stdlib

✨ Standard library for JavaScript and Node.js. ✨

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Quick Overview

stdlib is a comprehensive standard library for JavaScript and Node.js. It provides a wide range of mathematical and statistical functions, data structures, and utilities, aiming to be the "standard library" that JavaScript lacks compared to other programming languages.

Pros

Extensive collection of mathematical and statistical functions
Well-documented and thoroughly tested codebase
Supports both browser and Node.js environments
Modular architecture allowing for selective use of functions

Cons

Large package size due to its comprehensive nature
May have a steeper learning curve for beginners due to its breadth
Some functions might be overkill for simple projects
Ongoing development may lead to occasional breaking changes

Code Examples

Calculating the mean of an array:

const stdlib = require('@stdlib/stdlib');

const data = [1, 2, 3, 4, 5];
const mean = stdlib.stats.mean(data);
console.log(mean); // Output: 3

Generating random numbers from a normal distribution:

const stdlib = require('@stdlib/stdlib');

const rng = stdlib.random.normal(0, 1);
const randomValues = Array.from({ length: 5 }, () => rng());
console.log(randomValues);
// Output: [0.123, -0.456, 0.789, -1.234, 0.567] (example values)

Performing matrix operations:

const stdlib = require('@stdlib/stdlib');

const matA = [[1, 2], [3, 4]];
const matB = [[5, 6], [7, 8]];
const result = stdlib.linalg.matmul(matA, matB);
console.log(result);
// Output: [[19, 22], [43, 50]]

Getting Started

To use stdlib in your project, follow these steps:

Install the package:
```
npm install @stdlib/stdlib
```

Import the library in your JavaScript file:

const stdlib = require('@stdlib/stdlib');

Start using the functions:

const data = [1, 2, 3, 4, 5];
const mean = stdlib.stats.mean(data);
console.log(`The mean is: ${mean}`);

Competitor Comparisons

numpy

27,792

The fundamental package for scientific computing with Python.

Pros of NumPy

Extensive ecosystem and widespread adoption in the scientific Python community
Highly optimized C implementations for numerical operations
Rich documentation and extensive tutorials

Cons of NumPy

Limited to Python programming language
Larger memory footprint compared to pure Python lists
Steeper learning curve for beginners

Code Comparison

NumPy:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
mean = np.mean(arr)

stdlib:

const { mean } = require('@stdlib/stats-base-mean');

const arr = [1, 2, 3, 4, 5];
const result = mean(arr);

Key Differences

NumPy is Python-specific, while stdlib is JavaScript-based
NumPy focuses on numerical computing, while stdlib aims to be a more general-purpose library
NumPy has a more mature ecosystem and wider adoption in scientific computing
stdlib provides a broader range of mathematical and statistical functions
NumPy's array operations are generally faster due to its C implementation

Use Cases

NumPy is ideal for:

Scientific computing and data analysis in Python
Machine learning and AI applications
Image and signal processing

stdlib is suitable for:

JavaScript-based data analysis and statistics
Web applications requiring mathematical computations
Node.js backend services with numerical processing needs

scipy

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SciPy library main repository

Pros of SciPy

Mature and widely adopted in the scientific Python community
Extensive documentation and large user base for support
Optimized for numerical computations and scientific applications

Cons of SciPy

Limited to Python ecosystem, not suitable for JavaScript projects
Can be complex to set up and use for beginners
Larger package size compared to stdlib

Code Comparison

SciPy (Python):

from scipy import stats
import numpy as np

data = np.random.normal(0, 1, 1000)
ks_statistic, p_value = stats.kstest(data, 'norm')

stdlib (JavaScript):

const { kstest } = require('@stdlib/stats-kstest');
const { random } = require('@stdlib/random-base-normal');

const data = new Array(1000).fill(0).map(() => random(0, 1));
const [ks_statistic, p_value] = kstest(data, 'normal');

Both libraries provide similar functionality for statistical tests, but SciPy is more tightly integrated with NumPy and the Python scientific ecosystem, while stdlib offers a JavaScript-native solution for statistical computations.

sympy

12,879

A computer algebra system written in pure Python

Pros of SymPy

Comprehensive symbolic mathematics library with advanced algebraic capabilities
Extensive documentation and a large, active community
Seamless integration with other scientific Python libraries (NumPy, SciPy, etc.)

Cons of SymPy

Limited to Python ecosystem, not as versatile across different programming languages
May have slower performance for certain numerical computations compared to specialized libraries

Code Comparison

SymPy example:

from sympy import symbols, expand
x, y = symbols('x y')
expr = expand((x + y)**3)
print(expr)

stdlib example:

const stdlib = require('@stdlib/stdlib');
const pow = stdlib.base.special.pow;
const x = 2;
const y = 3;
console.log(pow(x + y, 3));

Summary

SymPy is a powerful symbolic mathematics library for Python, offering advanced algebraic capabilities and integration with the scientific Python ecosystem. stdlib is a more general-purpose JavaScript library with a focus on numerical computations and statistics. While SymPy excels in symbolic mathematics, stdlib provides a broader range of mathematical and statistical functions across multiple programming languages. The choice between them depends on the specific requirements of your project and the programming language you prefer to work with.

pandas

43,524

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Pros of pandas

Extensive data manipulation and analysis capabilities for Python
Well-established ecosystem with extensive documentation and community support
Optimized for performance with C-based implementations

Cons of pandas

Limited to Python programming language
Can have a steeper learning curve for beginners
Memory-intensive for large datasets

Code Comparison

pandas:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = df.groupby('A').sum()

stdlib:

const stdlib = require('@stdlib/stdlib');

const arr = [1, 2, 3, 4, 5];
const mean = stdlib.stats.mean(arr);

Key Differences

pandas is Python-specific, while stdlib is JavaScript-focused
pandas specializes in data manipulation and analysis, stdlib offers a broader range of mathematical and statistical functions
pandas has a more extensive ecosystem and community support
stdlib aims to be a comprehensive standard library for JavaScript, covering various domains beyond data analysis

Use Cases

Choose pandas for data analysis and manipulation tasks in Python
Opt for stdlib when working with JavaScript and requiring a wide range of mathematical and statistical functions

scikit-learn

59,384

scikit-learn: machine learning in Python

Pros of scikit-learn

Extensive collection of machine learning algorithms and tools
Well-documented with comprehensive tutorials and examples
Strong community support and regular updates

Cons of scikit-learn

Limited to Python programming language
Focused primarily on machine learning, less versatile for general-purpose computing

Code Comparison

scikit-learn (Python):

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

stdlib (JavaScript):

const stdlib = require('@stdlib/stdlib');
const lr = stdlib.ml.logistic_regression();
lr.fit(X_train, y_train);
const predictions = lr.predict(X_test);

Key Differences

Language: scikit-learn is Python-based, while stdlib is JavaScript-based
Scope: scikit-learn focuses on machine learning, stdlib aims to be a comprehensive standard library
Community: scikit-learn has a larger, more established community
Documentation: scikit-learn offers more extensive documentation and examples

Use Cases

scikit-learn: Ideal for data scientists and machine learning practitioners working in Python
stdlib: Suitable for JavaScript developers looking for a comprehensive library for various computational tasks

statsmodels

10,062

Statsmodels: statistical modeling and econometrics in Python

Pros of statsmodels

Comprehensive statistical modeling and econometrics library in Python
Extensive documentation and examples for various statistical methods
Strong integration with other scientific Python libraries (NumPy, pandas, etc.)

Cons of statsmodels

Limited to Python ecosystem, not as versatile across programming languages
May have a steeper learning curve for users new to statistical modeling
Less focus on general-purpose mathematical functions compared to stdlib

Code Comparison

statsmodels:

import statsmodels.api as sm
X = [[1, 1], [1, 2], [1, 3]]
y = [1, 2, 3]
model = sm.OLS(y, X).fit()
print(model.summary())

stdlib:

const stdlib = require('@stdlib/stdlib');
const ols = stdlib.stats.ols;
const X = [[1, 1], [1, 2], [1, 3]];
const y = [1, 2, 3];
const results = ols(y, X);
console.log(results);

Both libraries provide functionality for linear regression, but statsmodels offers a more comprehensive summary output, while stdlib focuses on a broader range of mathematical and statistical functions across multiple programming languages.

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README

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib.

stdlib (/ËstÃ¦ndÉrd lÉªb/ "standard lib") is a standard library with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js. The library provides a collection of robust, high performance libraries for mathematics, statistics, data processing, streams, and more and includes many of the utilities you would expect from a standard library.

What sets stdlib apart is its fully decomposable architecture, which allows you to swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be confident that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code available.

Want to join us in bringing numerical computing to the web? Start by starring the project. :star2:

Explore this GitHub repository for stdlib's source code and documentation. For guidance on developing stdlib, refer to the development guide.

Thank you for being a part of our community! Your support is invaluable to us!

Resources

External Resources

Features

150+ special math functions.
35+ probability distributions, with support for evaluating probability density functions (PDFs), cumulative distribution functions (CDFs), quantiles, moments, and more.
40+ seedable pseudorandom number generators (PRNGs).
200+ general utilities for data transformation, functional programming, and asynchronous control flow.
200+ assertion utilities for data validation and feature detection.
50+ sample datasets for testing and development.
A plot API for data visualization and exploratory data analysis.
Native add-ons for interfacing with BLAS libraries, with pure JavaScript fallbacks.
A benchmark framework supporting TAP.
REPL environment with integrated help and examples.
Can be bundled using Browserify, Webpack, and other bundlers for use in web browsers.
Every function is accompanied by TypeScript declaration files, ensuring type safety and facilitating intelligent code completion in IDEs.

Installation

To accommodate various use cases, stdlib can be used in multiple ways. The preferred method of use depends on your individual use case. We've provided some user stories to help you identify the best approach. ð

While this project's installation instructions defaults to using npm for package management, installation via other package managers, such as yarn, should be a matter of simply swapping out npm commands with those of the relevant package manager.

User Stories

I want to perform data analysis and data science tasks in JavaScript and Node.js, similar to how I might use Python, Julia, R, and MATLAB.
- Install the entire project as a command-line utility.
I am building a web application.
- I plan on using Browserify, Webpack, and other bundlers for use in web browsers.
  - Install individual packages. Installing the entire project is likely unnecessary and will lead to slower installation times.
- I would like to vendor a custom bundle containing various stdlib functionality.
  - Follow the steps for creating custom bundles.
- I would like to include stdlib functionality by just using a script tag.
  - I would like to use ES Modules.
    - Use an individual package's ES Module build.
  - I would like to use a pre-built bundle (possibly via a CDN, such as unpkg or jsDelivr).
    - Install (or consume via a CDN) an individual package's pre-built UMD browser bundle.
- I am interested in using a substantial amount of functionality found in a top-level stdlib namespace and don't want to separately install hundreds of individual packages (e.g., if building an on-line calculator application and wanting all of stdlib's math functionality).
  - Install one or more top-level namespaces. Installing the entire project is likely unnecessary and will lead to slower installation times. Installing a top-level namespace is likely to mean installing functionality which will never be used; however, installing a top-level namespace is likely to be easier and less time-consuming than installing many individual packages separately.
    
    When bundling, installing a top-level namespace should not be a concern, as individual functionality can still be independently required/imported. Project installation times may, however, be somewhat slower.
I am building a Node.js server application.
- I am interested in using various functionality found in stdlib.
  - Install individual packages. Installing the entire project is likely unnecessary and will lead to slower installation times.
- I would like to vendor stdlib functionality and avoid dependency trees.
  - Install individual package UMD bundles.
- I am interested in using a substantial amount of functionality found in a top-level stdlib namespace and don't want to separately install hundreds of individual packages.
  - Install one or more top-level namespaces. Installing the entire project is likely unnecessary and will lead to slower installation times. Installing a top-level namespace is likely to mean installing functionality which will never be used; however, installing a top-level namespace is likely to be easier and less time-consuming than installing many individual packages separately.
I am using Deno.
- Import individual packages using pre-built Deno builds.
I would like to use stdlib functionality in an Observable notebook.
- Consume a pre-built browser bundles via a CDN, such as unpkg or jsDelivr.
I want to hack at stdlib, possibly even creating customized builds to link to platform-specific native libraries (such as Intel's MKL or some other numerical library).
- Install the project as a system library by cloning this repository and following the installation instructions as described in the development guide.

Complete Library

To install the entire project as a library or application dependency,

$ npm install @stdlib/stdlib

Once installed, stdlib packages can be individually required/imported to minimize load times and decrease bundle sizes. For example, to use require

var ndarray = require( '@stdlib/ndarray/array' );

var arr = ndarray( [ [ 1, 2 ], [ 3, 4 ] ] );
// returns <ndarray>

and to use import

import ndarray from '@stdlib/ndarray/array';

var arr = ndarray( [ [ 1, 2 ], [ 3, 4 ] ] );
// returns <ndarray>

Individual Packages

stdlib is designed to allow decomposition of the main project into individual packages which can be independently consumed. Accordingly, users of the project can avoid installing all project functionality and only install the exact functionality they need.

To install individual packages, replace forward slashes / after @stdlib/ with hyphens -. For example,

$ npm install @stdlib/ndarray-array

Once installed, individual packages can be required/imported. For example, to use require

var ndarray = require( '@stdlib/ndarray-array' );

var arr = ndarray( [ [ 1, 2 ], [ 3, 4 ] ] );
// returns <ndarray>

and to use import

import ndarray from '@stdlib/ndarray-array';

var arr = ndarray( [ [ 1, 2 ], [ 3, 4 ] ] );
// returns <ndarray>

Namespaces

stdlib is comprised of various top-level namespaces (i.e., collections of related functionality united by common themes). For example, to install all math functionality found in the top-level math namespace,

$ npm install @stdlib/math

Once installed, packages within a top-level namespace can be individually required/imported to minimize load times and decrease bundle sizes. For example, to use require

var sin = require( '@stdlib/math/base/special/sin' );

var v = sin( 3.14 );
// returns <number>

and to use import

import sin from '@stdlib/math/base/special/sin';

var v = sin( 3.14 );
// returns <number>

Note: installing nested namespaces found within top-level namespaces (e.g., math/base) is not supported. Consider installing individual packages or the relevant top-level namespace.

Command-line Utility

To install globally for use as a command-line utility and/or use the REPL,

$ npm install -g @stdlib/stdlib

which will expose the stdlib command. For example, to see available sub-commands

$ stdlib help

and to run the REPL

$ stdlib repl

Environment Builds

ES Modules

To use ES Modules via a <script> tag, use ES Module builds available in each package's repository via a dedicated esm branch (e.g., see the esm branch for @stdlib/math-base-special-erf). For example,

<script type="module">
import linspace from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-base-linspace@esm/index.mjs';
import erf from 'https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-erf@esm/index.mjs';

const x = linspace( -10.0, 10.0, 100 );

for ( let i = 0; i < x.length; i++ ) {
    console.log( 'x: %d, erf(x): %d', x[ i ], erf( x[ i ] ) );
}
</script>

Deno

To use individual packages in Deno, use Deno builds available in each package's repository via a dedicated deno branch (e.g., see the deno branch for @stdlib/ndarray-array). For example,

import ndarray from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-array@deno/mod.js';

var arr = ndarray( [ [ 1, 2 ], [ 3, 4 ] ] );
// returns <ndarray>

jQuery-like Bundle

For those wanting a jQuery-like bundle, one can use pre-built distributable UMD bundles for use in browser environments or as shared ("vendored") libraries in server environments available in each package's repository via a dedicated umd branch. See sections UMD and Node.js for more details.

UMD

To use UMD bundles either via a <script> tag or in Observable, use UMD browser builds available in each package's repository via a dedicated umd branch (e.g., see the umd branch for @stdlib/math-base-special-erf). For example,

<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/array-base-linspace@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-erf@umd/browser.js"></script>
<script type="text/javascript">
(function () {

var x = linspace( -10.0, 10.0, 100 );

for ( var i = 0; i < x.length; i++ ) {
    console.log( 'x: %d, erf(x): %d', x[ i ], erf( x[ i ] ) );
}

})();
</script>

Node.js

To vendor stdlib functionality and avoid installing dependency trees, use UMD server builds available in each package's repository via a dedicated umd branch (e.g., see the umd branch for @stdlib/math-base-special-erf). For example,

var linspace = require( '/path/to/vendor/umd/@stdlib/array-base-linspace' );
var erf = require( '/path/to/vendor/umd/@stdlib/math-base-special-erf' );

var x = linspace( -10.0, 10.0, 100 );

for ( var i = 0; i < x.length; i++ ) {
    console.log( 'x: %d, erf(x): %d', x[ i ], erf( x[ i ] ) );
}

Custom Bundles

To create a custom bundle based on project needs,

follow the download, configuration, and installation instructions as described in the development guide.
navigate to the local installation directory.
run the following command to print help documentation for providing a list of stdlib package names to bundle
```
$ NODE_PATH=./lib/node_modules node ./bin/cli bundle-pkg-list -- -h
```

modify and run the above command with the list of packages to bundle

$ NODE_PATH=./lib/node_modules node ./bin/cli bundle-pkg-list -- <pkg> <pkg> <pkg> ...

Upon generating a bundle, the bundle can be loaded via a <script> tag as described above for pre-built distributable UMD bundles.

System Library

To install as a system library (e.g., for the purposes of creating custom builds), follow the download, configuration, and installation instructions as described in the development guide.

Prerequisites

Installing and running stdlib for use in Node.js requires the following prerequisites:

Node.js: JavaScript runtime (version >= 0.10)
npm: package manager (version > 2.7.0; if Node < 1.0.0, version > 2.7.0 and < 4.0.0; if Node <= 10.x.x, version > 2.7.0 and < 6.0.0)

Most functionality in stdlib is implemented in JavaScript and no further prerequisites are required to use stdlib (i.e., you can safely avoid installing any additional prerequisites); however, some implementations try to capture performance benefits by using native bindings and/or WebAssembly. While not required to run stdlib, as every stdlib implementation has a JavaScript fallback, the following dependencies are required for building native add-ons, including linking to BLAS and LAPACK libraries:

GNU make: development utility and task runner
GNU bash: an sh-compatible shell
gcc & g++ or Clang: C/C++ compilation and linking (g++ version >= 4.8; clang version >= 3.5, Xcode version >=8.3.1 on OS X)
gfortran: Fortran compilation and linking (version >= 4.8)

While not required to run stdlib, the following dependencies are required for automatically downloading external libraries:

curl, wget, or fetch (FreeBSD): utilities for downloading remote resources

The following external libraries can be automatically downloaded and compiled from source using make:

OpenBLAS: optimized BLAS library
Electron: framework for cross-platform desktop applications

Contributing

First time contributor?

See the contributing guidelines.

Already an expert?

Fork the repository.

Clone the forked repository

$ git clone --depth=1 https://github.com/<username>/stdlib.git

where <username> is your GitHub username.

Navigate to the stdlib directory
```
$ cd stdlib
```
Install dependencies
```
$ make install-node-modules
```
Initialize your stdlib development environment
```
$ make init
```

Users

The following organizations and key stakeholders trust and rely on stdlib:

Does your organization use stdlib? If so, we'd love to hear from you!

Governance

For information about the governance of the stdlib project, see GOVERNANCE.md.

License

See LICENSE.

Copyright

Status

Version

Community

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of NumPy

Cons of NumPy

Code Comparison

Key Differences

Use Cases

Pros of SciPy

Cons of SciPy

Code Comparison

Pros of SymPy

Cons of SymPy

Code Comparison

Summary

Pros of pandas

Cons of pandas

Code Comparison

Key Differences

Use Cases

Pros of scikit-learn

Cons of scikit-learn

Code Comparison

Key Differences

Use Cases

Pros of statsmodels

Cons of statsmodels

Code Comparison

Convert designs to code with AI

README

Resources

External Resources

Features

Installation

User Stories

Complete Library

Individual Packages

Namespaces

Command-line Utility

Environment Builds

ES Modules

Deno

jQuery-like Bundle

UMD

Node.js

Custom Bundles

System Library

Prerequisites

Contributing

Sponsors

Users

Governance

License

Copyright

Status

Version

Community

Top Related Projects

Convert designs to code with AI

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