It provides efficient, fast operations on large multi-dimensional arrays and matrices. NumPy's vectorization avoids slow Python loops, leading to significant performance improvements. Libraries like Pandas, SciPy, Scikit-learn, TensorFlow, and PyTorch depend on NumPy arrays.
It includes powerful mathematical, statistical, and linear algebra operations critical for AI algorithms. NumPy facilitates data preprocessing, feature scaling, and numerical simulations. It supports broadcasting, masking, and advanced indexing to manipulate data effectively.
With NumPy, data scientists can prototype and test algorithms efficiently. Its integration with C, C++, and Fortran supports high-performance computing.
Understanding NumPy equips learners with the skills to handle real-world datasets and model inputs.