Скачать или смотреть Solving the Issue of Appending Scraped Information to Lists with Python Multi-threading

Discover how to effectively collect results when using Python multi-threading for web scraping with Selenium. Learn the best practices to return and combine your scraped data.
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This video is based on the question https://stackoverflow.com/q/77039769/ asked by the user 'Tzeboys' ( https://stackoverflow.com/u/18067334/ ) and on the answer https://stackoverflow.com/a/77040648/ provided by the user 'Emilio Silva' ( https://stackoverflow.com/u/367381/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Overcoming the Challenges of Appending Scraped Data in Python Multi-threading

When diving into the realm of web scraping using Python, especially with the aid of libraries like Selenium and Beautiful Soup, you may encounter an array of hurdles. One such notable problem arises when trying to append scraped information to lists while implementing multi-threading. Many developers see their lists coming out empty or only partially filled, despite their code functioning correctly when executed sequentially. Let’s break down this prevalent issue and explore a coherent solution.

Understanding the Problem

In a typical multi-threading setup using concurrent.futures, one may initiate tasks in parallel that are designed to scrape data from various URLs. However, due to the nature of multi-threading and how Python handles shared data, you may find that not all threads share the lists where you are trying to append your results. This can lead to minimal or even zero data being collected, which can be quite frustrating.

Key Points to Consider:

Thread Isolation: Each thread operates independently, making it difficult to share and append data to a global list.

Data Collection: Relying on global variables for storing data from threads could lead to race conditions or incomplete data.

The Solution: Collecting Results with Futures

To effectively gather results from your threads, you need to adjust your approach slightly. Here’s a step-by-step guide to amend your existing code for proper data collection.

Step 1: Modify the Get_info Function

Instead of directly appending data to global lists, you should return results from the Get_info function. A good practice is to use a dictionary that encapsulates all the required information.

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Step 2: Use Futures to Collect Results

In your main execution logic, use the Future objects returned from the submit function to wait for and collect results across all threads. Here’s how to make those modifications:

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Step 3: Combine the Results

After collecting results, you can consolidate them into the final data structure, such as a DataFrame before exporting to Excel.

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Conclusion

By utilizing the power of concurrent.futures and returning structured results from your threads, you can overcome the challenges associated with collecting scraped data in a multi-threaded environment. This method provides a streamlined approach to gather all your data correctly and efficiently. Embrace these adjustments and watch your Python scraping projects flourish without the pain of empty lists!