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*numpy matrix operations cheat sheet: a quick guide*

numpy, the fundamental package for scientific computing in python, offers a powerful array object and numerous tools for matrix operations. this cheat sheet serves as a handy reference for efficient matrix manipulations.

*creating arrays*
utilize functions like `numpy.array()`, `numpy.zeros()`, and `numpy.ones()` to create matrices of various dimensions. initialize matrices with specific values or default to zeros or ones for ease of calculations.

*basic operations*
perform element-wise addition, subtraction, and multiplication easily. numpy supports broadcasting, allowing operations on arrays of different shapes.

*matrix multiplication*
use `numpy.dot()` or the `@` operator for matrix multiplication, ensuring proper alignment of dimensions.

*transposing matrices*
transpose a matrix using the `.t` attribute, which flips the rows and columns, facilitating various linear algebra applications.

*matrix inversion*
calculate the inverse of a matrix with `numpy.linalg.inv()`, provided the matrix is square and non-singular.

*determinants and eigenvalues*
determine matrix properties with `numpy.linalg.det()` for determinants and `numpy.linalg.eig()` for eigenvalues and eigenvectors.

*reshaping and slicing*
effortlessly reshape matrices using `.reshape()` and slice them for specific data extraction.

this numpy matrix operations cheat sheet is an essential tool for data scientists and engineers, providing quick access to vital functions for efficient matrix manipulations and calculations. keep it handy to boost your productivity and enhance your data analysis skills!
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