When Should I Choose Numpy Arrays Over Matrices?

Differences and Choice Between Numpy Arrays and Matrices

Numpy arrays and matrices are similar data structures in Python, but they offer distinct functionalities and characteristics. Here's an examination of their differences:

Dimensionality and Properties

• Numpy matrices: Exclusively two-dimensional.
• Numpy arrays (ndarrays): N-dimensional.
• Matrices inherit attributes and methods of ndarrays.

Matrix Multiplication Notation

• Matrices provide concise notation for matrix multiplication: a @ b where a and b are matrices.
• Ndarrays support infix matrix multiplication with the @ operator since Python 3.5.

Element-Wise vs. Matrix Operations

• Ndarrays consistently apply operations element-wise, except for matrix multiplication via np.dot or @.
• Matrices execute matrix operations by default, such as matrix multiplication when multiplying two matrices.

Mathematical Operators

• ** operator: In matrices, a**2 returns a*a, but in ndarrays, it squares each element.

Versatility and Consistency

• Numpy arrays: More general than matrices, allowing for any dimensionality.
• Numpy matrices: Provide convenient matrix operations, but limit dimensionality to two.
• Mixing matrices and ndarrays in a program can lead to unexpected results.

Recommendation for Use

If you solely work with two-dimensional matrices and prioritize visual simplicity in matrix multiplication notation, matrices may suit your needs. However, for versatility, consistency, and ease of use, choosing exclusively numpy arrays is recommended. This allows you to perform all matrix operations and more using ndarrays.

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