NumPy Description
The NumPy vectorization and indexing concepts are fast and flexible. They are the current de-facto standard in array computing. NumPy provides comprehensive mathematical functions, random numbers generators, linear algebra routines and Fourier transforms. NumPy is compatible with a wide variety of hardware and computing platforms. It also works well with sparse array libraries, distributed, GPU, or GPU. NumPy's core is C code that has been optimized. Enjoy Python's flexibility with the speed and efficiency of compiled code. NumPy's high-level syntax makes it easy for programmers of all backgrounds and experience levels. NumPy brings the computational power and simplicity of languages such as C and Fortran into Python, making it a language that is much easier to learn and to use. This power is often accompanied by simplicity: NumPy solutions are often simple and elegant.
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Summary: NumPy is an essential part of the Python ecosystem. It provides a huge variety of mathematical functions in a very performant library for free.
Positive: - used for scientific computing
- huge variety of mathematical functions, random number generators, etc.
- supports a wide variety of hardware and GPU acceleration
- very fast code that runs in C, despite working with Python
- simple syntax makes it easy to learn
- good documentation
- free and open sourceNegative: - there aren't many cons to using NumPy; it's a mainstay of the Python computing community for a reason
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