DEV Community: kevincarroll

How to Choose the Right Keywords for Different Geographic Markets

kevincarroll — Fri, 26 Jun 2026 10:57:32 +0000

When you're doing keyword research for a new project, the typical workflow involves brainstorming terms, checking search volume, and maybe looking at difficulty scores. But if you're targeting multiple countries, the process becomes exponentially more complex. A keyword that's high-volume in the US might have zero searches in Germany, and competition levels vary wildly by region.

Most developers start with Google Keyword Planner or similar tools, but these often lack granular country-level data or require manual filtering. You end up exporting CSV files and cross-referencing metrics across different tools, which is inefficient and error-prone.

This is where having a unified keyword research tool with country-level filtering becomes essential. Instead of jumping between platforms, you can analyze search volume, CPC, keyword difficulty, and ad competition all in one place, tailored to specific geographic markets.

Here's a simple Python script to demonstrate how you might programmatically check keyword difficulty using an API:

python
import requests

def check_keyword_difficulty(keyword, country_code='us'):
# Hypothetical API endpoint
api_url = f"https://api.keywordresearch.com/v1/difficulty"
params = {
'keyword': keyword,
'country': country_code,
'api_key': 'YOUR_API_KEY'
}
try:
response = requests.get(api_url, params=params, timeout=10)
data = response.json()
return {
'keyword': keyword,
'difficulty': data.get('difficulty', 'N/A'),
'volume': data.get('volume', 0),
'cpc': data.get('cpc', 0.0)
}
except Exception as e:
print(f"Error: {e}")
return None

Example usage

keyword_data = check_keyword_difficulty('best SEO tools', 'us')
if keyword_data:
print(f"Keyword: {keyword_data['keyword']}")
print(f"Difficulty: {keyword_data['difficulty']}")
print(f"Volume: {keyword_data['volume']}")
print(f"CPC: ${keyword_data['cpc']}")

This gives you a basic structure, but real-world keyword research needs more depth. You want to see historical trends, seasonal patterns, and competitor bids for ad placements.

Tools like the SERPSpur Keyword Research Tool provide all these metrics in one dashboard. You can enter a keyword, select a country, and instantly get search volume, CPC, keyword difficulty, and ad competition data. This is particularly useful when you're expanding into new markets or optimizing existing content for specific regions.

Why does this matter for SEO? Targeting the right keywords with the right difficulty level for your domain authority is critical. High-difficulty keywords might be wasted effort for a new site, while low-competition terms in specific countries can drive targeted traffic without heavy competition.

Whether you're building an SEO tool or doing manual research, having country-level keyword data helps you make informed decisions and avoid wasting resources on terms that won't convert.

How to Convert PDF and Excel Invoices to CSV for Faster Data Processing

kevincarroll — Sat, 20 Jun 2026 03:35:14 +0000

Manually converting invoice data from PDF or Excel files into CSV format is one of the most time-consuming tasks in accounting and data management workflows. It often involves repetitive copy-pasting, formatting adjustments, and a high risk of human error.

In many real-world scenarios, invoices arrive in different formats such as PDF, XLS, XLSX, or even HTML. Handling them individually can slow down reporting pipelines and create inconsistencies in structured data storage.

The Problem with Manual Conversion

Traditional invoice processing usually involves:

Extracting line items manually from PDFs
Reformatting Excel sheets for database compatibility
Fixing inconsistencies in columns and values
Rechecking for missing or misaligned data

As invoice volume increases, these tasks quickly become inefficient and error-prone.

Automated Approach to Invoice Conversion

A more efficient approach is using tools that automatically parse invoice documents and convert them into structured CSV format.

These tools typically:

Read multiple file formats (PDF, XLS, XLSX, HTML)
Detect table structures and line items
Normalize data into rows and columns
Export clean CSV files ready for spreadsheets or databases

For example, uploading a multi-page invoice PDF can result in fully structured rows representing each item, without manual formatting adjustments.

Why CSV Output Matters

CSV remains one of the most widely used formats for:

Accounting software imports
Database ingestion
Data analysis workflows
Spreadsheet processing

Having clean CSV output ensures compatibility across systems and reduces preprocessing work.

Practical Impact

Automating invoice-to-CSV conversion helps reduce:

Repetitive manual data entry
Formatting inconsistencies
Processing time for bulk invoices

It also improves accuracy when handling large datasets.

Closing Note

As data-driven workflows become more common in finance and operations, automating repetitive tasks like invoice conversion can significantly improve efficiency and reliability without changing existing systems.

Building an Affordable SEO Toolkit in Python Using SERPSpur API

kevincarroll — Sat, 13 Jun 2026 08:19:33 +0000

If you've ever used Semrush or Ahrefs, you know the sticker shock. I've been exploring an alternative that covers the essentials without the premium price tag. Here's a Python snippet that uses the SERPSpur API to pull a comprehensive site analysis—traffic estimates, keyword rankings, site health, and backlink gaps—all in one call:

python
import requests

API_KEY = "your_api_key_here"

def analyze_site(domain):
response = requests.get(
"https://api.serspur.com/v1/site-analysis",
headers={"Authorization": f"Bearer {API_KEY}"},
params={"domain": domain, "include": "traffic,keywords,health,backlinks"}
)
data = response.json()
print(f"Domain: {data['domain']}")
print(f"Traffic: {data['traffic']['estimated_monthly']}")
print(f"Top Keywords: {[kw['keyword'] for kw in data['keywords'][:5]]}")
print(f"Site Health Score: {data['health']['score']}")
print(f"Backlink Gap Count: {len(data['backlinks']['gaps'])}")
return data

Example usage

analyze_site("example.com")

This gives you a quick snapshot without bouncing between tools. I've found it particularly useful for competitor analysis on a budget. What features do you consider must-haves in an SEO toolkit?

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?