Pandas for JavaScript

Zero dependencies. Pandas-inspired API. 503 tests verified against Python.

$ npm install @rockiey/pandasjs

Get Started View on GitHub

Features

Zero Dependencies

Pure JavaScript implementation. No native modules, no heavy bundles. Just import and go.

Lightweight & Fast

Typically 1-3x faster than Python pandas on small data. Some operations are comparable. 503 tests verified.

Pandas-Inspired API

Familiar method names and patterns for Python pandas users. Not a full port, but covers common operations.

DataFrame & Series

DataFrame and Series with iloc, loc, groupby, merge, pivot, melt, and more.

Window & GroupBy

Rolling, expanding, and exponential weighted windows. Multi-column groupby with agg, transform, filter.

Run Python Code

Use pd.run() to transpile Python pandas code to JS and run it natively. No Pyodide needed.

Install

			$ npm install @rockiey/pandasjs

			// then in your code
			import pd from '@rockiey/pandasjs'
		

			<!-- add to your HTML -->
			<script src="https://cdn.jsdelivr.net/npm/@rockiey/pandasjs/dist/pandasjs.min.js"></script>

			// pd is now available globally
			const df = pd.DataFrame({a: [1,
			2, 3]})
		

			// direct ESM import from CDN
			import pd from 'https://cdn.jsdelivr.net/npm/@rockiey/pandasjs/dist/pandasjs.esm.js'
		

Quick Example

import pd from '@rockiey/pandasjs'
			
const df = pd.DataFrame({
name: ['Alice', 'Bob', 'Charlie',
				'Alice', 'Bob'],
			
score: [85, 92, 78, 95, 88],
grade: ['A', 'A', 'B', 'A', 'B']

			})

			// groupby + aggregation
const result = df.groupby('name').agg({score: ['mean', 'max']})
console.log(result.toString())
// rolling mean
const rolling = df.col('score').rolling(2).mean()
// transpile Python pandas code to JS and run
const pyDf = pd.run(`
					df = pd.DataFrame({'x': [1,2,3], 'y': [4,5,6]})
					df.describe()
					`)

Performance

503 tests verified against Python pandas. Timings measured on small test data — results vary by workload and environment.

Area	Python	JavaScript	Ratio
Creation & Access	0.72 ms	0.83 ms	0.9x
Selection & Indexing	1.68 ms	1.08 ms	1.6x
Computation	2.91 ms	1.34 ms	2.2x
Reshaping	5.40 ms	2.43 ms	2.2x
Advanced	5.70 ms	2.05 ms	2.8x
Window	2.12 ms	1.93 ms	1.1x
GroupBy & Merge	7.83 ms	2.05 ms	3.8x
IO & Data Types	4.42 ms	1.79 ms	2.5x
DateTime & Strings	3.64 ms	2.21 ms	1.6x
Utilities	4.24 ms	1.82 ms	2.3x
Arithmetic & Conversions	2.31 ms	2.04 ms	1.1x
Cumulative & Stats	2.81 ms	2.05 ms	1.4x
GroupBy Enhancements	2.03 ms	1.18 ms	1.7x
Reshape & Top-level	5.34 ms	2.03 ms	2.6x
String & Window Ext	1.98 ms	1.64 ms	1.2x