Google Trends is a free and incredibly useful tool that provides search interests, popular keywords and hot topics in many languages for different platforms such as web search, YouTube Industry Email List or Google Shopping. Regardless of the marketing channel, it can be a very useful tool for gaining valuable insights and making meaningful choices for the next steps in your project. Basically, it gives the data on the relative popularity of a keyword from 2004 to the Industry Email List present day, which is really cool! ( Relative popularity is the ratio of interest in your search term to interest in all keywords searched on Google. ) So far so good, but analyzing Google Trends data at scale is usually impractical.
Many of us don't use it much because it seems tedious to search the website for keywords and get data points one by one. So how can we use Industry Email List Google Trends more effectively? In this article, my goal is to show Industry Email List you the pyrenes library in Python and the benefits you can get from it in your data analysis. I will also explain the connection between Google Spreadsheets and Jupiter Notebook in order to import data into Google Data Studio to easily share it with others. For example, when analyzing Search Console data on the Data Studio dashboard,
Wouldn't it be nice to have Google Trends data on the same page? If your answer is yes, let's dig! 3 topics I will cover in this article: Coding with the Industry Email List Pyrenes library and exploring its features Connect Jupiter Notebook to Google Spreadsheets with spready library Import data into Google Data Studio System requirements to use the Pyrenes library Python 2.7+ and Python 3.3+ Requires Requests, lamp, Pandas libraries. If you don't know how to Industry Email List install libraries, check out this Python document. (hint: pip install pandas) Jupiter Notebook is an open source web application that provides the environment to run your code.