Is PyRun completely free to use?

Yes — PyRun is entirely free. There is no sign-up, no subscription, and no hidden limit. You can write and run Python code as many times as you like without creating an account.

Does PyRun work without an internet connection?

After the first load, PyRun continues to work offline. The Python engine (Pyodide) is cached locally by your browser, so you can keep coding even when you have no internet access. Some features like link sharing require a connection.

Which version of Python does PyRun use?

PyRun runs Python 3.11 via Pyodide, a port of CPython compiled to WebAssembly. You get access to the full Python standard library, including modules like math, json, datetime, itertools, and more.

Is my code private? Does PyRun store what I write?

Your code never leaves your device unless you explicitly generate a public share link. All execution happens entirely inside your browser sandbox. PyRun has no server that receives or stores your code.

Can I use PyRun on a phone or tablet?

Yes. PyRun is built with a responsive layout and works on mobile browsers. The interface adapts to smaller screens, switching to a stacked layout so both the editor and output terminal remain accessible.

Can I import and open my own Python files?

Absolutely. You can open any .py file from your device using the toolbar button or with the Ctrl+O keyboard shortcut. The file loads directly into the editor, ready to run.

How do I share my code with someone else?

Use the Share button in the toolbar or press Ctrl+H. PyRun generates a unique URL that encodes your code. Anyone with the link can open it in their browser and see (or run) the exact same code.

Does PyRun support the input() function?

Yes. PyRun has full support for Python's built-in input() function. When your code reaches an input() call, the terminal prompts you to type a response, just as it would in a local terminal.

Numpy Matrix Ops

Matrix multiplication and dot product.

Try Numpy Matrix Ops Code

How it Works

NumPy arrays are C-contiguous maps inside WASM.

Operations run natively using predefined LAPACK functions.

Source Code

Multiplication array pipeline using numpy internally.

matrix.py

Try in Editor

import numpy as np

# Create two 3x3 matrices
matrix_A = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])

matrix_B = np.array([
    [9, 8, 7],
    [6, 5, 4],
    [3, 2, 1]
])

print("Matrix A:")
print(matrix_A)
print("\nMatrix B:")
print(matrix_B)

# Matrix Multiplication (Dot Product)
result = np.dot(matrix_A, matrix_B)
print("\nResult of A * B:")
print(result)

# Transpose
print("\nTranspose of Result:")
print(result.T)

Terminal Output

Matrix A:
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Matrix B:
[[9 8 7]
 [6 5 4]
 [3 2 1]]

Result of A * B:
[[ 30  24  18]
 [ 84  69  54]
 [138 114  90]]

Transpose of Result:
[[ 30  84 138]
 [ 24  69 114]
 [ 18  54  90]]

Real-world Applications

Data processing
ML Pipeline math
AI tensor initialization

Frequently Asked Questions

Are C-Extensions supported?

Most standard data-science C-extensions are pre-compiled and packed within the Pyodide runtime core.