Building a Python Guide to Search and Analyze Etsy Listings with Google API

In today’s digital marketplace, platforms like Etsy provide an invaluable repository of creative products ranging from handmade crafts to vintage items. However, finding specific items or analyzing trends can be quite a challenge. In this blog post, we will walk through an engaging project that utilizes the Google API to search and analyze Etsy listings, allowing developers to harness the power of data from this marketplace effectively.

Introduction

The aim of our project is to build a Python application that searches Etsy listings based on user-defined keywords and analyzes the results to identify potential digital products. By using the Google Custom Search API, we can filter the results to only include relevant Etsy listings while excluding unwanted items through a list of negative keywords. This functionality not only streamlines the search process but also provides a fantastic opportunity to learn about integrating APIs, data processing, and Excel file manipulation in Python.

def google_search_etsy(keyword, max_results=10):
    negative_query = " ".join(f"-{kw}" for kw in NEGATIVE_KEYWORDS)
    query = f"site:etsy.com/listing {keyword} {negative_query}"
    print(f"🔍 Searching Etsy listings for '{keyword}'...")
    
    url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={GOOGLE_API_KEY}&cx={CX}"
    res = requests.get(url)
    data = res.json()
    urls = []

    for item in data.get("items", []):
        link = item.get("link")
        if link and "etsy.com/listing" in link:
            urls.append(link)
    
    print(f"✅ Found {len(urls)} Etsy listings for '{keyword}'.")
    return urls

Prerequisites and Setup

Before diving into the implementation, it’s crucial to have a few prerequisites in place:

def extract_listing_id(url):
    match = re.search(r"etsy\.com/listing/(\d+)", url)
    return match.group(1) if match else ""

• Python: Ensure you have Python 3.x installed on your machine.
• API Keys: Sign up for the Google Custom Search API and obtain your API key. You will also need access to the RapidAPI service for Etsy data.
• Libraries: Install the necessary Python libraries, which include requests, http.client, openpyxl, and openai. You can install these using pip:

pip install requests http.client openpyxl openai

Additionally, create a keywords.txt file containing the keywords you want to search for, one per line.

Core Concepts Explanation

To build our application, we need to understand several core concepts:

def get_etsy_listing(listing_id):
    try:
        conn = http.client.HTTPSConnection("etsy-data-api.p.rapidapi.com")
        headers = {
            'x-rapidapi-key': RAPIDAPI_KEY,
            'x-rapidapi-host': "etsy-data-api.p.rapidapi.com"
        }
        conn.request("GET", f"/get-listing?listing_id={listing_id}", headers=headers)
        res = conn.getresponse()
        data = res.read()
        conn.close()
        listing_data = json.loads(data.decode("utf-8"))
        return listing_data
    except Exception as e:
        print(f"❌ Error fetching listing {listing_id}: {e}")
        return None

1. API Integration

APIs (Application Programming Interfaces) allow different software applications to communicate with each other. In our case, we are using the Google Custom Search API to retrieve Etsy listings based on specific keywords. Understanding how to make API requests, handle responses, and manage errors is crucial for successful integration.

2. Regular Expressions

Regular expressions (regex) are a powerful tool for searching and manipulating strings. In our project, we utilize regex to extract specific data, such as listing IDs from Etsy URLs. This technique allows us to efficiently process and analyze data without cumbersome string operations.

3. Data Analysis and Excel Manipulation

After gathering the data, we will analyze it to identify trends and product types, particularly focusing on digital products. We will also leverage the openpyxl library to create and manipulate Excel files, providing a structured way to store and present our findings.

Step-by-Step Implementation Walkthrough

Now that we understand the core concepts, let’s break down the implementation of our application.

def looks_digital_by_text(title, tags, description):
    combined = " ".join([str(title or ""), str(tags or ""), str(description or "")]).lower()
    for indicator in DIGITAL_INDICATORS:
        if indicator in combined:
            return True
    return False

1. Configuration

We begin by setting up the configuration for our application. This includes defining API keys, output file names, and negative keywords. Utilizing environment variables for sensitive information like API keys helps keep our credentials secure.

2. Google Search Function

The first major function we implement is the Google search function. This function constructs a query that leverages both the user-provided keywords and the negative keywords to filter out irrelevant results. This ensures that our search is efficient and focused.

3. Extracting Listing IDs

After retrieving the search results, we need to extract the listing IDs from the URLs. This is where our regex skills come into play. We define a function that searches and returns the listing ID from each URL, which is essential for further data retrieval.

4. Retrieving Listing Details

With the listing IDs in hand, we can now fetch detailed information about each Etsy listing using the RapidAPI service. This step involves making an API call and handling potential errors gracefully, ensuring that our application is robust and reliable.

5. Analyzing Digital Products

To identify potential digital products, we implement a heuristic function that checks for specific keywords in the title, tags, and description of each listing. This analysis will help us categorize the listings effectively and focus on digital products that may be of interest.

6. Storing Results

Finally, we compile our findings and write them to an Excel file using the openpyxl library. This process allows for easy sharing and analysis of the data, making it practical for users who may want to perform further exploration or visualization.

Advanced Features or Optimizations

Once the basic functionality is in place, consider implementing advanced features to enhance your application:

def is_relevant_listing(listing_text, keywords):
    prompt = f"""
You are a market analyst for an Etsy shop that ONLY sells digital products.

You are given:
- Focus keywords: {', '.join(keywords)}
- A product listing's text content.

Determine if the listing is strongly related to ANY of these focus keywords AND is a digital product (PDF, eBook, printable, template, digital download). 
Ignore all physical products (t-shirts, mugs, posters, stickers, handmade items).

Respond with only 'YES' or 'NO'.
"""
    try:
        response = openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": "You are an intelligent Etsy product classifier."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=3,
            temperature=0
        )
        answer = response.choices[0].message.content.strip().upper()
        return answer.startswith("Y")
    except Exception as e:
        print(f"❌ OpenAI error: {e}")
        return False

• Rate Limiting: Implement rate limiting on API calls to avoid hitting usage caps.
• Multi-threading: Use multi-threading or asynchronous programming to improve the performance of API calls, especially if processing a large number of keywords.
• Data Visualization: Integrate visualization libraries like matplotlib or seaborn to create insightful graphs and charts based on the analyzed data.

Practical Applications

This project has numerous practical applications:

def main():
    with open(KEYWORDS_FILE, "r", encoding="utf-8") as f:
        keywords_list = [line.strip() for line in f if line.strip()]

    if not keywords_list:
        print("❌ No keywords found in keywords.txt")
        return

    print("\n⚡ Keywords loaded from keywords.txt:")
    for kw in keywords_list:
        print(f" - {kw}")
    
    proceed = input("\nDo you want to start the Etsy search with these keywords? (yes/no): ").strip().lower()
    if proceed not in ["yes", "y"]:
        print("❌ Exiting. Update keywords.txt and try again.")
        return

    all_urls = []
    for keyword in keywords_list:
        urls = google_search_etsy(keyword, max_results=10)
        all_urls.extend(urls)
        time.sleep(1)  # avoid hitting API too fast

    all_urls = list(dict.fromkeys(all_urls))

    if not all_urls:
        print("❌ No URLs found for any keywords. Exiting.")
        return

• Market Research: Businesses can utilize this application to monitor trends in their niche, helping them to make informed decisions about product listings.
• SEO Optimization: By analyzing keywords and product descriptions, sellers can optimize their listings to improve search visibility.
• Competitive Analysis: Sellers can evaluate competitors’ products and pricing strategies, gaining insights into their market positioning.

Common Pitfalls and Solutions

While building this application, developers may encounter some common pitfalls:

• API Limits: Ensure you are aware of API call limits and implement error handling to manage rate-limiting responses gracefully.
• Data Inconsistencies: When dealing with external data sources, be prepared to handle missing or inconsistent data in API responses.
• Regex Errors: Regular expressions can be tricky. Always test your regex patterns thoroughly to ensure they match the expected formats.

Conclusion with Next Steps

In this blog post, we have explored how to build a Python application that effectively searches and analyzes Etsy listings using the Google Custom Search API and RapidAPI. By understanding the core concepts, implementing the application step-by-step, and considering advanced features, you now have a solid foundation for further exploration.

As a next step, consider enhancing your application with additional features, or perhaps explore integrating other APIs to broaden its functionality. The world of data is vast, and there are countless opportunities to leverage it for insights and innovation!

Happy coding!