Which library is widely used for scientific computing and provides a comprehensive set of tools for numerical analysis?

Which library extends NumPy and provides additional modules for optimization, integration, and statistical analysis?

Which library is designed specifically for deep learning and provides efficient tensor computation?

Which library is known for its dynamic computation graph and is widely used for deep learning research?

Which library is designed for high-performance scientific computing and provides a wide range of mathematical functions?

Which library is commonly used for data analysis and manipulation, providing data structures and operations for working with large datasets?

Which library is designed for symbolic mathematics and provides tools for manipulating mathematical expressions and performing symbolic calculations?

Which library is known for its high-performance linear algebra operations and is widely used in scientific computing?

Which library provides a comprehensive set of routines for solving linear systems, eigenvalue problems, and other matrix computations?

Which library is designed for optimization and provides algorithms for finding minima or maxima of functions?

Which library is known for its high-performance statistical computing and provides a wide range of statistical functions and methods?

Which library is designed for machine learning and provides algorithms for classification, regression, and clustering?

Which library is known for its high-performance data analysis and manipulation capabilities, including operations like filtering, sorting, and grouping?

Which library is designed for parallel and distributed computing, allowing users to distribute tasks across multiple cores or machines?

Which library is known for its high-performance numerical integration and differentiation routines?