DEV Community: Emma Watson

How I Automated Product Schema Markup for Thousands of E-Commerce Products

Emma Watson — Fri, 05 Jun 2026 06:54:46 +0000

I've been optimizing a large e-commerce site and needed to generate Product schema markup for thousands of items. Doing this manually was impossible, so I built a pipeline that uses the SERPspur schema generator.

Here's the core logic:

javascript
const axios = require('axios');

async function generateProductSchema(product) {
const response = await axios.post
type: 'Product',
name: product.name,
description: product.description,
sku: product.sku,
offers: {
price: product.price,
priceCurrency: 'USD',
availability:
}
});
return response.data;}

// Usage
const products = await fetchAllProducts();
const schemas = await Promise.all(products.map(generateProductSchema);

The key is that the API ensures all required fields are present and formats the output correctly. For example, it automatically wraps offers in the proper structure and adds

Google personalizes results based on your IP

Emma Watson — Mon, 01 Jun 2026 17:14:13 +0000

Ever noticed how your search results look completely different when you use a VPN? That's because Google personalizes results based on your IP, not just your query. This is a nightmare for SEO pros trying to audit local visibility.

I built a Python script that bypasses this by sending requests with custom headers and geolocation data. Here's a simplified version:

python
import requests

headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
'Accept-Language': 'en-US,en;q=0.9',
'X-Forwarded-For': '8.8.8.8' # spoof IP
}

params = {
'q': 'coffee shop',
'gl': 'us', # country
'hl': 'en', # language
'uule': 'w+CAIQICINVXNhIE5ldyBZb3Jr' # encoded location
}

response = requests.get('https://www.google.com/search', headers=headers, params=params)
print(response.text[:500]) # check if results are localized

This is great for one-off tests, but managing multiple locations and languages quickly becomes tedious. That's why I now use a dedicated tool that automates all this with a simple API call. It's been a game-changer for my international SEO workflow.

https://serpspur.com

Tracking Keyword Rankings with Python + CSV: A Lightweight SEO Experiment

Emma Watson — Fri, 29 May 2026 15:35:39 +0000

I've been experimenting with a lightweight approach to track keyword ranking changes using Python and a simple CSV file. Here's the gist:

python
import csv
import requests
from datetime import datetime

def check_ranking(keyword, url):
# Simplified ranking check - actual implementation would use search API
# This is just a placeholder
return {'keyword': keyword, 'url': url, 'rank': 5, 'date': datetime.now().isoformat()}

def update_tracker(csv_path, keywords):
with open(csv_path, 'a', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=['keyword', 'url', 'rank', 'date'])
if csvfile.tell() == 0:
writer.writeheader()
for keyword in keywords:
data = check_ranking(keyword['url'], keyword['url'])
writer.writerow(data)

keywords = [
{'keyword': 'python tutorial', 'url': 'https://example.com/python'},
{'keyword': 'web scraping', 'url': 'https://example.com/scraping'}
]
update_tracker('rankings.csv', keywords)

For serious rank tracking, I rely on SERPSpur's API which handles search result parsing and historical data. But this simple approach works for hobby projects. How do you track your keyword rankings?