Building Automated Web Scrapers with Python: A Step-by-Step Guide
Running an effective web scraper requires the right setup and execution. This guide walks through a practical example of scraping wedding venue data using Python and a deep learning model.
Executing Your Web Scraper
The first step is ensuring you’re working within the correct environment. In this case, the “deep seat crawler” conda environment was created specifically for this project. Once confirmed, you can launch your scraper with a simple command:
python main.py
This initiates the scraping process, opening a browser window (since headless mode was set to false) which allows you to monitor the scraping in real-time.
Watching the Scraper in Action
As the scraper runs, it systematically processes each page of results. It begins with page one, then moves to page two, three, and so on, extracting data from each page before proceeding to the next.
The terminal window provides valuable insights into the process through verbose logging. These logs show the scraper making calls to the deep learning model (in this case, “deep seek”) which analyzes the page content to identify and extract wedding venue information.
Data Extraction Process
For each venue, the scraper collects structured information including:
- Venue name (e.g., “Stillwater Pond”)
- Location (e.g., “Temple George, near Atlanta”)
- Capacity
- Detailed description
This information is processed page by page, with the terminal displaying progress updates such as “on wedding page three, I was able to scrape 10 different leads” or “in page three, we got 28 leads.”
Completion and Results
Once the scraper reaches the final page (page six in this example), it automatically terminates the process, closes the browser, and provides a summary of the operation:
- Total tokens used: 43,000 (well within the 60,000 tokens per minute limit)
- Breakdown of leads extracted per page
- Confirmation that all data has been saved to a CSV file
The resulting CSV file contains all the structured data ready for analysis or import into other systems like Google Sheets.
Versatility and Efficiency
The beauty of this approach is its flexibility – by simply changing the target URL, you can repurpose the same scraper to collect data from different websites. This demonstrates the power of automated web scraping: the ability to collect large volumes of structured data quickly and efficiently.
The next step would be importing this data into Google Sheets to create a well-formatted table for easier analysis and sharing.