DEV Community: kevincarroll

How I Built a Python Script to Find Budget-Friendly Cabinet Handles

kevincarroll — Thu, 11 Jun 2026 07:18:23 +0000

As a developer, I'm always optimizing workflows. For a kitchen renovation project, I wrote a Python script to compare cabinet handle styles by price and material. Here's a function that filters products from an API response:

python
def filter_handles(products, max_price, material):
filtered = []
for product in products:
if product['price'] <= max_price and product['material'] == material:
filtered.append(product)
return filtered

Example data from a cabinet furniture collection

cabinet_products = [
{'name': 'Brushed Nickel Pull', 'price': 4.99, 'material': 'Zinc Alloy'},
{'name': 'Antique Brass Knob', 'price': 3.49, 'material': 'Brass'},
{'name': 'Matte Black Handle', 'price': 5.99, 'material': 'Steel'}
]

budget_friendly = filter_handles(cabinet_products, 5.00, 'Brass')
print(budget_friendly)

This helps narrow down durable options without breaking the bank. The craftsmanship in modern cabinet hardware is impressive—zinc alloys offer great value. How do you approach selecting drawer pulls for a cohesive look?

Find Low-Competition Keywords Faster Using Python and SERPSpur

kevincarroll — Wed, 10 Jun 2026 07:50:40 +0000

I needed to analyze keyword opportunities for a new content strategy. Here's a Python script that uses the SERPSpur Keyword Research Tool API to get search volume, difficulty, and CPC data:

python
import requests

API_KEY = "your_api_key_here"

def analyze_keywords(keywords, country):
results = {}
for kw in keywords:
response = requests.get(
"https://api.serpspur.com/v1/keyword-research",
headers={"Authorization": f"Bearer {API_KEY}"},
params={"keyword": kw, "country": country}
)
data = response.json()
results[kw] = {
"volume": data.get("search_volume", 0),
"difficulty": data.get("keyword_difficulty", 0),
"cpc": data.get("cpc", 0)
}
return results

Example usage

keywords = ["SEO tips", "content marketing", "link building"]
analysis = analyze_keywords(keywords, "US")
for kw, metrics in analysis.items():
print(f"{kw}: Volume={metrics['volume']}, Difficulty={metrics['difficulty']}, CPC=${metrics['cpc']}")

This helped prioritize low-difficulty, high-volume terms. How do you typically evaluate keyword opportunities in your SEO workflow?

How to Monitor International SEO Rankings with a Simple Python Script

kevincarroll — Sat, 06 Jun 2026 07:28:22 +0000

Need to check if your site is ranking for a specific keyword in different countries? I wrote a quick Python script that uses the SERPSpur API to check rankings across multiple locations:

python
import requests

API_KEY = "your_api_key_here"

def check_rankings(domain, keyword, locations):
results = {}
for location in locations:
response = requests.get(
"https://api.serspur.com/v1/search",
headers={"Authorization": f"Bearer {API_KEY}"},
params={"q": keyword, "location": location, "num": 100}
)
data = response.json()
for i, result in enumerate(data['organic_results'], 1):
if domain in result['link']:
results[location] = i
break
else:
results[location] = "Not in top 100"
return results

Example usage

locations = ["United States", "United Kingdom", "Canada"]
ranks = check_rankings("example.com", "SEO tools", locations)
for loc, rank in ranks.items():
print(f"{loc}: Position {rank}")

This helps me quickly identify international SEO issues without manually searching each region. How do you handle multi-location ranking checks?