy.
  • Fast and stable.
  • Efficient symbolic differentiation.

Tutorial to learn Theano :

 

6. Natural Language Toolkit (NLTK) :

 

 

Natural Language Toolkit is a free, open source library in python. It is one of the most popular NLP python libraries.

NLTK works with human language data for applying to statistical language processing (NLP).

NLTK contains text processing libraries for tokenization, parsing, classification, stemming, tagging and sematic reasoning.

 
Features of NLTK:
  • Open source library in Python.
  • Popular Python NLP library.
  • It provides processing solutions for numerical and symbolic language processing.
  • NLTK comes along with dynamic discussion forum.
  • NLTK supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities.

Tutorial to learn NLTK :

 

Books to refer :

7. Scikit-Learn :

 

 

Scikit-Learn is one of the best libraries in Python for Data Science and Machine Learning.

Scikit-Learn is a free, open source library developed by David Cournapeau in 2007. It is an amazing library supported by Numpy and Scipy,

Scikit-Learn is an easy and best choice for a programmer to work with while dealing with huge and complex data.

 
Features of Scikit-Learn:
  • Free open source library in Python.
  • It provides various packages for feature extraction and selection, parameter tunneling, clustering, dimensionality reduction and many more.
  • Fast and efficient.
  • Flexible for use.

Tutorial to learn Scikit-Learn :

 

Book to refer :

Web Development Libraries:

Web development can be simply defined as creation and designing of a website and its maintenance.

Web development comprises of designing and building various web pages that are further combined to form a complete website. 

Web development involves frontend (Front-end) development and backend (Back-end) development.

 

Here is a list of some popular web development libraries in Python.

1. Requests :

 

 

Request is one of the most popular python HTTP library licensed by Apache 2.0. It is used to send all kinds of HTTP requests to the server.

 
Features of Requests:
  • Free open source library in Python.
  • Fast and efficient.
  • Flexible for use.
  • Easy to debug and explore.

Tutorial to learn Requests  :

 

Books to refer :

 

2. Beautiful Soup :

 

 

BeautifulSoup is the library in Python used for web scraping. It was developed by Leonard Richardson in 2004.

 
Features of BeautifulSoup:
  • Free open source library in Python.
  • Beautiful Soup is a Python library for pulling data out of HTML and XML files.
  • It is Fast and efficient.
  • Easy to learn.
  • Best library for web scraping.

Tutorial to learn BeautifulSoap :

 

Books to learn :

3. Selenium :

 

 

Selenium is a library in Python used to automate browsers licensed by Apache 2.0.

Selenium allows a playback tool for authoring functional tests using a test scripting language. It is used as a portable framework for testing web applications.

 
Features of Selenium:
  • Open Source library in Python.
  • Language and Framework Support.
  • Ease Of Implementation.
  • It is Fast and efficient.

Tutorial to learn Selenium :

 

Books to refer :

Game Development Library :

1. Pygame :

Pygame is an open source and cross-platform library in Python designed for creating 2D video games.

 Pygame was originally developed by Pete Shinners in 2000. It uses SDL (Simple DirectMedia Layer) library aims to allow real-time computer game development.

 
Features of Pygame:
  • Pygame is set of modules designed for video game programming.
  • Uses C and assembly code for core functions.
  • Simple and easy to use.
  • Comes on majority of operating systems.
  • It includes computer graphics and sound libraries.

Tutorial to learn Pygame :

 

Books to refer :

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