#GOOGLE TRENDS API PYTHON HOW TO#
#GOOGLE TRENDS API PYTHON INSTALL#

pytrends.get_historical_interest(search_list) df_ot = pd.DataFrame(pytrends.interest_over_time()).drop(columns='isPartial')Īnd to see a breakdown of these number on an hourly bases you can use the get_historical_interest method. We can now look at the the interest over time method to see the number of searches, based on a ranking where 100 is the most popular. Pytrends.build_payload(search_list, timeframe='today 12-m') Next setup the payload and keep the timeframe for searches to the past 12 months only. First thing is to import the necessary libraries and create the connection to Google Trends. I will use this list to look for number of searches and other related information. I’ve used the website to select the top 5 databases (as per date of this blog post). Let’s now explore these APIs using the Databases as the main topic of investigation and examining some of the different products. Suggestions: returns a list of additional suggested keywords that can be used to refine a trend search.Related Queries: returns data for the related keywords to a provided keyword shown on Google Trends’ Related Queries section.Interest by Region: returns data for where the keyword is most searched as shown on Google Trends’ Interest by Region section.Interest Over Time: returns historical, indexed data for when the keyword was searched most as shown on Google Trends’ Interest Over Time section.For my example I’ll be using the following: This will make it ease to format and explore the data. The pandas library is also loaded as the data returned by pytrends API into a pandas dataframe.

#GOOGLE TRENDS API PYTHON HOW TO#

The following code illustrates how to import and setup an initial request. You can get around this by using a proxy and there is an example on the pytrends PyPi website on how to get around this. You do need to be careful of how many searches you perform as you may be limited due to Google rate limits. In my particular case, the only library it updated was the version of pandas.

#GOOGLE TRENDS API PYTHON INSTALL#

To install pytrends use the pip command pip3 install pytrendsĪs usual it will change the various pendent libraries and will update where necessary. Some of the information is kind of interesting when you look at the related queries and also the distribution of countries. The information presented is based on what searches have been performed over the past 12 months. Here are a couple of screen shots from Google Trends, comparing Relational Database to NoSQL Database. For example, here is a quick example taken from the Google Trends website. Many of you are already familiar with using Google Trends, and if this isn’t something you have looked at before then I’d encourage you to go have a look at their website and to give it a try. The following examples show some ways you can use this library and the focus area I’ll be using is Databases. Pytrends is a library providing an API to Google Trends using Python. Less scientific are examples shown at TOPDB Top Database index and that isn’t meant to be very scientific. Yes a more rigorous scientific study is needed, and some attempts at this can be seen at. This is just a little fun to see what is possible. It isn’t very scientific or rigorous, so don’t come complaining if what is shown doesn’t match your knowledge and other insights. The examples shown below are just examples of what is possible. Exploring Database trends using Python pytrends (Google Trends)Ī little word of warning before you read the rest of this post.

Google trends api python