Python libraries are pre-built code packages that can be imported and reused across different projects, saving developers from having to build everything from scratch.
Python is an extremely powerful and versatile language, which has made it highly popular among developers. It has an active community and a wide range of specialized libraries.
In addition, Python libraries play a crucial role in the ecosystem of development with Python, as they provide a vast array of resources and templates that make building applications easier.
In this article, we’ve gathered 24 of the best Python libraries that can be used for web development, data science, automation, and graphical interfaces. Check out the full list below!
Learn more: Python Documentation: how to create and maintain It for your projects
Python Libraries for Web Development and APIs
Several libraries and frameworks simplify the creation of robust and efficient web applications and APIs in Python. Here are some examples:
1. Django
Django is a complete and robust framework that offers a wide range of essential features for web development. It includes an integrated ORM (Object-Relational Mapping), automatic admin interface, template support, and more.
2. Requests
A simple library for making HTTP requests in Python. It streamlines communication with APIs and web services when data needs to be retrieved or sent to a website.
3. Asyncio
Asyncio is Python’s native library for asynchronous programming. It forms the basis for many modern frameworks and allows you to write non-blocking code using coroutines, event loops, and the async/await syntax.
4. Flask
Like Django, Flask is also a collection of libraries. It’s lightweight and flexible, ideal for building simple web applications and APIs. Flask gives developers more freedom and flexibility.
5. FastAPI
A modern, high-performance framework ideal for building web APIs that use Python’s type hints. It offers a more productive and safer development experience, with built-in support for automatic interactive documentation and data validation.
Learn more: List of Python tools to practice online
Python Libraries for Data Science
There are several libraries and frameworks packed with tools, algorithms, and resources widely used in data science and artificial intelligence with Python. See below:
6. NumPy
NumPy is a fundamental library for anyone working with numerical computing in Python. It supports multidimensional arrays and essential mathematical functions for efficient numerical operations.
Learn more: Logs in Python for RPA automation
7. SciPy
SciPy (Scientific Python) is a set of algorithms and functions based on the NumPy extension of Python. It is widely used in scientific and technical computations, including linear algebra, optimization algorithms, solving differential equations, and the Fourier transform. SciPy’s functionalities include optimization, statistics, and signal processing.
This library is commonly used for scientific and technical computing, designed to work with NumPy arrays. It provides efficient and easy-to-use numerical routines, and it is free.
8. Pandas
Essential for anyone working with data science, this library is used for manipulating structured data, providing flexible data structures, transformation, and data analysis.
9. Matplotlib
Matplotlib is a graphics library that offers powerful and attractive visualizations. It is widely used in Python for data visualization. Furthermore, it can serve as a replacement for MATLAB, with the advantages of being free and open-source.
Matplotlib is used to create static, animated, and interactive visualizations in Python, generate publication-quality figures, and provide a wide range of graphs and diagrams.
10. Seaborn
Seaborn is a Python data visualization library based on Matplotlib. It’s a library for creating statistical graphs in Python.
Seaborn integrates closely with pandas data structures. Seaborn is used to explore and understand your data, creating beautiful and intuitive visualizations.
Its features include a high-level interface for drawing statistical graphs, and it’s considered an upgrade to the Matplotlib library because it uses attractive themes to plot Matplotlib graphs. It integrates closely with Pandas data structures.
Learn more: Python surpasses JavaScript on GitHub
Python Libraries for Artificial Intelligence
11. PyTorch
PyTorch is an open-source machine learning library that supports the construction and training of deep learning models, using a dynamic approach to artificial intelligence model development.
12. TensorFlow
An open-source library focused on creating artificial intelligence models. It is a flexible and scalable platform for developing, training, and deploying deep learning models.
13. Keras
Keras is a popular library widely used for deep learning modules and neural networks (similar to TensorFlow). Keras supports various backends, like TensorFlow, and acts as an interface to the TensorFlow library.
Its features include the development of deep learning modules and neural networks, a minimalist and modular approach.
Companies like Google, Netflix, Huawei, and Uber are currently using Keras, which works seamlessly on both CPU and GPU.
14. OpenCV
A widely used library for computer vision and image processing. It offers a vast range of functionalities and algorithms for analyzing, processing, and manipulating images and videos.
15. Scikit-learn
Scikit-learn is an open-source machine learning library designed to be simple and facilitate the implementation of machine learning algorithms for tasks such as clustering, regression, and dimensionality reduction.
Learn more: Shadow Python: everything you need to know today
Python libraries for automation and scripting
To assist with automation and scripting in Python, there are several libraries and frameworks.
They are responsible for facilitating task execution, interaction with systems, and also assisting in the development of scripts for process automation. Here are some options:
16. Selenium
Selenium is a library focused on automation and web navigation and browser testing. It allows the automation of processes on websites, interaction with web pages, form filling, click processing, among other automation actions.
17. BotCity
BotCity is a Python automation governance and orchestration platform with features such as computer vision, robot queues, datapools, and dashboards.
It offers a code assistant that generates scripts from developer actions in the UI, works in different IDEs (such as VSCode), and allows running automations in VMs, containers, or desktops.
Its orchestrator runs Python automations in frameworks like Selenium, PyAutoGUI, Robot, and TagUI, with its own libraries and over 30 plugins for common RPA scenarios.
At the heart of governance is BotCity Sentinel, which monitors Python usage (including AI-generated scripts) on workstations to identify Shadow Python and enforce compliance without reducing productivity.
Learn more: Shadow Python Risks: Understanding the Hidden Threat
18. Scrapy
Focused on web scraping, that is, extracting information from websites for your Python code. It is a widely used open-source framework for building web spiders that collect information and store the data in structured formats.
19. Pyautogui
Library for automating Graphical User Interface (GUI) tasks in operating systems such as macOS, Windows, and Linux, allowing the simulation of mouse and keyboard actions and screen capture.
20. PyODBC
PyODBC provides a programming interface for connecting to databases using Open Database Connectivity (ODBC), a standard API that allows applications to communicate with relational databases, regardless of the management system.
21. Paramiko
A library for automating operations on remote systems via Secure Shell (SSH), enabling automated command execution, file transfer, and system management.
Python Libraries for Creating Graphical User Interfaces
To assist in creating graphical user interfaces in Python, there are several libraries and frameworks that facilitate the development of applications with interactive and attractive user interfaces. Check out some options:
22. Tkinter
Standard Python library focused on creating graphical interfaces without many features. Simple to use for developing basic and intermediate applications. It has predefined widgets and easy integration.
23. Kivy
Python framework for developing multi-touch applications and graphical interfaces with support for touch and gesture input. Code developed in Kivy can be run on any platform, Linux, macOS, and Android, for example.
24. Dear PyGui
Aimed at developing quick and simple applications in Python, Dear PyGui is designed to be easy to use and offers high performance efficiency.
How many libraries are there in Python?
The Python community is very dynamic and constantly growing, with new libraries being developed regularly. This makes it very difficult to define an exact number of existing Python libraries.
However, by the end of 2022, the Python Package Index (PyPl), the official repository of Python packages, had over 350,000 registered packages. In other words, the diversity of Python packages is enormous.
What are the best Python libraries?
Knowing that there are many Python libraries, choosing the best one for your project can seem challenging.
However, with research and the help of guides like this one, it’s possible to choose the best Python library to develop your project. Check out some aspects that can be taken into account when making this decision:
- Needs: Carefully consider your specific needs. Consider the type of project being developed and identify the challenges;
- Documentation: Check if the libraries you are considering have documentation and resources that can guide you in developing the project, since libraries with code examples and tutorials help in understanding their application;
- Efficiency: Consider whether the chosen Python library is efficient in terms of speed, resource utilization, and ability to handle complex tasks;
- Updates: Look for Python libraries that are constantly being developed, check the history of updates and bug fixes.
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Learn more: BotCity Academy
Ready to work with Python libraries?
We hope this article has provided important information about the main Python libraries used. Remember that the choice of library or framework depends on your project needs, personal preferences, and development prerequisites.
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