Getting Started with Numpy from Scratch: From Array Creation to Basic Operations

NumPy is a core library for numerical computing in Python, providing high-performance multidimensional arrays and computational tools, suitable for scenarios such as data science and machine learning. Installation is done via `pip install numpy`, with the import typically abbreviated as `np`. Arrays can be created in various ways: from Python lists, using `np.zeros`/`ones` (arrays of all zeros/ones), `arange` (arithmetic sequences), `linspace` (uniformly distributed values), and `np.random` (random arrays). Array attributes include `shape` (dimensions), `ndim` (number of dimensions), `dtype` (data type), and `size` (total number of elements). Indexing and slicing are flexible: one-dimensional arrays behave like lists, while two-dimensional arrays use row and column indices, with support for boolean filtering (e.g., `arr[arr>3]`). Basic operations are efficient, including element-wise arithmetic (+, *, etc.), matrix multiplication (via `dot` or `@`), and the broadcasting mechanism (e.g., automatic expansion for array-scalar operations). Application examples include statistical analysis (using functions like `sum` and `mean`) and data filtering. Mastering these capabilities enables efficient numerical data processing and lays the foundation for advanced functionalities such as linear algebra.