Overview

The related_queries() function in the pytrends library allows you to retrieve related queries for a specific search term. By analyzing this data, you can gain insights into the questions and topics that are important to your audience, helping you to create more relevant and engaging content.

In this tutorial, we will cover:

  1. Importing the necessary libraries
  2. Setting up the pytrends request
  3. Retrieving related queries data
  4. Analyzing the results

Retrieve Related Queries Data

First, we need to import the necessary libraries and set up our pytrends request.

from pytrends.request import TrendReq

# Set up pytrends request
pytrends = TrendReq(hl='en-US', tz=360)

Next, we’ll specify the search term for our request using the build_payload() function, and then retrieve related queries data using the related_queries() function.

keywords = ['Python']

# Build payload
pytrends.build_payload(keywords, timeframe='now 7-d', geo='')

# Retrieve related queries data
related_queries = pytrends.related_queries()

This will return a dictionary containing related queries data for the search term ‘Python’ over the past 7 days.

Analyzing the Results

Now, we can analyze the related queries data to identify new opportunities for content creation and optimization.

# Extract the related queries for the keyword 'Python'
python_related_queries = related_queries[keywords[0]]['rising']

# Display the top 10 rising related queries
print(python_related_queries.head(10))

This will display the top 10 rising related queries for the search term ‘Python’, providing valuable insights into the questions and topics that are important to your audience.

Conclusion

In this post, we’ve demonstrated how to use the related_queries() function in the pytrends library to uncover related queries for a given search term. By analyzing this data, you can perform in-depth analysis of your target keywords and discover new opportunities for content creation and optimization. This tutorial has covered the process of collecting and analyzing related query data, from setting up the pytrends request to analyzing the results. Using these insights, you can create more relevant and engaging content for your audience.

NOTE : pytrends uses an unofficial API. Please use here for issues.

SAMPLE CODE : https://github.com/hobbyworker/google-trend-for-python