Main Python Libraries and Frameworks

Most used Python libraries for various purposes.

Introduction

Python is a versatile programming language known for its simplicity and readability, making it a popular choice for beginners and experts alike. One of Python's strengths is its vast ecosystem of libraries and frameworks, which can significantly speed up development and reduce the amount of code you need to write. In this article, we'll explore some of the most commonly used Python libraries and frameworks for different purposes.

Sites and APIs

• Requests: A popular Python library for making HTTP requests. It simplifies the process of sending HTTP requests, handling responses, and managing sessions. Learn more.

• Flask: A lightweight and flexible web framework for building web applications. Flask is known for its simplicity and ease of use, making it ideal for beginners. Learn more.

• Django: A more complex and powerful web framework for building robust web applications. Django comes with many built-in features and follows the "batteries-included" philosophy. Learn more.

Data Science and AI

• NumPy: A library for numerical computing in Python. It provides support for arrays, matrices, and many mathematical functions. Learn more.

• Pandas: A data manipulation and analysis library. Pandas make it easy to manipulate large datasets and perform complex operations with just a few lines of code. Learn more.

• Plotly (+ Dash): Interactive plotting library, with Dash for building web applications with complex interactions. Plotly allows you to create interactive charts and graphs, while Dash enables you to build web apps using Plotly visualizations. Learn more.

• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. It's a fundamental tool for data visualization. Learn more.

• Seaborn: A statistical data visualization library based on Matplotlib. Seaborn makes it easier to create complex visualizations with less code. Learn more.

• TensorFlow: An open-source machine learning framework developed by Google. TensorFlow is widely used for building and training machine learning models. Learn more.

• Keras: A high-level neural networks API, running on top of TensorFlow. Keras simplifies the process of building and training neural networks. Learn more.

• OpenCV: A library for computer vision and machine learning. OpenCV provides tools for image and video processing. Learn more.

• Pillow: A powerful library for opening, manipulating, and saving many different image file formats. Pillow is an essential tool for image processing tasks. Learn more.

• Scikit-learn: A machine learning library for classical machine learning algorithms. It provides simple and efficient tools for data mining and data analysis. Learn more.

• PyTorch: An open-source machine learning framework developed by Facebook. PyTorch is known for its flexibility and ease of use, particularly for deep learning applications. Learn more.

• NLTK: A powerful library for working with human language data. NLTK provides tools for text processing and analysis. Learn more.

Extra:

• PowerBI (With Py): Microsoft's business analytics service with Python integration. PowerBI allows you to create interactive reports and dashboards. Learn more.

• Streamlit: An open-source Python library that makes it easy to create web apps for machine learning and data science. Streamlit allows you to turn data scripts into shareable web apps in minutes. Learn more.

Automations

• Selenium: A tool for controlling web browsers through programs and automating browser tasks. Selenium is widely used for web testing and automation. Learn more.

• Scrapy: An open-source and collaborative web crawling framework for Python. Scrapy is used for extracting data from websites. Learn more.

• Beautiful Soup: A library for pulling data out of HTML and XML files. Beautiful Soup is often used for web scraping projects. Learn more.

• PyAutoGUI: A cross-platform GUI automation Python module. PyAutoGUI can simulate mouse clicks and keyboard presses. Learn more.

• Pyodbc (Databases): A Python module that makes accessing ODBC (Open Database Connectivity) databases simple. Pyodbc allows you to connect to various databases from your Python scripts. Learn more.

• Pywin32 (Windows automations): A set of Python extensions for Windows, providing access to many of the Windows API functions. Pywin32 is useful for automating Windows tasks. Learn more.

Graphic Interfaces

• Kivy: An open-source Python library for developing multitouch applications. Kivy is particularly useful for mobile application development. Learn more.

• Tkinter (comes with Python package already): The standard Python interface to the Tk GUI toolkit. Tkinter is a simple way to create GUIs in Python. Learn more.

• PyQt5: A set of Python bindings for Qt libraries. PyQt5 is used for creating desktop applications with advanced GUIs. Learn more.

Extra:

• Pygame (For games): A cross-platform set of Python modules designed for writing video games. Pygame is an excellent choice for game development in Python. Learn more.