How to Efficiently Scrape Novel Rankings with Python: De‑duplication and Speed Tips

1. Goal

Retrieve the address of the ranking page (http://www.qu.la/paihangbang/), find each novel's name and its link on the site.

2. Observe the Webpage Structure

Each category is wrapped in a consistent HTML block, making it easy for a crawler to locate and collect all novel links, then store them in a list.

3. Small Trick for List De‑duplication

Even across different categories, the same novel may appear multiple times, wasting resources when crawling large volumes. A single line of code solves this: unique_list = set(original_list) Using a set ensures the list contains no duplicate elements.

4. Code Implementation

Modular, functional programming is recommended: write each independent feature as a separate function for simplicity and reusability.

1. Web Scraping Header

2. Get Ranking Novels and Their Links

Iterate through each type of novel ranking, write the results sequentially to a file (novel name + novel link), store the content in a list, and return a list filled with URL links.

3. Get All Chapter Links of a Single Novel

Obtain each chapter's URL and create a novel file.

4. Get Single Page Content and Save Locally

When downloading files, they often contain formatting tags like <br/>. A simple method removes them: html = get_html(url).replace('<br/>', '\n') This replaces <br/> with a newline for proper paragraph breaks.

5. Drawbacks

The crawler works smoothly because the target site lacks anti‑scraping measures and has a clean, well‑structured layout. However, crawling an entire novel (≈1000 pages) takes about 8.5 minutes, and all 60 novels in the ranking would require roughly 8.5 hours with a single‑threaded approach.

To boost speed, consider writing a multithreaded module or, better yet, using the Scrapy framework, which can accelerate crawling by dozens or even hundreds of times.

6. Main Function

7. Output Results