NumPy: NumPy is a powerful library for numerical computing in Python. It provides efficient and high-performance multidimensional array objects and a wide range of mathematical functions to operate on these arrays. NumPy is widely used in scientific computing, data analysis, and machine learning.
Pandas: Pandas is a popular library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that allow you to efficiently handle and manipulate structured data. Pandas is commonly used for tasks such as data cleaning, data wrangling, and exploratory data analysis.
Matplotlib: Matplotlib is a versatile plotting library in Python that enables you to create a wide variety of static, animated, and interactive visualizations. It provides a MATLAB-like interface and supports a range of plots, including line plots, scatter plots, bar plots, histograms, and more. Matplotlib is widely used for data visualization in scientific computing and data analysis.
Scikit-learn: Scikit-learn is a comprehensive machine learning library in Python. It provides a consistent and user-friendly interface for various machine learning tasks, such as classification, regression, clustering, dimensionality reduction, and model evaluation. Scikit-learn incorporates a wide range of algorithms and tools, making it a go-to choice for many machine learning practitioners.
Requests: Requests is a simple and elegant HTTP library for Python. It allows you to send HTTP requests, handle responses, and interact with web services and APIs. Requests provides a straightforward API for making HTTP GET, POST, PUT, DELETE, and other types of requests. It also supports features like authentication, session management, cookies, and more, making it a powerful tool for web scraping, web development, and API integration.
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