What are the differences between numpy arrays and matrices? Which one should I use?

python

numpy

arrays

matrices

byAnton Shumikhin·Mar 10, 2025

Opt for numpy arrays in lieu of matrices for greater flexibility and wider applicability. Arrays support any number of dimensions, matrices are 2D-exclusive. Matrix multiplication? No problem, use np.dot(a, b) or a @ b with arrays:

import numpy as np
a, b = np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])
result = a @ b  # This is the 'at' symbol really pulling its weight 😜

Numpy arrays are conventionally standard, matrices are increasingly legacy—better steer clear for an unblemished future coding.

A detailed exposé on Arrays vs Matrices

numpy arrays (ndarray) are N-dimensional, suitable for catering diverse computational needs. They are your go-to for handling multidimensional data and its associated operations. Except for specific operators like @, ndarray ensures element-wise operation execution.

numpy matrices, although syntactically comfortable for matrix operations—* for matrix multiplication, lack the dynamic nature of ndarrays. They are strictly 2D-bound and their usage might result in complications because of backward compatibility issues.

Visual toolkit: Numpy workshops

Assume you're picking tools for a task from a toolkit:

Utility	Attribute	Mapping
🔨 Hammer	Easy-to-use, versatile	numpy array
🗡️ Scalpel	Specialized, specific	numpy matrix

Numpy Array (Hammer 🔨): Jack of all trades

🧱 Accommodates multi-dimensions
🔄 Extensively used in the NumPy milieu

Numpy Matrix (Scalpel 🗡️): Niche player

✂️ Fine-tuned for matrix operations
📉 Being phased out, exercise caution!

Arrays: A more dynamic choice

While matrices provide a neat syntax for select matrix operations, arrays rule the roost in advanced operations and data architectures. Be it neural networks, signal processing, or other advanced analysis, arrays support N-dimensional data and maintain the structure even during reduce operations.

The Scipy library works smoothly with arrays. For example, scipy.sparse matrices behave like numpy matrices—optimized for memory and multiplication. Libraries like Scipy expect ndarray inputs, further standardizing the use of arrays in Machine Learning.

Dodging compatibility issues

"Mixing oil and water" rings true for numpy matrices and arrays. Using them together could lead to unexpected behavior and errors. So, ensure you stick to one type—preferably arrays. If you receive data as a matrix, use np.asarray to convert it to array, ensuring your code behaves as expected.

Moving from matrix to array merely requires changing functions. Use .T for transpose, np.linalg.inv for inverse, and np.linalg.det for determinant, to keep your math in check while adhering to numpy standards.

Times when arrays have your back

Here are some practical scenarios where numpy's ndarray flexes its multidimensional muscle:

Vector operations, simplified

Arrays handle standard and row/column vector operations with ease. Not bound by 2D, arrays can broadcast operations across higher dimensions—a boon in complex system modeling and scientific computing.

# Broadcasting in action, adding a vector to all rows—no np.tile needed
X = np.array([[1, 2], [3, 4], [5, 6]]) 
v = np.array([1, 1])
result = X + v  # New array, who dis?

Complex array algebra

Multidimensional data structures and algebraic manipulations are onde arrays shine. They are essential for image processing and machine learning, as these fields inherently deal with data in multiple dimensions.

# Multidimensional arrays for image RGB layers—like three peas in a pod
image = np.zeros((1920, 1080, 3))

API consistency

Numpy methods largely return arrays, ensuring code continuity when arrays are predominant. It also streamlines the programming paradigm as arrays provide a clear, systematic approach to data manipulations.