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Solution:
1. Determine the structure of the data: The first step is to analyze the structure of the 4 webpages and identify the tables that need to be scrapped. This can be done by inspecting the HTML code or using tools like Web Scraping Browser Extension.
2. Identify the data sources: The next step is to identify the data sources for each table. This can be done by looking at the URLs, class names, or ids of the tables. This information will be useful in building the XPath or CSS selectors to extract the data.
3. Choose a web scraping tool: There are several web scraping tools available, such as Scrapy, BeautifulSoup, or Selenium, to extract data from websites. Choose a tool based on your familiarity and the complexity of the webpages.
4. Develop the scraper: Once the tool has been chosen, the next step is to develop a scraper to extract the data from the tables on the webpages. This can be done by using XPath, CSS selectors, or regular expressions. The code should be designed to navigate through the pages and extract the relevant data from the tables.
5. Test the scraper: After developing the scraper, it is essential to test it on the webpages to ensure that all the data has been extracted correctly. Make adjustments to the scraper if any data is missing or not extracted correctly.
6. Export the data: Once the scraper is working correctly, export the data in a suitable format such as CSV, Excel, or JSON.
7. Schedule the scraper: To ensure that the data is updated regularly, schedule the scraper to run at specific intervals, such as daily or weekly.
Conclusion:
Extracting data from the web can be challenging and time-consuming, but the above solution can help you to scrape data efficiently. It is essential to understand the structure of the data and choose the right web scraping tool for the project. With the right approach and tools, 4-page mixed-data scrapping can be done in just 15 minutes.
Best regards,
Giáp Văn Hưng