Basically, the interest scores go from 0 to 100 and this demonstrates the level of engagement in Google searches related to that specific keyword. Now, what exactly do all these numbers mean? In a previous Medium article by Simon Rogers a brief explanation of Google Trends data was given. This will display the data for each of the keywords in a table format. Now I just want to see the first ten rows of the dataframe, so I call the head function. I call this dataframe “df” just to keep things simple, but you can call it whatever you want. Next, we create a dataframe with the interest over time. kw_list = pytrend.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='') We define the pytrend all by calling in the keyword list and setting the time frame - in this case the past five years. This is a sample of five basic keywords as a starting point. In this case, we are looking at keywords related to Eating Disorder recovery and/or awareness. The next step is to build a keyword list that you want to investigate.
import pandas as pd from pytrends.request import TrendReq pytrend = TrendReq()
Next, we must import that pandas library to allow us to work with the dataframes. In this case, we need pytrends, which is the connection to Google Trends data.
#Google trends date launched install#
The first step in this analysis is to install the appropriate packages. This tutorial will go through the process of analyzing Google Trends data related to Eating Disorders. May is Mental Health Awareness month, and because of that, I chose to write about a data science project related to mental health.
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