Numpy Matrix Basics: Introduction to Multiplication, Transposition, and Inverse Matrix

This article introduces basic Numpy matrix operations, suitable for beginners to get started quickly. The core of Numpy is `ndarray`, created using `np.array`. Basic attributes include `shape` (number of rows and columns), `ndim` (dimension), and `dtype` (data type). Three core operations: 1. **Multiplication**: Distinguish between element-wise multiplication (`*`, requiring identical shapes) and matrix dot product (`np.dot`/`@`, where the number of columns of the first matrix equals the number of rows of the second matrix, resulting in a shape of `m×p`). 2. **Transposition**: Achieved using `.T` to swap rows and columns, suitable for adjusting shapes to fit operations. 3. **Inverse Matrix**: Exists only for square matrices with non-zero determinants, calculated using `np.linalg.inv`. Verification is done with `np.allclose` to check if it is the identity matrix. After mastering the basics, more complex operations can be advanced. Numpy requires more practice to improve proficiency.