How to Scrape Google Trends with Python

Python makes this process even better by automating data extraction from Google Trends. This means I can save time and gather large amounts of data quickly and efficiently. In this guide, I’ll show you how to scrape Google Trends data using Python.

We’ll go step-by-step through the setup, required libraries, and methods needed to collect meaningful data for deeper analysis and smarter decision-making.

What is Google Trends?

Google Trends is an online tool that shows how frequently a particular search term is entered into Google relative to the site’s total search volume over time. It’s a great resource for understanding:

• The popularity of certain topics.
• Geographical interest in various terms.
• Seasonality of interest.

Users can visualize and compare trends by accessing this data to understand audience behavior better.

Why Scrape Google Trends?

Scraping Google Trends data can be useful for many reasons:

• Keyword Research: SEO specialists and content creators must know which keywords are popular. Google Trends helps identify trending terms by location or over time, making creating content that drives organic traffic easier.
• Market Research: Marketers need to understand customer interests to predict changes in demand. Google Trends helps track search patterns, giving insights into what customers want and when.
• Social Research: Public interest shifts with events, innovations, and global changes. Google Trends helps researchers see how trends evolve, providing valuable insights for analyzing society.
• Brand Monitoring: Companies can use Google Trends to track their brand’s popularity, compare it with competitors, and respond quickly to changes in public interest.

Best Alternative to Scraping Google Trends

In this guide, we are going to see how to scrape Google Trends with Python. It’s not hard at all, but to do it at scale, you’ll probably need a better solution. I suggest you try Bright Data’s Google Trends Scraper, which is a part of its SERP API product.

It allows you to get structured data easily with an API call, providing you with all the data points you need. Response time and location accuracy are awesome, making it a worthy solution. It’s important to say that I am NOT affiliated with Bright Data, I just had a good experience using its products.

How to Scrape Data from Google Trends

Google Trends has no official API to scrape its data, but some workarounds exist. One popular tool is pytrends, a Python library that makes it easy to download reports from Google Trends. Pytrends is simple and user-friendly, but it has limitations. It can’t always access data behind dynamic or interactive elements.

You can use Selenium with Beautiful Soup to scrape these types of pages. Selenium is a powerful tool that can interact with web pages, even those that use JavaScript to load content. After scraping the data with Selenium, you can use Beautiful Soup to parse the HTML and extract specific information quickly. This combination helps you get the most detailed Google Trends data.

Now, let’s dive into the step-by-step process.

Step 1: Install Python Libraries

The first step is to install the necessary libraries. We’ll use pytrends, a popular Python library for accessing Google Trends data.

To install pytrends, use the following command:

pip install pytrends

Apart from pytrends, we will also use pandas for handling data and matplotlib for visualizing it. You can install them using:

pip install pandas matplotlib

Step 2: Import Libraries

Now, we will start by importing the libraries we just installed:

from pytrends.request import TrendReq
import pandas as pd
import matplotlib.pyplot as plt

Step 3: Set Up the Pytrends Connection

The pytrends library uses a class called TrendReq to interact with Google Trends. We need to create an instance of this class to initiate the connection:

# Initialize a Google Trends session
pytrends = TrendReq(hl='en-US', tz=360)

Here:

• hl=’en-US’ sets the language to English (US).
• tz=360 specifies the time zone. The value 360 represents UTC+6, but you can adjust it according to your needs.

Step 4: Building a Search Query

We need to define the keywords that we want to research. Let’s say we want to search for the popularity of “Python Programming,” “Data Science,” and “Machine Learning” over time:

# Define search terms
keywords = ["Python Programming", "Data Science", "Machine Learning"]
# Build payload
pytrends.build_payload(kw_list=keywords, timeframe='today 12-m', geo='US')

kw_list: This is a list of the search terms we are interested in.

timeframe: This defines the period for which you want the data. ‘today 12-m’ gets data from the past 12 months.

geo=’US’: This restricts the search to the United States. You can change this to ‘all’ for global data.

Step 5: Extracting Interest Over Time

One of the most commonly used features of Google Trends is tracking interest over time. Let’s extract this data:

# Fetch interest over time
interest_over_time_df = pytrends.interest_over_time()
# Display the data
print(interest_over_time_df.head())

This will print a DataFrame showing interest in the specified search terms over time. The output will include the keyword trends and an isPartial column indicating whether the data is complete or estimated.

Step 6: Visualizing Data

Data visualization helps us understand trends more clearly. Let’s plot a graph to visualize the search trends over time:

# Plotting the interest over time
interest_over_time_df.plot(figsize=(10, 6))
plt.title('Google Trends Over Time')
plt.xlabel('Date')
plt.ylabel('Interest Level')
plt.grid()
plt.show()

The plot will show the popularity of each search term over time, making it easy to see trends.

Step 7: Exploring Related Queries

Related queries show other terms people have searched for in conjunction with your keyword. To access related queries:

related_queries = pytrends.related_queries()
# Display related queries for each term
for key, value in related_queries.items():
print(f"Related queries for {key}:")
print(value['top'])

The above code will print related search queries for each keyword you provided, which helps explore how people search for similar topics.

Step 8: Interest by Region

To understand where a topic is most popular, you can look at the geographical interest:

# Fetch interest by region
interest_by_region_df = pytrends.interest_by_region(resolution='COUNTRY')
# Display interest by region
print(interest_by_region_df.head())

The data will show you the interest levels from various countries. For more localized data, use ‘CITY’ instead of ‘COUNTRY.’

Step 9: Visualizing Interest by Region

A heat map is an effective way to visualize interest by region:

# Plotting a bar chart for top countries
interest_by_region_df.sort_values(by='Python Programming', ascending=False).head(10).plot(kind='bar', figsize=(10, 6))
plt.title('Top 10 Countries Interested in Python Programming')
plt.xlabel('Country')
plt.ylabel('Interest Level')
plt.grid()
plt.show()

This will give you a visual representation of which countries are most interested in the keyword “Python Programming.”

Step 10: Google Trends Categories and Data Export

Google Trends categorizes data into various sectors like sports, health, business, etc. You can specify a category to filter results:

# Building payload with a category filter (e.g., 'Computer & Electronics')
pytrends.build_payload(kw_list=["Python"], cat=5, timeframe='today 3-m', geo='US')
# Extracting and exporting data to a CSV file
interest_over_time = pytrends.interest_over_time()
interest_over_time.to_csv('google_trends_data.csv')

This code will get trends data in the Computer & Electronics category and save it as a CSV file, allowing you to perform further analysis or share the data.

Step 11: Handling Data with Pandas

Once you have the data, Pandas makes it easy to manipulate it. For example, you can calculate the average interest over the entire period:

# Calculate average interest for each keyword
average_interest = interest_over_time_df.mean()
print(average_interest)

Step 12: Checking Trending Searches

Google Trends also has a feature called “Trending Searches,” which shows what’s currently popular. You can scrape this information as follows:

# Get today's trending searches in the US
trending_searches_df = pytrends.trending_searches(pn='united_states')
# Display trending searches
print(trending_searches_df)

The trending_searches function gives you a quick overview of what people are currently searching for, which helps stay ahead of breaking news or viral trends.

Step 13: Real-Time Interest

Another cool feature of Google Trends is “real-time trends.” You can check what’s trending in specific regions at the moment:

# Get real-time trending searches
real_time_trends = pytrends.realtime_trending_searches(pn='US')
# Display real-time trends
print(real_time_trends.head())

This allows you to identify spikes in interest almost as they happen, which is valuable for creating reactive content.

Step 14: Keyword Suggestions

If you want to expand your keyword research, you can get suggestions for related keywords:

# Get suggestions for related keywords
suggestions = pytrends.suggestions(keyword='Python Programming')
# Display suggestions
print(suggestions)

This feature can help you discover new areas to explore, enhancing your overall analysis.

Step 15: Automate the Scraping Process

The real benefit of scraping Google Trends is the ability to automate the entire process. You can write a script that runs periodically to collect and save data. Below is an example of automating data collection every week:

import schedule
import time
# Define a function to scrape and save Google Trends data
def scrape_google_trends():
pytrends.build_payload(kw_list=["Python Programming"], timeframe='now 7-d')
data = pytrends.interest_over_time()
data.to_csv('weekly_google_trends_data.csv')
# Schedule the job to run every Monday at 8 am
schedule

Step 16: Handling Common Challenges

When scraping data from Google Trends, you may encounter a few common issues:

• Request Limitations: Google Trends limits the number of requests from a single IP. You can solve this issue by adding a delay between requests or using a proxy. Here is a list of recommended residential proxy providers.

from time import sleep
pytrends = TrendReq(hl='en-US', tz=360)
sleep(60) # Pauses for a minute between requests

• Errors with Pytrends: If Google blocks your requests or returns an error, try re-authenticating with a different IP or wait before making subsequent requests.
• Incomplete Data: Sometimes, data may be incomplete, indicated by the isPartial column in the results. You can handle this by filtering out rows where isPartial is True.

Conclusion

Scraping Google Trends data can provide valuable insights into keyword popularity, consumer behavior, and market trends. By automating this process with Python and using pytrends, you can quickly gather and analyze search interest data to make informed decisions, whether you’re an SEO specialist, researcher, or marketer.

The steps in this guide covered everything from setting up Python libraries to visualizing data and automating the collection process. You can apply these techniques to gather insightful data for your projects or marketing campaigns.