Introduction
If you’ve ever worked with data in Python, you’ve likely encountered a library called NumPy. At its core, NumPy (short for Numerical Python) is the fundamental package for scientific computing in Python. While Python’s built-in lists are flexible and powerful, they are quite slow and inefficient when dealing with large, multi-dimensional datasets and complex mathematical operations.
NumPy solves this problem by providing a specialized data structure, the ndarray (n-dimensional array), which is designed for efficient numerical operations. Its speed, memory efficiency, and rich ecosystem of functions make it the undisputed foundation for data analysis, scientific computing, and machine learning in the Python world. It’s the foundation for almost all of the big data science tools you’ll hear about later, so understanding it is a crucial first step.