Playwright vs NumPy Technology Comparison for Beginners

// login.spec.js
const { test, expect } = require('@playwright/test');

test('user can log in', async ({ page }) => {
  await page.goto('https://example.com/login');
  await page.fill('#user', 'test');
  await page.fill('#pass', 'secret');
  await page.click('#login');
  await expect(page).toHaveTitle(/Dashboard/);
});

# Playwright commands
npm init playwright@latest
npx playwright test
npx playwright test --headed
npx playwright test --ui
npx playwright codegen https://example.com

import numpy as np

# Create arrays
a = np.array([1, 2, 3, 4])
b = np.zeros((2, 3))
c = np.arange(0, 10, 2)

print(a.shape)   # (4,)
print(b.shape)   # (2, 3)

import numpy as np

x = np.array([1, 2, 3, 4])
y = np.array([10, 20, 30, 40])

print(x + y)         # element-wise
print(x * 2)         # scalar multiply
print(x.mean())      # 2.5
print(x.sum())       # 10
print(x.reshape(2, 2))

Quick comparison:
Playwright → browser automation, E2E tests
NumPy      → arrays, math, data processing
Both       → rarely used together
Not rivals → different layers of work

Beginner mistakes:
- Comparing them as alternatives
- Trying to test UI with NumPy
- Trying to compute arrays with Playwright
- Not learning Python basics before NumPy
- Not learning locators before Playwright

Testing workflow:
1. Write code + UI
2. Add Playwright tests
3. Run locally
4. Run in CI (headless)
5. Review reports
6. Ship with confidence

Data workflow:
1. Load data (pandas)
2. Convert to NumPy arrays
3. Clean and normalize
4. Compute statistics
5. Visualize (matplotlib)
6. Feed into model