Last Updated on 29 April 2025
Introduction
Google Trends is a free tool that shows what people are searching for across the web. However, manually checking trends can be time-consuming. That’s where Pytrends, the unofficial Google Trends API for Python, becomes incredibly useful.
In this complete Pytrends tutorial, you’ll learn how to use the Pytrends API to analyze keyword popularity, track search interest over time, and explore trending topics — all using Python.
Read also: What is Seaborn in Python? A Guide to Data Visualization
What is Pytrends?
Pytrends is a Python library that interacts with Google Trends and allows users to retrieve search interest data for specific keywords. It helps track keyword popularity, compare multiple terms, and analyze trends by region or category.
Using Pytrends, you can:
- Monitor keyword popularity over time
- Compare multiple search terms
- Analyze trends by region or country
- Retrieve related queries and rising trends
- Get Google Autocomplete suggestions
- Fetch daily trending search topics
Installation
Install Pytrends using the following command:
pip install pytrends
Initialize Pytrends API
First, import the necessary module and initialize the Pytrends API:
from pytrends.request import TrendReq # Create a Pytrends request pytrends = TrendReq(hl='en-US', tz=360)
Checking Interest Over Time
The function interest_over_time() retrieves the search interest data for specified keywords over a given time period.
How it works?
It fetches the relative search interest for each keyword over time. The values range from 0 to 100, where:
- 100 represents peak search interest.
- 50 means the search term is half as popular as at its peak.
- 0 means insufficient data for that time period.
The function returns a Pandas DataFrame, where:
- The index represents time (usually daily or weekly).
- Each column represents a keyword.
- An additional ‘isPartial’ column indicates whether the data is partial.
# Define keyword(s) kw_list = ["Python", "Java"] # Build payload pytrends.build_payload(kw_list, timeframe='today 12-m') # Get interest over time data = pytrends.interest_over_time() print(data.head())
Output
Python Java isPartial date 2024-01-28 95 61 False 2024-02-04 97 60 False 2024-02-11 100 66 False 2024-02-18 97 64 False 2024-02-25 96 63 False
Interest by Region
Regional interest can help you understand where a keyword is most popular. For example, if you’re checking the popularity of Python language, knowing which regions search for it the most can help with marketing or creating content.
Here’s an example that shows how to retrieve the regional interest for the keyword “Python” and filter it by the top regions:
pytrends.build_payload(["Python"], timeframe='today 12-m', geo='')
data = pytrends.interest_by_region()
# Sort the data to show the top 10 regions with the highest interest in Python
top_regions = data.sort_values(by="Python", ascending=False).head(10)
# Display the top 10 regions
print("Top 10 Regions Interested in Python:")
print(top_regions[['Python']])
Output
Top 10 Regions Interested in Python:
Python
geoName
China 100
Singapore 23
Israel 21
Hong Kong 18
India 16
Switzerland 15
South Korea 14
Tunisia 13
Taiwan 12
Russia 12
Key Details:
geoparameter: This can be used to specify a region or country. For example,geo='US'would fetch data for the entire United States, orgeo='IN'for India.timeframeparameter: Defines the period over which the data is collected, liketoday 12-mfor the last 12 months ornow 7-dfor the past week.
Related Queries
The related_queries() function retrieves other related search queries for a specific keyword. This function provides valuable insights into what other topics or queries are being searched in relation to the keyword you’re searching.
It categorizes the results into top queries and rising queries:
- Top related queries: These are the most popular related searches in the given timeframe.
- Rising related queries: These are searches that have seen the most significant increase in interest over a recent period that indicates trending topics.
# Create a Pytrends request
from pytrends.request import TrendReq
pytrends = TrendReq(hl='en-US', tz=360)
# Specify the keyword
keyword = ['Google', 'Facebook']
# Build the request payload
pytrends.build_payload(kw_list=keyword)
try:
# Retrieve related queries data with error handling
related_queries = pytrends.related_queries()
top_queries = related_queries[keyword].get('top')
rising_queries = related_queries[keyword].get('rising')
print("Top Related Queries:")
print(top_queries.head() if top_queries is not None else "No data available")
print("\nRising Related Queries:")
print(rising_queries.head() if rising_queries is not None else "No data available")
except (KeyError, IndexError):
print("No related queries found for", keyword)
Output
No related queries found for ['Google', 'Facebook']
Google Autocomplete Suggestions
Google Autocomplete provides search predictions based on what users commonly search for. With Pytrends, you can fetch these suggestions programmatically. This is very useful for SEO, content creation, and keyword research.
suggestions = pytrends.suggestions(keyword="Python")
# Display suggestions
for suggestion in suggestions:
print(suggestion['title'], ":", suggestion['type'])
Output
Python : Programming language Ishan Sharma : YouTuber Python family : Snake Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter : Book by Wes McKinney Python : Snake
Daily Trending Searches
Apart from analyzing specific keywords, Pytrends also allows us to fetch Google’s daily trending searches using the today_searches() function. This helps to identify what’s currently trending worldwide or in a specific country.
from pytrends.request import TrendReq
import re
# Create a Pytrends request
pytrends = TrendReq(hl='en-US', tz=360)
# Get today's trending searches
trending_searches = pytrends.today_searches()
# Extract search terms from URLs
def extract_search_term(url):
match = re.search(r"q=([^&]+)", url)
return match.group(1).replace("+", " ") if match else url
# Convert to a readable list
trending_topics = [extract_search_term(link) for link in trending_searches]
# Print the cleaned trending searches
print("Today's Trending Searches in India:")
for i, topic in enumerate(trending_topics, start=1):
print(f"{i}. {topic}")
Output
Today's Trending Searches: 1. Benavidez vs Morrell 2. UNC vs Duke 3. Espanyol vs Real Madrid 4. Ken Martin 5. Celtics 6. Warriors 7. Senior Bowl 2025 8. Kestrel 9. NASCAR 10. Tennessee basketball 11. Bournemouth vs Liverpool 12. Lakers vs Knicks 13. Jey Uso 14. Pebble Beach 15. Serie del Caribe 2025 16. Twinless 17. Nuggets vs Hornets 18. Somalia
Fetching Trends for a Specific Country
You can also get country-specific trending searches by passing a country code to today_searches(). For example, to fetch trending searches in India (‘IN’):
trending_searches_india = pytrends.today_searches(pn='IN') print(trending_searches_india.head())
Limitations of Using the Pytrends Library
While Pytrends is a powerful tool, it has some limitations:
- Unofficial API: Pytrends is not an official Google API, so it may break if Google changes its trends service.
- Rate Limiting: Google may block excessive requests if too many are sent in a short period.
- No exact search volumes: The data provided by Google Trends is not raw search volume but a normalized value, from 0 – 100.
- Limited Historical Data: Some historical data is not available for specific queries.
- Geo-Specific Restrictions: Certain queries may not be available in all countries or regions.
Read also: Google Colab for Python: Advantages vs Disadvantages
Summary
If you want to use Google Trends for SEO, content planning, or market research, Pytrends is a great Python tool to help you. It lets you track keyword popularity, compare terms, explore related searches, and much more — all with just a few lines of code.
This tutorial gave you practical examples to start using Pytrends in your own projects.
Start using Pytrends today and turn search data into smart decisions.
Happy Searching!
