In the previous Python Tutorial for digital marketers, I talked about leveraging Shopify APIs to scrape the competitors’ product feed and monitor up-to-trend products and pricing from there, for the purpose to adjust tactics and keep your business cutting-edge from the same selling marketplaces.

In a way, the product feed is not sufficient to understand the market demand, because you might not want to anchor a dot, where they’re selling low-demand products, or the product trend has been going down. Then, unfortunately, you test along with these sellers and lose money at the end, because you just follow without further step analyzing the big picture.

The big picture is critical in the business battleground, and the end consumer search trend is a key implication to tell you how’s the demand going out there, and what topics they are looking for.

In this piece of Python Tutorial, I would walk through how to pull search data from Google Trends API via Pytrends, then people can learn the data that is integrated with your in-house database, and identify the opportunities. By the end of this Python Tutorial, you can master how to install Pytrends and necessary modules, what available API methods and parameters you can leverage to scrape available data, and custom the data sheet based on actual needs.

In the previous Python Tutorial, we talked about how to scrape more than 50 videos from a Youtube search query keyword, and also grab the performance of each video, such as view, comment, like, etc. However, it’s not the end of automation power, like saying you aim to research, filter Youtubers, and automate the collaboration invitation process. At least, the fetched list of Youtubers should be saved and managed in a datasheet on a cloud drive instead of in the CSV file, that can be set up and easily integrated with other platforms.

So in this Python Tutorial, I will continue to use the Python script from the Python Tutorial Chapter 6, and walk you through how to create a Robot user account, leverage Google Sheet API to save all fetched data in a Google Sheet In your web scraping python script. By the end of this Python Tutorial, you can learn what modules you need to set up, and experience just looking at a spreadsheet that is automatically listing all videos in a preset format.

In the previous Python Tutorial, I walked you through how to create a youtube API key and scrape videos by search queries via python programming. Basically, the beauty of youtube video scraping is you don’t need to pay any fees to research potential YouTubers for your brand and eCommerce store product review, which is a super-efficient way.

But having said that for filtering the better YouTubers, it’s not sufficient for us to select just by those fetched videos, on the other hand, we also need to look into the video data, such as view, comments, likes, dislikes, etc, which can help us make a better candidate list. Also, by default Youtube data API v3 allows developers to scrape the top 50 pieces of videos, I would say it might be not enough because you might miss some secondary tier YouTubers whose videos might rank lower positions, but the content is pretty good and engaging.

As I haven’t talked about how to fetch those data above, so in this piece, I’ll continue to use the previous Youtube video scraping python script, and walk you through Youtube data API video data, how to write the codes to fetch the data, and scrape more than 50 pieces of videos from the search query result.

Previously we took Ring.com website as an example, and walked you through how to specify Ring product price data to scrape and save the bulk of data in local drive. Basically, the script code can scrape any general websites that are built and developed by CMS, such as WordPress, Shopify, etc. If you want to have a copy of the python script file, please contact us.

Being said that, not all the websites can be indexable and crawlable, because the data feed from some websites can be only accessible via API, such as Youtube, Facebook, Amazon, etc. Frankly if you just want to scrape these sites’ data by URL, I suggest you leverage Google Sheet importxml instead of Python, because it’s much easier (Time is money). On the other hand, automation and bulk amount of scraping data work are your regular work, for example, if you are a social media marketer, you need to recruit influencers, or KOLs, the following a series of articles can help relieve your stress and repeating manual work.

We would start with Youtube video scraping first, and by the end of this article, you can learn how to install Youtube API key, and what give methods you can use by Youtube API, how to scrape youtube videos, and video analytic data in bulk.