5. Introduction to NumPy#
NumPy, short for Numerical Python, stands as a robust and influential Python library dedicated to numerical computing. It holds a central role in scientific computing within the Python ecosystem, offering extensive support for handling large, multi-dimensional arrays and matrices. The library boasts an array of mathematical functions, adeptly designed to efficiently operate on these arrays. NumPy has established itself as a cornerstone in various fields, including data analysis, machine learning, image processing, and scientific computations [Harris et al., 2020, NumPy Developers, 2023].
At its core, NumPy provides a versatile multidimensional array object, alongside several derived objects such as masked arrays and matrices. Along with this, it offers a vast array of routines specifically optimized for swift operations on arrays. These routines encompass a broad spectrum of functionalities, encompassing mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and much more. Overall, NumPy serves as an indispensable tool for a wide range of numerical computations and data manipulation tasks in Python.