Although you’ll be able to follow along without experience, it’s always a good idea to start from the basics. Note: If you’re new to web scraping, we’ve created a web scraping in Python tutorial for beginners. We did the same thing for a couple more entries from different paginated cells and yes, it seems like all our target data is in there even though the front-end doesn’t display it.Īnd with this information, we’re ready to move to the code! Scraping HTML Tables Using Python’s Beautiful Soupīecause all the employee data we’re looking to scrape is on the HTML file, we can use the Requests library to send the HTTP request and parse the respond using Beautiful Soup. Next, copy a few cells and search for them in the Source Code. To verify this, Right Click > View Page Source. Of course, because this is an HTML table, all the data should be on the HTML file itself without the need for an AJAX injection. So which one is gonna be? Either of these solutions will add extra complexity to our script, so instead, let’s check where’s the data getting pulled from first. Or clicking on the next button to move through the pagination. The first is clicking the drop-down menu and selecting “100” to show all entries: ![]() It only shows ten rows which matches the number of entries selected on the front-end.Ī few more things to know about this table is that it has a total of 57 entries we’ll want to scrape and there seems to be two solutions to access the data. There’s a clear tag pair opening and closing the table and all the relevant data is inside the tag. This is why this is a great page to practice scraping tabular data with Python. Let’s enter the table’s URL () in our browser and inspect the page to see what’s happening under the hood. Still, understanding how they work is crucial for finding the right approach. However, as we’ll see in real-life scenarios, not all developers respect these conventions when building their tables, making some projects harder than others.
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