5 best JavaScript multidimensional array libraries
Tags: javascript, webdev
Published: 2023-12-05 15:16:46

**Written by [Pascal Akunne](https://blog.logrocket.com/author/pascalakunne/)✏️**

JavaScript, with its versatility and widespread use, is a language that can be applied to a wide range of tasks. When it comes to dealing with multidimensional arrays, JavaScript lacks the built-in capability that other programming languages provide. 

However, owing to a vibrant ecosystem and a growing development community, there are various libraries available to assist you in effectively managing and manipulating multidimensional arrays. In this article, we'll explore the five best JavaScript multidimensional array libraries. Let’s dive right in.

## What is a multidimensional array?

A multidimensional array is a programming data structure that organizes elements in several rows and columns or dimensions, forming a grid or matrix-like structure. A multidimensional array enables the efficient storing and processing of data in a tabular or grid-like format. 

A two-dimensional array, for example, is similar to a table with rows and columns, but a three-dimensional array is similar to a cube with layers. Multidimensional arrays are widely utilized in a variety of applications, including numerical computation, image processing, and displaying structured data, making them an invaluable programming tool.

## The 5 best JavaScript libraries for multidimensional arrays

### ndarray

![ndarray](https://blog.logrocket.com/wp-content/uploads/2023/11/ndarray-multi-dimensional-array-library.png) [ndarray](https://github.com/scijs/ndarray) is a robust JavaScript library created exclusively for manipulating n-dimensional arrays. This library is a fantastic choice if your project requires numerical computation. It is useful for scientific and engineering applications because it supports a wide variety of array manipulation and mathematical operations. 

ndarray allows you to easily construct, modify, and execute operations on arrays of any dimension. 

Here is a code sample that uses ndarray:

```javascript
const ndarray = require('ndarray');

// Creating a 2D array
const data = ndarray(new Float64Array([1, 2, 3, 4, 5, 6]), [2, 3]);

console.log(data.get(0, 1)); // Output: 2

// Performing operations
data.add(10); // Add 10 to all elements

// Printing the updated array
console.log(data.data); // Output: Float64Array [ 11, 12, 13, 14, 15, 16 ]
```

Because of its low-level array manipulation, ndarray is particularly efficient for numerical operations. It is optimized for mathematical computations, making it a performant choice for scientific computing.

#### **ndarray use cases**

*   Numerical simulations, scientific computing, and data analysis
*   Projects that need efficient element-wise operations on multidimensional data

#### When/why it's suitable

*   Use ndarray when you need to perform extensive mathematical operations on big multidimensional datasets
*   Maps and Sets are more suited for associative data structures, but ndarray is better suited for numerical operations such as image processing and scientific simulation

### math.js

![Math.js](https://blog.logrocket.com/wp-content/uploads/2023/11/math-js-multi-dimensional-array-library.png) [math.js](https://mathjs.org/) is a comprehensive JavaScript library that offers support for working with matrices and multidimensional arrays. It contains a huge array of mathematical functions in addition to array operations, making it suitable for a wide range of mathematical activities. 

One of the benefits of math.js is its simplicity. This library will let you conduct fundamental array operations as well as complicated mathematical computations. It is commonly utilized in areas like physics, engineering, and data analysis. 

Here is a code sample using math.js:

```javascript
const math = require('mathjs');

// Creating a 2x2 matrix
const matrix = math.matrix([[1, 2], [3, 4]]);

// Matrix operations
const result = math.multiply(matrix, 2); // Multiply by 2

// Printing the result
console.log(result.toArray()); // Output: [ [ 2, 4 ], [ 6, 8 ] ]
```

math.js provides a wide range of mathematical functions, but its performance may not match low-level libraries like ndarray. And although it is suitable for common mathematical problems, math.js may not be the fastest option for huge datasets.

#### math.js use cases

*   General-purpose mathematics, symbolic calculations, and applications that do not require high performance such as math lessons and quizzes
*   Cases in which usability and a large variety of mathematical operations are more essential than sheer performance, such as business analytic dashboards

#### When/why it's suitable

*   Choose math.js for a comprehensive math library when performance is not a priority
*   This is a good choice if you require an easy-to-use library for a variety of mathematical activities

### NumJs

![NumJs](https://blog.logrocket.com/wp-content/uploads/2023/11/numjs-multi-dimensional-array-library.png) [NumJ](https://github.com/nicolaspanel/numjs)[s](https://github.com/nicolaspanel/numjs) is a JavaScript library inspired by Python's NumPy. NumJs, while not as comprehensive as some of the other libraries on this list, supports multidimensional arrays and contains fundamental mathematical operations and functions. 

If you're already familiar with NumPy, the shift to NumJs should be pretty simple. It's a good choice for applications that only need simple array operations and numeric computations:

```javascript
const nj = require('numjs');

// Creating a 3x3 array
const arr = nj.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]);

// Array operations
const sum = arr.sum(); // Sum of all elements

// Printing the result
console.log(sum); // Output: 45
```

NumJs is appropriate for simple array operations and numeric uses. And while it is not as well optimized as some other libraries, it offers a lightweight solution for fundamental use cases.

#### NumJs use cases

*   Projects that require basic numeric array operations and where simplicity is a priority
*   Educational reasons and situations in which lightweight libraries are preferable

#### When/why it's suitable

*   Consider NumJs for small to medium-sized projects that require simple numeric data processing such as fitness tracking or a grade calculator

### Lodash

![Lodash](https://blog.logrocket.com/wp-content/uploads/2023/11/lodash-multi-dimensional-array-library.png?w=300) [Lodash](https://lodash.com/) is a popular utility library that provides a wide range of methods for dealing with arrays, collections, and objects. Lodash, while not expressly built for multidimensional arrays, may be a useful tool for fundamental array operations. 

Lodash can be a decent option if your project includes simple array operations and you're seeking a lightweight solution. It is well-known for its speed and efficiency:

```javascript
const _ = require('lodash');

// Creating a 2D array
const matrix = [[1, 2], [3, 4], [5, 6]];

// Flattening the array
const flatArray = _.flatten(matrix);

// Printing the flattened array
console.log(flatArray); // Output: [ 1, 2, 3, 4, 5, 6 ]
```

While it is not designed for numerical workloads, Lodash is extremely fast for general array and object operations.

#### Lodash use cases

*   General-purpose utility functions, arrays, and object manipulations
*   Use cases in which basic data structure operations are more important than mathematical or quantitative duties such as inventory management system

#### When/why it's suitable

*   When you require a utility library for a variety of activities involving arrays, objects, and collections
*   Because of its speed, Lodash is a good choice for non-mathematical operations such as text processing and data manipulation

### TensorFlow.js

![TensorFlow.js](https://blog.logrocket.com/wp-content/uploads/2023/11/tensorflow-js-multi-dimensional-array-library.png) [TensorFlow.js](https://www.tensorflow.org/js) is a must-have library for individuals interested in machine learning and deep learning. While it primarily focuses on neural networks, it inherently involves working with multidimensional arrays, often referred to as tensors, for training and inference. 

TensorFlow.js is a good solution for web-based AI applications because it allows you to design and train machine learning models in JavaScript:

```javascript
const tf = require('@tensorflow/tfjs-node');

// Creating a 2x2 tensor
const tensor = tf.tensor([[1, 2], [3, 4]]);

// Tensor operations
const multiplied = tensor.mul(2); // Multiply by 2

// Printing the result
multiplied.print();
```

TensorFlow.js performs well for neural network tasks but may be overkill for other applications.

#### TensorFlow.js use cases

*   Projects with which to consider using TensorFlow.js include deep learning, neural network training and inference, and machine learning applications
*   Use when you need to leverage JavaScript for AI and deep learning tasks such as speech recognition in voice assistants

#### When/why it's suitable

*   TensorFlow.js is the finest choice for developing and training machine learning models, particularly deep neural networks
*   For general-purpose data structures or non-AI tasks like task management applications and user authentication systems, other libraries or data structures like Maps and Sets might be more appropriate

## Conclusion

When dealing with multidimensional arrays in JavaScript, the library you use is determined by the complexity of your project and your individual needs. There is a JavaScript library to meet your demands, whether you want complex numerical computations, basic array operations, or even deep learning capabilities. 

These five libraries cover a wide range of use cases while offering the tools required to work successfully with multidimensional arrays in JavaScript. Depending on the complexity of your project, select the one that best meets your needs and begin working with multidimensional arrays in JavaScript like a pro.

---

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