Question 199750 in Javascript Question posted in
A very good W3school tutorial can be found here.

Javascript – scrape data from tables using playwright

I’m new to playwright and node. Need to scrape some tables, so want to check whats the most efficient way of scraping the large data from tables:

  1. Is it by locating the table with locator and looping through all rows and columns?
  2. Or is it possible to get all the html content of table at once and then get the data from it? if yes, what would be the most efficient way?
  3. Or any other suggested approach?
    Note: some cells contain anchor tags so will need to get the href values as well.

TIA.

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2

Answers


  1. 0

    When scraping large data from tables using Playwright and Node.js, the most efficient approach would depend on the structure and complexity of the table as well as your specific requirements. Here are a few suggested approaches:

    1.Locating the table with a locator and looping through rows and columns:

    • This approach works well for tables with a simple structure and a limited number of rows and columns.
    • Use Playwright’s selectors to locate the table element and then iterate through the rows and columns to extract the desired data.
    • You can use methods like page.$$ or element.$$(‘selector’) to find the table rows and columns.

    2.Getting the HTML content of the table and parsing it:

    • If the table structure is complex or you need to extract data from multiple tables, you can fetch the HTML content of the table using Playwright and then parse it using a library like cheerio or jsdom.
    • Playwright provides the element.innerHTML() method to get the HTML content of an element.
    • Once you have the HTML content, you can use the DOM manipulation capabilities of cheerio or jsdom to traverse and extract data efficiently.

    3.Utilizing data extraction libraries:

    • There are specialized libraries like table-parser or tabulator-parser available that can help you extract tabular data from HTML tables.
    • These libraries are designed specifically for parsing tables and provide efficient methods to extract data, handle complex table structures, and handle various data formats like CSV, JSON, etc.
    • You can integrate these libraries into your scraping workflow to simplify the extraction process and improve efficiency.

    Remember to consider factors like table size, complexity, and the amount of data you need to extract when choosing the most efficient approach. It’s recommended to test and benchmark different methods to determine the best solution for your specific use case.

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  2. 0

    It depends on the use case:

    1. Static Data: If the requirement is just to grab and verify couple of static values from table then I would directly fetch those specific values from the table and verify.

    2. Dynamic Data : On the other hand if it’s going to be verification of most of the table cell values in some form, then I would grab all the table data by looping through it and will store them in an two dimensional array(row, column) and use as required.

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