What is a Library?

A library can be defined as a collection of pre-combined codes that can be put to work to avoid writing long and lengthy codes from scratch, saving the time of developers and increasing code reusability. Instead of writing everything from ground zero, you can borrow these ready-made blocks of code to save time and effort. They can also be defined as a collection of reusable resources and functions that can be implemented while writing code to make the process easier.

What Are Libraries Of  Python?

Libraries of Python is a collection of pre-combined, modules and packages that help you to perform various tasks and include various functionalities in your code. These promote small-length codes and help you to avoid writing lengthy code from scratch by substituting these with already defined functions.

These libraries of Python contain pre-written code, classes, and functions that can be easily implemented to write different programs. We have several libraries of Python that are capable of performing certain mathematical functions and computations. Python has a huge storehouse of libraries that cover various IT services like web development, data analysis, machine learning, etc.

To use these libraries of Python we first need to begin by importing libraries using the import statement. The import can be such that you can import the libraries as a whole and also specific modules within that particular library.

Advantages Of Libraries Of Python

These libraries of Python help us a lot through the process, there are plenty of libraries of Python available, each used for different purposes and tasks. The basic advantages of these libraries of python include:

Time-efficient: As we have already discussed that the libraries provide pre-written code, hence we don’t have to write everything from the beginning. This saves a lot of time for the testing team as well as the developer.

Reliability: These libraries of Python are experimented and well-tested, and then exposed to the audience for usage. Hence we can say that these are legitimate and help to reduce various bugs in your code. 

Ease of Use: As we have already discussed above, these libraries of Python are reliable, simplified and easier to use. They are capable of doing complex tasks by using easy function calls and methods.

Community Support and Enhanced Productivity: These libraries of Python have large communities and help groups that work continuously to improve their quality. Moreover using such libraries of Python helps in handling routine tasks, thus you can be creative regarding your codes and focus on other aspects of your project, enhancing productivity and overall user engagement.

Top 15 Libraries of Python

Now we will move to the main objective of this article and study about 15 top libraries of Python that everyone should know. We will be discussing various Python libraries for data scientists, web developers, engineers, analysts, etc. in this section. These top libraries of Python include:

1. NumPy

The very first in the list of top libraries of Python is NumPy. This is extensively used by data scientists for scientific computation. It is one of the most commonly used packages in Python, that is capable of:

• Array Handling: You can create and manage arrays, both one-dimensional and multi-dimensional.
• Mathematical Operations: You can perform a variety of math operations, such as addition, subtraction, multiplication, and division.
• Logical Operations: Besides mathematical operations, you can easily perform logical operations, such as comparison of elements etc. NumPy makes use of BLAS and LAPACK for performing these scientific and mathematical computations.

2. TensorFlow

TensorFlow is among the open-source libraries of Python that can perform numerical computation and is also used for implementing machine learning algorithms. It is used by researchers and physicists for running out their respective functionalities. The basic features of this library include:

• Machine Learning: As discussed above TensorFlow is a very famous tool for building machine learning models and implementing them with ease.
• Scalable and efficient: TensorFlow is capable of handling large amounts of data and complex code blocks.
• Visualization Tools: Along with machine learning TensorFlow also has certain tools like TensorBoard, which help you to implement Data Visualization and visualize the training process by translation of complex codes into visual formats.

3. Pandas

It is an open-source, free software license. Pandas provide various provisions for data analysis and make use of easy data structures.  For operations like data analysis and modeling, Pandas can make the whole process a lot more easier without making any upgrades to more specific languages. Basic features of this library include:

• Integration: It can work well with other Python libraries like NumPy and Matplotlib.
• Cleaning unnecessary and noisy data: Pandas can clean messy data by handling missing values, this is very essential when it comes to data handling as we have to deal with a big chunk of data.
• Tabular Data: Pandas can convert data in a tabular format, like a spreadsheet or SQL table, making data handling easier and boosting the analysis process.

4. Requests

Requests are extensively used by web developers and help you to integrate python packages with various web development languages like HTML, CSS etc. It enables you to send HTTP requests and include headers, using basic Python dictionaries. Basic features of this library are explained below in brief for your better understanding:

• Easy to Use: It is very easy to use as it implements simple functions like get() and post() to make HTTP requests.
• HTTP Requests: As already mentioned before, This library allow you to send HTTP requests to web servers, like fetching web pages or retrieving data answers from client servers. This is the main standout feature of this library.
• Timeouts and Errors: Setting timeouts is also an integral part of web development. This basically defines and controls the amount of time to wait for a response, this can be implemented using Requests which further helps in handling errors efficiently.

5. SQLAlchemy

SQLAlchemy is a Database abstraction Python library that features a range of databases. It provides features like:

• Query Building: It allows you to build complex database queries by using simple Python-based methods and packages.
• Easy to understand: This Python library is beginner-friendly and is very easy to implement in a system. It improves the speed of communication between Python language and databases and supports most of the Platforms.
• Transactions: SQLAlchemy supports transactions, which ensure that multiple database operations either succeed together or fail together( ACID PROPERTIES), maintaining data integrity.
• Community Support: This python library have large communities and help groups that work continuously for improving their quality.

6. Scikit- learn

It is an open source machine learning library. It is used in integration with various models like classification, regression, clustering, model selection etc. This Python library uses a wide range of machine-learning algorithms that are easy to use and implement. Spotify uses Scikit-learn integrated with supervised learning for its music recommendations. The key features of this library include:

• Ease of Integration: You can easily integrate sci-kit-learn into your existing Python projects making it highly accessible. It easily integrates with other Python libraries like NumPy and Pandas which we have discussed above, and helps you to work with data arrays and other data types.
• Scalability: Scikit-learn is scalable and suitable for both small and large datasets, with algorithms offering overall optimization.
• Open Source and a huge community: It is open-source and actively maintained by its loyal community members who make efforts to ensure continuous improvements and updates.

7. Bob

It was developed at the Idiap Research Institute in Switzerland, Bob is an open-source signal-processing libraries of Python. The initial formation of this library includes the usage of both Python and C++. This Python library promotes image recognition, video processing, and other advanced features using machine learning algorithms.

• Machine Learning and Pattern Recognition: Bob incorporates tools for tasks like face recognition, speaker recognition, and other pattern recognition tasks.
• Evaluation and Benchmarking: It also provides tools for evaluating and benchmarking algorithms, helping you assess and compare performances.
• Data Handling: Bob has certain capabilities to handle datasets and carry out preprocessing of data, all this integrated with machine learning.

8. Dash

This Python library acts like a digital monitor that helps you explore data and keep track of all your experiments and operations done on the data. Dash acts like the front end of the Python backend.  Not only this it also has a productive framework for data visualization that helps you to get information of all your operations at a glance through visual formats like maps and graphs. Key features include:

• Interactive Visualizations: As discussed before you can create interactive graphs and charts with Plotly( a feature of Dash) that allows users to explore data dynamically by zooming and filtering.
• Web Applications: This libraries in Python enables you to create interactive web applications to engage more users by combining data visualization, user input, and business trends.
• Callbacks for Interactivity:  This is also one of the unique features of Dash, it uses callbacks to update the content of your app based on user input enhancing real-time interactivity without reloading the page.

9. Scipy

It is among the open-source libraries of Python used for scientific computing. Scipy is also used for quality assurance by data scientists and quality support engineers. The core Scipy packages include Numpy, SciPy library, Matplotlib, and Pandas. We have already discussed most of these libraries so far and might discuss other of these mentioned above in the later section of this article. The key features of this library are:

• Signal and Image Processing: SciPy incorporates tools for signal and image processing which is useful for analyzing and manipulating data in various domains.
• Sparse Matrices: SciPy helps us to cater to the problem of high usage of memory and computer hardware, by supporting sparse matrices, which are memory-efficient and carry out representations of large datasets for tasks like solving linear systems and other algorithms keeping in mind the minimum memory space.
• Documentation and Community: This Python library has large communities and help groups that work continuously to improve their quality.

10. Matplotlib

This is also a mathematical library but it answers your questions in a pictorial representation i.e. dimensional plotting. It is among the open-source libraries of Python that help you to translate all your algebraic or linear mathematical problems into visual graphs on interactive platforms. You can design charts, graphs, pie charts, scatter plots, histograms, error charts, etc. The salient features of this library are:

• Plotting: This is the most unique feature of Matplotlib that allows you to create static, animated, and interactive visualizations through graphs and other visual formats.
• Customization:  Now, when you have already visualized your data, you can even customize it i.e. you can make the graph as per your liking giving specifications of your plots, colors, labels, markers, axes, etc.
• Integration: It integrates well with other Python libraries like NumPy, SymPy, and Pandas, allowing you to plot data stored in arrays, data frames, etc. very efficiently. This also makes it a versatile library.

11. SymPy

We again have one of the most widely known libraries of Python which works on your mathematical operations. This library deals with all sorts of symbolic mathematics, i.e. an effective way for computer algebra systems (CAS) while keeping the code as simple as possible and easily extensible. Let’s see its features for a better understanding of the concept:

• Symbolic Mathematics: SymPy is a Python library for performing symbolic mathematics, allowing you to work with mathematical expressions symbolically rather than numerically. This helps you to write shorter code lines by using mathematical symbols rather than their complete names.
• Algebraic Operations: SymPy supports basic algebraic operations such as simplification, expansion, factorization, and substitution of variables in expressions.
• Plotting: It also includes plotting capabilities to visualize mathematical expressions and functions using Matplotlib (discussed above).

12. Ramp

This Python library is used for the rapid prototyping of machine-learning models. This basically deals with creating interactive prototypes that can be used for further research and improvement. Its features include:

• Ease of usage: Ramp uses a simple syntax for implementing various functionalities and has user-friendly algorithms. It is a lightweight pandas-based machine-learning framework, that enhances the seamless environment of applications.
• Community assistance: This Python library has large communities and helps groups that work continuously to improve their quality.

13. Pyglet

Pyglet is used for designing visually appealing games and other applications that engage a bigger section of users. This Python library supports user interface events, joysticks, OpenGL graphics, animations, movies, playing sounds, etc all under one platform. Linux, OS X, and Windows all support Pyglet. Its features include:

• Graphics and Multimedia: This Python library is made for creating games, simulations, and interactive multimedia applications with graphics, sound, and video.
• OpenGL Integration: It integrates with OpenGL which is a graphics rendering API that allows you to create 2D and 3D graphics seamlessly.
• Text and GUI Elements: Pyglet includes features for text and basic GUI( graphical user interface) elements within applications, enhancing user engagement and interactions.
• Audio and Video Playback: It supports playing audio and video files, enabling multimedia applications to incorporate sound effects and real-time musical upgradation all under one platform!

This is one of the most widely used libraries of Python generally used for high scalability projects.

14. Bokeh

This Python library can be solely described as a Data visualization library that allows interactive visualization by making use of HTML and Javascript to provide graphics.  It is highly flexible and one such library that is used in integration with web-based applications. The basic features of this Bokeh include:

• Web-Based and Interactive Visualization: Bokeh is a Python library used for creating interactive and dynamic visualizations in web browsers. It further generates plots and graphics in HTML and JavaScript, enabling users to engage in real-time.
• Integration: It integrates well with other Python libraries such as Pandas, NumPy, and SciPy, enabling seamless data handling and integration with existing workflows.
• Export and Sharing: This Python library can be exported as standalone HTML files or by embedding them in web applications.

15. NuPIC

This Python library is a platform that makes use of the HTM learning algorithm. This also carries out the translation of these algorithms in public source. This is mainly based on the biology of the neocortex. Now we will talk about its basic features:

• Easy to understand: This Python library beginners friendly and is very easy to implement in a system. It improves the speed of communication between Python language and web based servers.
• Highly Flexible: This python library can be used to perform various programming tasks with the use of extensive libraries and built in functions.
• Efficient Memory Management: This python library involves automatic garbage collection of free up unused memory and promotes efficient memory utilization.

Thus we have discussed 15 libraries of Python that all developers must know to ease their work out. All these libraries of Python have different features that can be used respectively as per the requirements of the users and the specifications of the product.