Introduction

Due to the huge number of libraries, Python is used seamlessly for the trending technologies like Artificial intelligence,web development, scripting, game development, and much more. Because of its rapid growth in advanced technologies, the popularity of python is drastically increased in the past few years. In this article, the top Python libraries are mentioned that are in trend among the developers.

To begin with, lets us understand what a Library & Python library is,

Library

A library refers to a collection of pre-written code like files, programs, routines, scripts, functions, classes that can beimport to our project for adding specific functionality. Libraries help us to use thepre-written code without writing the code from the scratch to enhance the functionality implemented by our code.

Python Library

Python offers a large number of python libraries with pre-compiled code that is used for the different types of development, ranging from web development to machine learning. Python Standard Library plays a very important rolewhich consists of more than 200 core modules. It makes python coding simpler and convenient for the developers along with its simple syntax to implement complex projects. Let’s take a look at the list of the most popular Python libraries used in different development areas.

Pandas

Pandas stands for ‘Python Data Analysis Library'. Pandas is an important data analysis library used for data science applications.

Python is an open-source library that provides high performance among the most famous python libraries. This Library provides flexible data structure to work with structured data like tabular, multidimensional heterogeneous datas and time series data.Itsupportdata science operations like Sorting, Iteration, Concatenation, data conversion, Visualizations, Aggregations, etc. It is simple, fast, and expressive among other data analysis libraries and they are:
  • Numpy
  • SciPy
  • Cython
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Features of Pandas

  • Easy to learn and use offering high performance and provides Time Series functionality.
  • It has many built-in methods with graphical interface used for reshaping and pivoting of the data sets.
  • It is used for data alignment,integration of the missing data and aggregations, transformations.
  • Process different formats of data sets like matrix data, tabular heterogeneous, time series and its operations such as subsetting, slicing, filtering, groupBy, re-ordering, and re-shaping.

NumPy

NumPystands for ‘Numerical Python’. NumPy is a popular Machine learning library with advanced math functions and a rudimentary scientific computing package.

NumPy is also one of the famous Python libraries which providemost basic Data Structures, multidimensional array objects and a collection of routines for processing of array. It consists of in-built mathematical functions, logical operations for easy computations. It has in-built functions for linear algebra and random number generation.The most important object defined in NumPy is an N-dimensional array type called ndarray.

Features of NumPy

  • It provides many mathematical functionalities with useful linear algebra, Fourier transform, and random number capabilities.
  • It is easy to learn and use with good documentation.
  • Sophisticated functions and can be used in Data Science.
  • It is used for scientific purpose with Tools for integrating C/C++ and Fortran code.

SciPy

SciPy stands for ‘Scientific Python’. Among many libraries for data science, SciPy is the simplest library with many mathematical algorithms that are ideal for solving statistics problems.

SciPy is an open-source library used for high-level scientific computationssolving mathematical, scientific, engineering, and technical problems. It is similar to NumPy to handle complex computations which heals with large number of data and stats. It works with Numpy allows sorting and indexing of array data, the numerical data code is stored in SciPy. It is an open-source library with huge community support commonly used by developers.

Features of SciPy

  • Large community support with many built in scientific constants.
  • Rapid development with data science tools.
  • Easy to get started with different data structures like graph, matrix.

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TensorFlow

TensorFlow is the most popular library that is used for implementing machine learning and deep learning applications. TensorFlowis an open source machine learning framework for all developers developed byGoogle teamto build their AI models.

TensorFlow can store an algorithm in a cube and array-like structure and perform a tensor operation on them. Even in neural computation, developers use TensorFlow because it is very good with the new algorithms.

TensorFlow Features

  • It is an open-source library.WithTensorFlow, we can develop ML models easily because it uses high-level APIs.
  • It can create ML models for different platforms.
  • TensorFlow comes with a simple and flexible architecture.
  • It has a large expert community.

Matplotlib

This Python library is used to provide a graphical interface to represent the data. Matplotlib is used with NumPy and SciPy libraries because the main job of this library is to plot 2D graphs for various filtered data.

First, we use the NumPy and SciPy or any other data analysis library to sort the data, and then we use matplotlib to get a proper graphical view of that data. This library helps to analyze the data in a fun way so even non-programmers can also read and interpret the data easily.

Matplotlib Features

  • It helps to plot a graphical interface for the generated data.
  • This library is fun to use.
  • It can plot various kinds of graphs like a bar graph, a pie graph, and more.
  • This library is very easy to learn.

Keras

It is an open-source and high-level neural network Python library that is commonly used for developing deep learning algorithms and performing experiments. This library is written in Python itself, which makes it capable of running on top of other machine learning libraries for Python.

Keras also use Theano and TensorFlow at the backend to perform many tasks. The main drawback of Keras is that its speed is a little slow as compared to other machine learning libraries.

Keras Features

  • It offers many features like compiling models, processing data-sets, visualization of graphs, and much more.
  • It provides a modular approach for creating artificial neural networks.
  • Being created using Python itself, it is easy to debug.

Theano

It is another powerful Python library for machine learning, deep learning, and data analysis. It offers many mathematical algorithms and also comes with multi-dimensional array support that can be used for handling complex data.

Theano Features

  • Supports integration with NumPy.
  • It can perform many complex mathematical functions.
  • It can evaluate expression faster as compared to other Python machine learning libraries.

Scikit-Learn

Scikit-Learn is among the widely used Python library for machine learning and data science. When it comes to managing and processing huge amounts of complex data, many developers consider this library as the ideal option.

This Python library comes with many built-in algorithms and training methods, including regression. Scikit-Learn is built using NumPy, SciPy, and matplotlib libraries and is known for its exceptional data handling power.

Scikit-Learn Features

  • It has many inbuilt methods for cross-validation.
  • Allows development of unsupervised learning models.
  • It also provides a graphical interface to it easier for developers to work with it.

Pygame

If you want to use Python to develop games, you can use the Pygame library. This library allows you to develop games for the Windows and Linux platforms. Though this library doesn’t support the creation of 3D games, it is a great option for intermediate-level Python developers looking to level up their game development skills.

PyGame Features

  • It has good documentation.
  • You can create 2D games.
  • It is easy to learn and implement.

Request

Request library is among the most useful Python libraries. Almost every Python web framework uses this library to send HTTP requests to servers. The request library helps us to send organic HTTP requests without any manual intervention.

Request Features

  • International Domains and URLs.
  • Comes with Keep-Alive & connection pooling.
  • Enables sessions with cookie persistence.
  • Supports browser-style SSL verification.

Beautiful Soup

Beautiful Soup library is an ideal choice for developers to scrape information from a web page, make HTTP requests using Beautiful Soup library and Request library, and retrieve data from a web page in the form of HTML or XML. It can also retrieve API data if API data is present on the web page. This library is easy to use and you can go through its documentation to explore all its capabilities.

Beautiful Soup Features

  • It can use the API of a website.
  • It can access the HTML of a web page to extract useful information.

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