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Table of contents
1.
Introduction
2.
What is a Python library? 
3.
List of Top 10 Libraries in Python 2024
3.1.
1. Matplotlib 
3.2.
2. NumPy
3.3.
3. Pandas
3.4.
4. Scipy
3.5.
5. PyGame
3.6.
6. Pyglet
3.7.
7. Scrapy
3.8.
8. SymPy
3.9.
9. Fabric
3.10.
10. Pillow 
4.
How Python Libraries work?
5.
Python Standard Library
6.
Use of Libraries in Python Program
7.
Implementation of Library
8.
Benefits Of Using Python For Data Science
9.
How to choose the best Python Library?
10.
Frequently Asked Question
10.1.
What are the common Python libraries?
10.2.
How many libraries are in Python?
10.3.
Which Python library should I learn first?
10.4.
What is the most useful library in Python?
10.5.
What is the difference between Python libraries and API?
11.
Conclusion
Last Updated: Jun 4, 2024
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Top 10 Python Libraries

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Introduction

In this section, we are going to discuss the Python Libraries their function and some features of these libraries. But, before discussing Python Libraries, firstly we must have a better understanding of the terms like “Modules” and “Packages”.

Python Libraries

A Module is a file which contains some Python code whereas the term Packages state that it is a directory of sub-packages and modules. A Library is a collection of files (known as Modules) that contains functions for use by other programs. It means that Python Libraries are a reusable piece of code that we can add to our programmes.

They may also contain data values like numerical constants and other things. Library’s contents are supposed to be related, but there’s no way to enforce that. The Python standard library is an extensive suite of modules that comes with Python itself. Many additional libraries are available from PyPI (the Python Package Index) and from here we can conclude that a package is a library that can be installed using a package manager like RubyGems or npm.

What is a Python library? 

A Python library is a collection of pre-written code modules that provide a set of functionalities to perform specific tasks. These libraries contain reusable functions, classes, and constants that programmers can leverage to expedite development, avoid reinventing the wheel, and build more robust and complex applications. 

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List of Top 10 Libraries in Python 2024

1. Matplotlib 

It is one of very important Python Library which helps us to deal with data analysing and is a numerical plotting library. Actually It is a Python 2D plotting library which produces quality figures in a variety of Hardcopy Formats and interactive environment across platforms. Matplotlib can be used in Python Scripts, Python and Python shell. It tries to make easy things easy and hard things possible. In this, we can generate plots, Histograms, Power Spectra etc with the few lines of codes.

2. NumPy

It is one of the fundamental Library of Python, which has advanced math functions and a package of scientific computing with Python. It is useful for linear Algebra, Fourier Transformation and other various complex Mathematical functions.NumPy can also be used as an efficient multi-dimensional container of generic data, arbitrary data types are also defined in NumPy which makes it speedily integrate with a wide variety of database.

3. Pandas

Pandas, an open-source library, operates under the BSD (Berkeley Software Distribution) license. It is used for Data Science. It is used for Data Analysis purposes as it provides fast, expressive and flexible data structures to easily work with structured and time-series data in Python Programming Language.

4. Scipy

This library of Python is most popular, as we have been reading so much about this. It is just another form which may be used in place of NumPy. They use NumPy for more mathematical functions. SciPy uses NumPy arrays as their basic data structure and comes with modules for various commonly used task in scientific Programming, including Ordinary differential equations solving and signal processing.

5. PyGame

It is a set of Python modules which is used to create video games. It consists of computer graphics and sound libraries that are designed to be used with the Python programming language. Pygame was officially written by Pete Shinners to replace PySDL. Pygame is suitable to create client-side applications that can be potentially wrapped in a standalone executable.

6. Pyglet

It is a cross-platform windowing and multimedia library for python. Pyglet is an excellent choice for an object-oriented programming interface in developing games. In fact, it also finds use in developing other visually-rich applications for Mac OS X, Windows, and Linux. In the 90s, when people were bored, they resorted to playing Minecraft on their computers. Pyglet is the engine behind Minecraft.

7. Scrapy

If your motive is fast, high-level screen scraping and web crawling, then go for Scrapy. It is used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

8. SymPy

It is an open-source library for symbolic mathematics. With very simple and comprehensible code that is easily extensible, SymPy is a full-fledged Computer Algebra System (CAS). It is written in Python and hence does not need external libraries.

9. Fabric

Along with being a library, Fabric is a command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. Fabric is very simple and powerful and can help to automate repetitive command-line tasks. This approach can save time by automating our entire workflow. With Fabric, we can execute local or remote shell commands, upload/download files, and even prompt running user for input, or abort execution.

10. Pillow 

It is a Python Imaging Library(PIL), which adds support for opening, manipulating, and saving images. The current version identifies and reads a large number of formats. Pillow allows us to get some basic information about images.

How Python Libraries work?

Python libraries work by providing a collection of pre-written code modules that users can import into their programs. These libraries contain functions, classes, and constants that offer specific functionalities, ranging from numerical computing and data manipulation to web scraping and machine learning. When a library is imported into a Python script or program, its modules become accessible, allowing users to leverage the provided functionalities to accomplish their tasks efficiently. Libraries often rely on dependencies and external packages, which are automatically installed or managed using package managers like pip. By incorporating Python libraries into their projects, developers can streamline development, reduce code duplication, and benefit from the collective expertise of the Python community.

Python Standard Library

The Python standard library is a collection of modules bundled with the Python programming language. These modules are available for use without taking external installations or downloads. The standard library delivers developers with different functionalities and tools. It covers areas like network communication, data manipulation, mathematics, debugging, regular expressions, and more. 

The Python Standard Library is like an expansive collection of tools and resources that come with Python. It includes everything you need to write programs, like commands, functions, and ways to handle input and output. Access to the Python Standard Library makes Python a robust programming language. 

The standard library modules can be imported into Python programs using the import statement. It let the developers to access the functions, classes, and objects delivered by these modules. Proper documentation is available for the Python standard library. It offers precise and clear information to use these modules effectively. Developers greatly benefit from the Python standard library. As it provides a large number of tools that significantly enhance productivity and streamline the development process of Python applications.

Use of Libraries in Python Program

When writing large-scale Python projects, we want to keep the code modular. We split the code into different portions for easier maintenance, and we may use that code whenever we need it. Modules fill that role in Python. Instead of using the same code in several projects and complicating the code, we construct commonly used functions as modules and simply import them into a program wherever they are needed.

Although we do not need to write that code, we can take advantage of its capability by importing its module. A library contains a collection of interconnected modules. And we import modules from their libraries whenever we need to use them. Because of Python's basic syntax, it's a fairly simple job to do. We just need to use import.

Implementation of Library

We will write code of library in a python file. All the classes and methods of library will be in this file and these classes and methods will be reused by developer for their application. At the end of the file we will have if statement if__name__= ‘main’ The statement under this condition will be executed when this file is executed standalone as a script, otherwise this file is used as module.

end of the file

Now the library Operationlib is available which can be used in user file Operationlib.py.

library Operationlib

We have the classes and methods of library and we want to use them. The developer will reuse them using library file name.

classes and methods of library

Now we can place the library file and user file in the same directory, then we can run the user file. If we want to have the library file in a different directory. Then we have to set the library file path to environment variable PYTHONPATH. Then we can run the user file. In, this case any other user file from other directories can also use the library file.

Implementation of Library

The Output of above file is

Inside _init_
Taking Backup
Restoring to the previous version
Updating to the latest version

Benefits Of Using Python For Data Science

Python is widely used in data science due to its versatility, ease of learning, and extensive ecosystem of libraries and tools. Its rich collection of libraries such as NumPy, Pandas, and Scikit-learn offer powerful capabilities for data manipulation, analysis, and machine learning. Python's simplicity and readability make it accessible to beginners, while its scalability and performance meet the demands of professional data scientists. Moreover, Python's vibrant community fosters collaboration, innovation, and the sharing of best practices, further enhancing its appeal in the field of data science.

How to choose the best Python Library?

To choose a best Python library we can follow these steps:

  • Identify Requirements: Understand the specific needs and objectives of your project, such as data manipulation, visualization, or machine learning.
  • Research Libraries: Explore available Python libraries relevant to your requirements, considering factors like functionality, performance, and community support.
  • Evaluate Features: Assess the features and capabilities of each library, including ease of use, documentation quality, and compatibility with your existing tools and infrastructure.
  • Consider Performance: Evaluate the performance and efficiency of the library for handling large datasets and complex computations.
  • Community Support: Look for libraries with active communities, frequent updates, and a strong user base, ensuring ongoing development and support.
  • Compatibility: Ensure compatibility with other libraries and frameworks in your ecosystem, minimizing integration challenges and promoting interoperability.
  • Experiment and Test: Experiment with different libraries through prototyping and testing to gauge their suitability and performance in real-world scenarios.
  • Scalability: Consider the scalability of the library for future growth and expansion of your data science projects.
  • Feedback and Reviews: Seek feedback from peers, colleagues, and online reviews to gain insights into the experiences of other users with the library.
  • Documentation and Resources: Prioritize libraries with comprehensive documentation, tutorials, and educational resources to support your learning and development efforts.

Frequently Asked Question

What are the common Python libraries?

Some common Python libraries are NumPy for numerical computing, Pandas for records manipulation and analysis, Matplotlib for records visualization, and TensorFlow for deep analysis. Those libraries are broadly used in numerous statistics, science, and medical computing programs.

How many libraries are in Python?

Python has a vast and continuously growing ecosystem of libraries. The total numbers of Python are more than 137000 libraries. All these libraries are used in machine learning, data science, data manipulation and visualization, and more.

Which Python library should I learn first?

For beginners, start with pandas for data manipulation and analysis. It's user-friendly, widely used in data science, and provides powerful data structures. Learning pandas will build a strong foundation for exploring other libraries like numpy and matplotlib.

What is the most useful library in Python?

There are only so many helpful libraries in Python; it depends on your project or task's specific needs and requirements. They are NumPy, used for numerical computing; Pandas, used for data manipulation; Matplotlib provides tools for visualization and many more.

What is the difference between Python libraries and API?

Python libraries are collections of pre-written code modules that provide specific functionalities to perform tasks within Python programs. APIs (Application Programming Interfaces) define protocols and methods for communication between different software components, allowing them to interact and exchange data.

Conclusion

After reading this article now we know which libraries to go for if we choose to extend our career in Python. Many of the Python Libraries help us with data-science as well. And if someone wishes to go out of his way, then create his own library, and get it published with the PyPI and help the community grow.

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