About OnlinePy

The Technology: Pyodide + WebAssembly

OnlinePy is powered by Pyodide, an ambitious project that compiles the entire CPython interpreter and scientific Python stack to WebAssembly (WASM). This means your Python code executes natively inside your browser — no backend server, no cloud container, no network round-trip.

We use Pyodide v0.29.3, leveraging the full power of SharedArrayBufferfor better multi-threading performance and OPFS (Origin Private File System) for efficient caching of Python packages. Once loaded, the environment is cached — allowing you to even run Python offline.

Quick Guide: Using NumPy in OnlinePy

Getting started with scientific Python is straightforward. Simply import the library — OnlinePy will automatically install it at runtime when needed.

import numpy as np

# Create an array
arr = np.array([1, 2, 3, 4, 5])

# Basic operations
print(f"Mean: {np.mean(arr)}")
print(f"Std: {np.std(arr)}")
print(f"Sum: {np.sum(arr)}")

# Matrix operations
matrix = np.array([[1, 2], [3, 4]])
print(f"\nDeterminant: {np.linalg.det(matrix)}")

💡 Tip: The first run may take a few seconds as NumPy loads into memory. Subsequent runs are instant.

Getting Started Tutorial

import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'score': [95, 87, 92]
})
print(df.describe())

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
plt.plot(x, np.sin(x))
plt.title("Sine Wave")
plt.show()