Typically, a library is defined as a collection of books or a room or place where many books are stored for later use. Similarly, in the world of programming, a library is considered a collection of pre-compiled code that can be used later in a program to perform specific and defined operations. In addition to pre-compiled code, a library may contain documentation, configuration data, message templates, classes, values, and more.

Python libraries are a set of related modules. They contain packages of code that can be used repeatedly in different programs. Python libraries make programming easier and more convenient for the programmer, as we do not need to write the same code over and over for different programs. Python libraries play a very important role in fields such as machine learning, data science, and data visualization.

How Python Libraries Work

As mentioned earlier, Python libraries are simply a collection of code or code modules that we can use in a program to perform specific operations. We use libraries so that we don't need to write the code again in our program if it is already available. But how do these libraries work? In fact, in a Windows environment, library files have a DLL (Dynamic Load Libraries) extension. When we link a library with our program and run that program, the automatic linker searches for that library. It then extracts the functions of that library and interprets the program accordingly. This is how we use library methods in our programs.

Basic Python Libraries

Core Python libraries contain the precise syntax, language semantics, and symbols specific to the Python language. They contain built-in modules that provide access to basic system functions such as data I/O and some other essential modules. Most Python libraries are written in the C programming language. Core Python libraries consist of more than 200 essential modules. All these modules work together to make Python a high-level programming language. Basic Python libraries play a very important role; without them, programmers cannot access Python's functionality. Additionally, there are over 580,600 Python libraries registered on the PyPi website. But beyond that, there are many other libraries in Python that make a programmer's life easier. Let's take a look at some commonly used libraries:

1. NumPy Library

The NumPy library is one of the most famous libraries in the Python language, primarily used for array processing and scientific calculations. This library provides many useful tools that help developers deal with numerical data faster and more effectively. NumPy is considered the foundation for many other libraries such as Pandas and SciPy.

NumPy Features:

• Provides efficient data structures for handling multi-dimensional arrays.
• Optimizes mathematical operations, making them faster compared to traditional methods.
• Supports many mathematical functions such as linear algebra, statistics, and complex mathematical functions.

When to use NumPy:

NumPy is an excellent choice when you need to work with large amounts of numerical data and need to perform mathematical operations on them quickly. It is widely used in fields such as machine learning, artificial intelligence, and data analysis.

2. Pandas Library

Pandas is another popular library used mainly for data manipulation and analysis. It is the first choice for any programmer working in the field of Data Science or even simple data management. Pandas provides high-level data structures such as DataFrames that make dealing with large or unorganized data much easier.

Pandas Features:

• Seamless handling of structured data in formats like CSV, Excel, and SQL.
• Ability to perform data cleaning and transformation operations easily and effectively.
• Broad support for statistical operations and data analysis.

When to use Pandas:

Pandas is widely used in the field of data analysis when you need to import data from CSV files or databases, or when you need to process and clean data for use in analytical models.

3. Matplotlib Library

If you need to plot and visualize data, Matplotlib is the ideal library for that. Matplotlib is one of the most used libraries in the Python community for drawing graphs and data visualization in general. Whether you want to draw line, pie, or bar charts, Matplotlib provides the necessary tools for that.

Matplotlib Features:

• The ability to create high-quality and diverse graphs.
• Full customization of graphical elements.
• Broad support for multiple types of charts, such as line, pie, scatter plots, and histograms.

When to use Matplotlib:

Matplotlib is used when you want to represent your data visually for analysis or to display results. It is a powerful tool that helps clarify patterns and trends in data clearly.

4. SciPy Library

SciPy is considered an extension of the NumPy library and is a library used mainly in scientific and engineering operations. SciPy offers many advanced functions in fields such as linear algebra, statistics, signal analysis, and image processing.

SciPy Features:

• Provides advanced tools in complex mathematical operations.
• Works integrally with the NumPy library.
• Contains specialized functions in algorithm optimization, integration, and differential equations.

When to use SciPy:

SciPy is used when you need to perform advanced calculations, especially in scientific fields such as physics, engineering, and applied mathematics.

5. TensorFlow Library

TensorFlow is an open-source library developed by Google to facilitate building artificial intelligence and deep learning models. Although it is considered a relatively complex library, it has become one of the main tools in the field of machine learning thanks to its power and flexibility.

TensorFlow Features:

• Supports building deep learning models in a flexible and powerful way.
• Ability to run models on Graphics Processing Units (GPU) to achieve higher performance.
• Used in many modern applications such as classification and prediction, reinforcement learning, and image analysis.

When to use TensorFlow:

Use TensorFlow when you need to build complex machine learning models or AI applications. It is ideal in fields that require deep learning such as computer vision and natural language processing.

6. BeautifulSoup Library

BeautifulSoup is a library used to extract data from HTML and XML files. It is a very powerful tool for any developer working in the field of "Web Scraping", as it facilitates analyzing and structuring web pages to extract the required data.

BeautifulSoup Features:

• The ability to analyze HTML and XML in an easy and effective way.
• A simple interface that makes the process of extracting data from pages a smooth operation.
• The possibility of dealing with dynamic pages through integration with other libraries.

When to use BeautifulSoup:

Use BeautifulSoup when you need to analyze web pages and extract data from them, whether you are building tools to collect data from the internet or need to perform data analysis from different sources.

7. FastAPI Library

FastAPI is a modern framework for developing web applications using Python, and it is considered one of the fastest libraries in this field. FastAPI was designed to facilitate building fast and efficient Application Programming Interfaces (APIs), focusing on high performance and ease of use.

In the year 2025, I added FastAPI instead of Flask because I noticed a huge interest in the FastAPI article I wrote previously.

FastAPI Features:

• High Performance: FastAPI is based on Starlette and Pydantic, making it one of the fastest frameworks in Python.
• Automatic Documentation: FastAPI provides automatic interactive documentation using OpenAPI and Swagger.
• Type Support: FastAPI allows using data types, which facilitates input validation and increases code clarity.
• Ease of Use: It features a simple interface that is easy to learn, making it suitable for both beginners and professionals.

When to use FastAPI:

Use FastAPI when building high-performance APIs, especially in applications that require fast response and complex data processing. It is also ideal for projects that require integration with other systems or need clear and easy-to-use documentation.

8. Django Library

Django is another Python web development framework, but it is more complex and powerful than Flask. Django provides many advanced features for building large and complex web applications, and is famous for its ability to speed up the application development process thanks to built-in features such as ORM and an authentication system.

Learn more about the differences between Django and Flask.

Django Features:

• A comprehensive framework that provides many built-in tools.
• Supports large and complex projects.
• Contains an advanced security system.

When to use Django:

Django is an excellent choice if you need to build a complex and large web application that contains massive databases and multiple users. It provides everything you need to develop powerful applications in a short time.

9. OpenCV Library

OpenCV is an open-source library used primarily for image processing and Computer Vision. OpenCV is considered one of the most powerful libraries available for developing applications that deal with images and video, whether in processing still or moving images.

OpenCV Features:

• Broad support for a large range of image and video formats.
• Powerful tools for analyzing images and videos and recognizing patterns and objects.
• Flexibility in integration with other Python libraries such as NumPy and TensorFlow.

When to use OpenCV:

OpenCV is used in developing computer vision applications such as facial recognition, video processing, and developing augmented reality applications. It is considered an ideal tool for any project that requires dealing with visual media.

10. Requests Library

Requests is a library used to facilitate dealing with the HTTP protocol and making web requests in an easy and smooth way. Requests helps in sending requests to servers and getting data or sending it in a very simple way.

Requests Features:

• An easy programming interface for dealing with HTTP GET, POST, and other types of requests.
• Support for authentication operations and files, in addition to session setup.
• A large community and continuous support.

When to use Requests:

Use Requests when you need to connect to web servers to fetch or send data, such as when dealing with APIs or fetching data from web pages.