NumPy

NumPy is the foundational library for scientific computing in Python, providing multidimensional array objects, vectorized mathematical operations, and unified C/C++ API bindings.

When to Use

• High-Performance Numerical Computation: Vectorized array arithmetic and multi-dimensional tensor manipulation.
• Scientific Computing & Linear Algebra: Matrix decompositions (SVD, QR, Cholesky), eigenvalues, and inversions via np.linalg.
• Foundational Data Layer for AI/ML: Serving as the memory buffer standard for PyTorch, TensorFlow, OpenCV, and Scikit-learn.
• NumPy 2.0+ Modern Standard: Utilizing improved typing, standardized C-API, and optimized array constructors.

Quick Start

import numpy as np

# Vectorized matrix operations with broadcasting
a = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float64)
b = np.array([10, 20, 30])