Scraping data from Google search results

Hello everyone!

If you came here looking for a fast and efficient solution to collecting data from a Google search, then you came to the right place. In this course, I will show you how to use Python and Google Cloud Platform(GCP) to grab web URLs from Google search results. By utilizing the GCP, you are given a robust set of tools  to customize your collection.

This is guaranteed to be the fastest and most fruitful way to collect data from your searches. This will also open up the door for many other opportunities to explore Python and GCP to take on future projects, such as scraping and collecting images.

The code in this course can be expanded  upon, and I have uploaded it to gitHub as well, and will continue to update it in the future.�

Now.. if you search the internet for “how to scrape google search results”, it is unlikely that you will find a guide like this one, or even a method utilizing the same approach. This was something that I personally has been trying to figure out for a long time, and I have been unable to find any straightforward guides on this method, so I hope you came here and finally found what you are looking for.

The applications for this are limitless, but most people will probably be doing this because they want to save time by automating this process. Essentially, that is what we are doing here: we are automating the process of going to the Google search engine, typing in a search term, finding the most relevant URL, and saving that to a file. This is a process that can do all of that in less that a second. We can utilize Python’s ability to read and write to CSV files in order to ‘feed’ Google a CSV with our search terms, get the most relevant result, and  then append the results to a new CSV file.�

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