DEV Community: Victoria

How I Built a Python Script to Spot SEO Penalty Warning Signs

Victoria — Sat, 27 Jun 2026 08:01:12 +0000

Ever had that sinking feeling when your traffic suddenly tanks and you have no idea why? You check Google Search Console, your analytics, everything looks normal. But something is off. You start wondering—did I get hit with a penalty? Is my site deindexed? Or worse, blacklisted?

I’ve been there. And I learned the hard way that most developers and site owners don’t have a quick way to check for these issues without manually poking around. That’s why I put together a simple Python script that can help you scan your site for common search engine penalty signals. It’s not a replacement for tools like SERPSpur’s Search Engine Penalty Radar, but it’s a great first line of defense.

Here’s the idea: We’ll check a few key indicators—HTTP status codes, robots.txt, meta tags, and basic blacklist lookups. Let’s start with a basic check using requests and BeautifulSoup.

import requests
from bs4 import BeautifulSoup

def check_site_health(url):
    try:
        response = requests.get(url, timeout=10)
        print(f&quot;Status Code: {response.status_code}&quot;)

        # Check for common penalty signals
        if response.status_code == 403:
            print(&quot;Warning: 403 Forbidden - possible blocking or penalty.&quot;)
        elif response.status_code == 404:
            print(&quot;Warning: 404 Not Found - page may be deindexed.&quot;)
        elif response.status_code == 503:
            print(&quot;Warning: 503 Service Unavailable - possible server issue.&quot;)

        # Parse HTML for noindex or canonical issues
        soup = BeautifulSoup(response.text, &#039;html.parser&#039;)
        meta_robots = soup.find(&#039;meta&#039;, attrs={&#039;name&#039;: &#039;robots&#039;})
        if meta_robots and &#039;noindex&#039; in meta_robots.get(&#039;content&#039;, &#039;&#039;).lower():
            print(&quot;Alert: Noindex tag found - page may be excluded from search.&quot;)

        # Simple blacklist check (using a public API like Google Safe Browsing is better)
        # For demo, we just check if the domain is in a known list
        # In practice, use SERPSpur or similar for comprehensive checks.

        return response.status_code
    except requests.exceptions.RequestException as e:
        print(f&quot;Error: {e}&quot;)
        return None

# Example usage
check_site_health(&#039;https://example.com&#039;)

This script gives you a quick sanity check. But real-world penalty detection is more nuanced. You need to monitor index status, check for manual actions, and track blacklist signals across multiple databases. That’s where dedicated tools shine. For a deeper dive, I recommend using something like SERPSpur’s Search Engine Penalty Radar—it automates all this and gives you a clear dashboard. But for daily quick checks, this snippet is a solid start.

Remember: catching a penalty early can save your traffic. Don’t wait until your analytics scream. Keep an eye out.

How to Check Google Rankings from Any Location Without a VPN

Victoria — Wed, 24 Jun 2026 04:30:41 +0000

Ever tried to check how your site ranks in a different city or country, only to get the same local results you always see? It’s one of those small frustrations that can mess with your SEO strategy. I’ve been there—thinking my rankings were solid, only to realize later that I was only seeing results biased by my own location. That’s where a simple, code-driven approach can help.

The trick is to simulate a local search query by spoofing your geographic location. Instead of VPN hopping or asking friends across the globe, you can use tools that manipulate the gl (country) and hl (language) parameters in Google search URLs. For example, searching for “best coffee shops” in Berlin, Germany, as if you were there? Just add &gl=de&hl=de to your URL.

But to scale this for SEO analysis, you can build a tiny script. Here’s a Python snippet using requests to fetch search results for multiple locations:

import requests
from bs4 import BeautifulSoup

def spoof_local_search(query, country, language):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
    }
    params = {
        'q': query,
        'gl': country,
        'hl': language,
        'num': 10
    }
    response = requests.get('https://www.google.com/search', headers=headers, params=params)
    return response.text

# Example: Search for "digital marketing agency" from France
html = spoof_local_search("digital marketing agency", "fr", "fr")
soup = BeautifulSoup(html, 'html.parser')
for result in soup.select('.tF2Cxc'):
    title = result.select_one('h3')
    link = result.select_one('a')
    if title and link:
        print(f"{title.text}: {link['href']}")

This script changes the gl parameter to simulate a user in France. You’ll get results tailored to that region, without leaving your desk. It’s perfect for checking if your site shows up in Parisian searches or if a competitor dominates a specific market.

But here’s where it gets practical: I often need to test multiple cities, languages, and even regions within a country. Manually running this for 50 locations is tedious. That’s why I use a dedicated tool like the SERPSpur Local Search Spoofing Tool. It does exactly this under the hood, letting me pick a country, city, region, or language and instantly see what locals see. No code, no VPNs, no guesswork.

For example, last week I needed to check visibility for a client’s service pages across three German cities. With the tool, I set the location to Munich, Hamburg, and Berlin separately, and spotted that one page wasn’t ranking in Hamburg due to a missing local keyword. That insight came in minutes.

The bottom line: local search spoofing is a must for anyone doing location-based SEO. Whether you use a script or a ready-made tool, the goal is the same—see the search landscape as your target audience does. It saves time, eliminates bias, and helps you make data-driven decisions. Give it a try on your next audit.

Why You're Seeing Endless CAPTCHAs and How to Verify an IP Ban

Victoria — Tue, 23 Jun 2026 11:57:04 +0000

When you start getting hit with CAPTCHA requests every time you search on Google or Bing, it's usually a sign that something's off with your IP address. I've been there—spending hours debugging scripts only to realize my IP was blacklisted.

Search engines block or throttle IPs they suspect of automated activity. This can happen even if you're just scraping legitimately or running frequent tests. The result? You get locked out of search results, which kills productivity.

Here's a quick Python snippet to check if your IP is on common blocklists:

python
import requests

def check_ip_blacklist(ip):
blacklists = [
f"https://www.tcpiputils.com/browse/ip/{ip}",
f"https://www.spamhaus.org/query/ip/{ip}",
]
for url in blacklists:
try:
response = requests.get(url, timeout=5)
if response.status_code == 200:
print(f"Check {url}: Result available")
except:
pass

check_ip_blacklist("8.8.8.8")

But this only covers generic blocklists. For search engines specifically, you need a dedicated check. A tool like the SERPSpur Banned IP Checker scans your IP against Google, Bing, Yahoo, and DuckDuckGo to see if you're blocked. It's useful when you're debugging access issues or setting up proxies.

If you're seeing unexpected CAPTCHAs, run a quick check. It might save you hours of head-scratching.

A Reliable Python Solution for Invoice Data Extraction

Victoria — Thu, 11 Jun 2026 05:05:49 +0000

I had to convert a batch of PDF invoices to CSV for import into our accounting system. Here's a Python script using the SERPSpur Invoice to CSV Converter API:

python
import requests

API_KEY = "your_api_key_here"

def convert_invoice_to_csv(pdf_path):
with open(pdf_path, "rb") as f:
files = {"file": f}
response = requests.post(
"https://api.serpspur.com/v1/invoice-converter",
headers={"Authorization": f"Bearer {API_KEY}"},
files=files
)
if response.status_code == 200:
return response.json().get("csv_url")
return None

Example usage

csv_url = convert_invoice_to_csv("invoice_2024.pdf")
if csv_url:
print(f"CSV ready: {csv_url}")
else:
print("Conversion failed")

This handled complex table structures surprisingly well. Have you found a reliable solution for invoice data extraction?

Why Customizing JSON-LD Schema Can Improve Rich Results Performance

Victoria — Wed, 10 Jun 2026 11:15:07 +0000

While testing various schema markup tools, I came across a structured data generator that promised quick and easy implementation. At first glance, the one-click approach seemed ideal. Generate the JSON-LD code, copy it to your website, and you're done.

However, I was curious about how the tool handled more advanced schema types such as FAQ and Review snippets. These schemas often require careful customization to match page content and maximize eligibility for rich results.

Why Customization Matters

Many website owners assume that generating schema markup is enough. In reality, even small adjustments can have a significant impact on how search engines interpret structured data.

For example:

FAQ schema may require more detailed question-and-answer formatting.
Review schema often benefits from accurate rating, author, and review information.
Product schema may need additional attributes such as availability and pricing.
Article schema can be enhanced with publisher and author details.

Generic output isn't always optimized for every page or use case.

Testing Advanced Schema Types

As I explored the tool further, my main question was whether it allowed users to edit the generated JSON-LD code. The ability to customize markup after generation is important because every website has unique requirements.

A flexible schema generator should provide:

Support for FAQ, Review, Product, and Article schemas
Editable JSON-LD output
Easy addition of custom properties
Validation before implementation
Compatibility with Google's structured data guidelines

Without these features, users may be limited to basic implementations that don't fully leverage rich result opportunities.

The Impact of Small Tweaks

Over time, I've found that minor modifications to schema markup can make a noticeable difference. Adding missing fields, refining descriptions, or improving data accuracy helps search engines better understand page content.

These improvements can increase the likelihood of earning enhanced search features such as:

FAQ rich results
Review stars
Product information panels
Enhanced article listings

Although schema markup alone won't guarantee higher rankings, it can improve visibility and click-through rates when implemented correctly.

What to Look for in a Schema Tool

When choosing a schema generator, don't focus solely on speed and convenience. Look for tools that balance automation with flexibility.

The best tools allow you to:

Generate schema quickly.
Edit the JSON-LD code manually.
Validate structured data before publishing.
Support multiple schema types.
Keep markup aligned with Google's latest recommendations.
Final Thoughts

One-click schema generation is a great starting point, but customization is often where the real value lies. Whether you're working with FAQ, Review, Product, or Article markup, the ability to refine JSON-LD after generation can help improve structured data quality and increase your chances of earning rich results in search engines.
https://infinitydecor.co.uk/

The Fastest Way to Create Valid JSON-LD for SEO

Victoria — Mon, 08 Jun 2026 07:02:39 +0000

Just spent the weekend building a simple schema generator that actually works

I've been helping a friend optimize their recipe blog for Google visibility, and the biggest pain point was always schema markup. Writing JSON-LD by hand is tedious, and most generators I tried either output invalid markup or miss critical properties.

So I hacked together a small CLI tool that takes a URL and auto-generates the most appropriate schema type based on content analysis.

How it works:

Scrapes the page for content type (article, product, recipe, local business, etc.)
Extracts key metadata from HTML tags
Generates JSON-LD with all required + recommended properties
Validates against Google's structured data testing tool

Here's a simplified version of the content classifier:

python
import re
from urllib.parse import urlparse

def classify_content(soup, url):
# Check for recipe patterns
if soup.find('script', type='application/ld+json'):
return 'Recipe' if 'recipe' in str(soup) else 'Article'

# Check for product indicators
if soup.find('meta', {'property': 'product:price:amount'}):
    return 'Product'

# Default to Article for blogs
if len(soup.find_all('article')) > 0:
    return 'Article'

return 'WebPage'

The tool generates complete JSON-LD blocks like this:

{
"@context": "https://schema.org",
"@type": "Recipe",
"name": "{{title}}",
"author": {
"@type": "Person",
"name": "{{author}}"
},
"datePublished": "{{date}}",
"description": "{{excerpt}}",
"prepTime": "{{prep_time}}",
"cookTime": "{{cook_time}}",
"recipeIngredient": {{ingredients}},
"recipeInstructions": {{instructions}}
}

Biggest win: My friend's recipe posts went from taking 20 minutes of manual schema work to about 30 seconds. Google Search Console now shows rich results for 90% of their posts.

For production use, I've been experimenting with SerpSpur's schema generator which handles edge cases better (like multiple schema types on one page, or nested organizations). Their API also validates against Google's latest requirements.

https://serpspur.com/tool/schema-markup-generator-json-ld/

What schema types do you find yourself writing most often? Always looking for new patterns to automate.

python #seo #jsonld #schemarkup #automation

How I Automated Local SEO Rank Tracking with Python

Victoria — Tue, 02 Jun 2026 09:53:49 +0000

I’ve been digging into how local search results vary across different cities, and manually changing VPNs or browser settings every time was a nightmare. So I built a quick Python script that uses SERPSpur’s Local Search Spoofing Tool to simulate results for any country, city, or language. Here’s a snippet to get started:

python
import requests

url = 'https://serpspur.com/tool/local-search-spoofing-tool/'
params = {
'keyword': 'coffee shop',
'country': 'US',
'city': 'New York',
'language': 'en'
}
response = requests.get(url, params=params)
print(response.json())

This returns the top 10 results as if you’re searching from that exact location. No more guessing if your SEO strategy is actually working in different markets. The tool handles the geo-spoofing natively, so you can focus on analyzing the data. I’ve been using it to check how my site ranks in London vs. Sydney, and it’s been a game-changer for local SEO audits. #python #seo #localsearch

Smart Retry Strategies for JSON API Integrations

Victoria — Thu, 28 May 2026 10:46:31 +0000

Quick tip for anyone working with JSON APIs: I created a simple Python decorator to handle retries with exponential backoff. It's saved me from countless transient errors:

python
import time
import functools

def retry(max_attempts=3, delay=1):
def decorator(func):
@functools.wraps(func)
def wrapper(args, **kwargs):
for attempt in range(max_attempts):
try:
return func(*args, **kwargs)
except Exception as e:
if attempt == max_attempts - 1:
raise
wait = delay * (2 * attempt)
print(f'Attempt {attempt + 1} failed, retrying in {wait}s...')
time.sleep(wait)
return None
return wrapper
return decorator

@retry(max_attempts=5, delay=1)
def fetch_data(url):
response = requests.get(url)
response.raise_for_status()
return response.json()

For production systems, I've used SERPSpur's API which has built-in retry logic. But this pattern is useful for any API integration. What's your favorite retry strategy?

Local SEO Testing Hacks: VPNs, Proxies & SERPSpur Explained

Victoria — Mon, 25 May 2026 05:42:27 +0000

Dev.to community! 👋

Quick question for those working on local SEO tools: How do you handle location spoofing for testing search results?

I've been experimenting with different approaches—VPNs, browser extensions, and even writing custom proxy scripts. But I recently found a dedicated tool called SERPSpur that does it cleanly with an API. It's been super handy for my testing pipeline.

What's your go-to method for checking local search results? Any hidden gems I should know about? Let's compile a list of best practices! 🧠
https://serpspur.com/