![]() Next, we iterate through the dataframe storing our GSC Queries data. ![]() Now we create an empty dataframe ( sortby will import GSC data from whatever metric you’re sorting by) that will hold our trending information along with a couple of lists and counters for stats. df = pd.read_csv(get_gsc_file, encoding='utf-8')ĭf.sort_values(by=, ascending=False, inplace=True) Now we import the Queries.csv file into a pandas dataframe, sort descending by whatever dimension you chose above, and select the top n with n being whatever cutoff value you chose above. geo: Two-letter code for which country you want to segment.Choices are “Today 1-m”, “Today 3-m”, or “Today 12-m”. timeframe = What span of data do you want Google Trends to return.pause: How many seconds between Google Trends calls.Keep to a reasonable amount, perhaps under 200 or Google may ban you. cutoff: How many top n queries you want to process.Choices are “Clicks”, “Impressions”, “CTR”, or “Position” sortby: How you want the top n queries to be selected.get_gsc_file: the GSC Queries.csv file name and location.Next, we declare some variables we’re going to be using. import pandas as pdįrom pytrends.request import TrendReq Settings and GSC Dataframe Setup If you are using Google Colab put an exclamation mark at the beginning.įirst, we very simply import the 4 modules listed above that the script requires. pandas: for storing the information in a table formįirst, let’s install the pytrendsmodule which you won’t like have already.time: for pausing the script to prevent blocking.JSON: handles the request from the Google Trends API which comes in JSON format.pytrends: module that connects to the Google Trends API.Understanding how Google Trends works and what the data means.Google Search Console performance data “Queries.csv” export file.Python 3 is installed locally or Google Colab, and basic Python syntax is understood.Not interested in the tutorial? Head straight for the app here! Also, be sure you understand how Google Trends works and what their metrics mean. Be sure you understand these things before taking any action or having confidence in them. Note, the results vary heavily depending on your settings and how you calculate the trend. This could help forecast query performance (by Geo) in the near term and help you find queries to create more content if trending up! ![]() In this Python SEO tutorial, I’ll show you step by step how to create the framework for determining if your top queries are trending up, down, are flat, or don’t have enough data. What if we could begin to marry these two systems to find the overall search trend for your top ranking queries to understand what the near-term potential could be. Google Trends also is a useful platform that can give insights into a query’s relative popularity within Google’s system (by Geo) historically and a little forecasting for the future. ![]() Google Search Console already gives SEO’s amazing historical data for how the queries you rank for are performing. ![]()
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