The Ultimate Guide to Google Trends Scraping with Python

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The Ultimate Guide to Google Trends
Scraping with Python
Since its initial release in May 2006, Google Trends has become one of the leading
search-tracking datasets. Every month, millions of people visit the website to study
and examine the extensive collection of Google search data. Understanding what
people search for online has never been as impactful as it is now, and that’s why
Google Trends is helpful.
With more than 1 billion searches conducted on Google each day and Google Trends
being used 80% more than before, data is in higher demand than ever for making
decisions. Using a few lines of code, you can track trends, identify early
opportunities, and make your strategies reflect consumer interests. In this article,
we’ll show you how to use web scraping to get keyword insights and trending data
from Google Trends.
What is Google Trends and How Is It Used?
Google Trends is a tool created by Google that measures the popularity of search
terms on the internet, which is helpful in gaining insight into market and user
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behavior. This allows people to identify different seasons, detect emerging interests,
and learn what affects their audience.
You can use Google Trends, a free service from Google, to find out what topics
people worldwide are searching for. The tool is popular among marketers, content
creators, businesses, and researchers wanting to observe changes and breakdowns
in interest, geographic data about popularity, and spot new questions.
Key features include:
● Through the analysis of search term trends over time, Google Trends
emphasizes the ability of users to identify developing patterns and
interpret seasonal interest fluctuations.
● The tool connects search interest to a particular area and displays
which regions or cities are interested in a given keyword most.
● Through Google Trends, users are able to explore more keywords and
trending topic and eventually this influences and provides new
directions for content strategies and understanding the motives of the
users.
● By delivering real-time insights, Google trends can help monitor
current events while an indicator of viral content appears.
● Users have the option to narrow the search trends down by selecting
any category, such as business, health, or entertainment, to perform
deeper research.
● It allows users to compare the search trends for up to 5 terms, on how
they rank together in popularity is displayed side by side.
● Using the platform, Google Trends users can share data and charts in
reports and slideshows or even add data research.
● Because of Google Trends, where one can see what’s trending
currently and when, businesses can perfect their market research,
develop targeted content, and fine-tune their SEO methods.
Why Scrape Google Trends Data?
Through programmatic extraction of Google Trends information, you get more
in-depth insights, automated processes, and more versatility that goes far beyond
the features of the platform. With the help of automated data retrieval, you can
monitor trends of keywords, process enormous amount of data, and integrate
patterns of search in your business decisions.
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1. Keyword Research and SEO Optimization
Google Trends is an unprecedented resource for SEO specialists and content
producers. It demonstrates the keyword search interest change over time, which
helps to make a better decision about high-traffic keywords. Use of Python or
similar programs gives marketers a chance to pull and analyze trends of keywords,
observe new search interests, and fine-tune their content plans to be in step with
consumer behavior. Businesses can take advantage of this idea to improve their
SEO performance and promote the number of qualified website visitors.
2. Market Research and Business Intelligence
Businesses obtain knowledge of how consumer interest varies across the regions
and times by examining the scraped Google Trends data. It can be helpful for
e-commerce businesses to determine regional interest in product categories and
use this as a basis to change inventory strategies, time seasonal campaigns, or
create new offerings. By combining Google Trends data with material on consumer
spending and demographics, businesses can be better able to estimate the direction
of demand trends and refine their marketing efforts.
3. Societal and Cultural Research
Google Trends is used by researchers, journalists, and analysts in tracking how
much interest there is in social issues, news events, or breaking news. Accessible
data enables tracking of sustained patterns, monitoring flips in public sentiment,
and identifying a consistent behavior of a general population. Monitoring public
interest using scraped data is important in media journalism, scholarly research,
and policy analysis because scraped data gives insights into how attitudes toward
topics change over time.
4. Competitor and Trend Monitoring
Scraped data provides businesses with an insight on how the public attention on
their competitors or topics related to the industry is shifting. This information
enables businesses to watch for turnarounds in market behavior, alterations in
consumer tastes, or the introduction of new entrants to their industry. Repetitive
scraping of data enables companies to detect trends sooner compared to their
rivals.
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5. Custom Analytics and Reporting
The aggregated Google Trends data can be consumed in the in-house dashboards or
data lake or business intelligence applications. This functionality empowers
organizations to automate trending topic reports and keyword performance across
groups, and overlay trend data with their core business KPIs for analysis. It makes
it easy to develop custom notifications once specific keywords experience increased
search activity.
6. Brand Reputation Monitoring
With the help of Google Trends, companies can monitor how a brand’s public profile
and reputation change over time. A sudden rise in the volume of searches can be
an indicator of increased interest, or it can indicate a situation (a scandal, a
controversy). Using data scraping, all kinds of firms can create alerts or visual
dashboards that track fluctuations in search volume based on new product releases,
marketing efforts, PR efforts, or media reports. It gives brands insights to
determine how to compare against the competition, follow their reputation
strategies’ success rates, and initiate strategic communications when necessary.
How to Scrape Google Trends Data
To scrape extensive Google Trends data, it is important to follow the predetermined
steps. To begin with, it is important to install the required libraries and complete the
setup process.
Install libraries
Once you have access to the Google Trends scraper, you will need to install some
extra libraries. Open a terminal and execute the subsequent pip command:
Then, import these libraries into a new Python file.
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To use API requests, you will require the requests library, as well as pandas to
manipulate incoming data.
Send a request
Let’s begin by creating a first request to the API:
The variables USERNAME and PASSWORD include the authentication required by
the Web Scraper API, while the payload contains the API’s configuration on how
to handle your request.
Meanwhile, the source specifies the type of scraper that will be utilized to process
this request. Naturally, google_trends_explore is suited to this particular use case.
Also, specify the query you want to search for. Check out our documentation for
more information on available parameters, such as how to handle Google’s terms
and API request rate constraints.
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You can also use the Google Trends URL to narrow your query and scrape pertinent
data.
The configuration is completed. You may now complete and send the request.
If everything is in order, when you run the code, you should see the raw results of
the query in the terminal window, like the following:
Save results to CSV
Now that you have the findings, modify the formatting and save in a CSV file so
that the data may be easily analyzed. The Pandas Python library can let you do all
of this. This strategy is excellent for individuals who want to scrape and arrange
data efficiently.
The API’s answer includes four sorts of information: interest_over_time,
breakdown_by_region, related_topics, and related_queries. Let’s separate each
category into its own CSV file.
Begin by converting each to a pandas dataframe:
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Because the data for related_topics is multi-level, you’ll need to flatten it into a
single-level structure. As a result, the flatten_topic_data function was added to
accomplish this.
The final step is to save the data to a file:
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You’ve now set up a folder structure for storing all of your individual CSV files,
organized by keyword.
Scraping publicly available Google Trends data using Python is an effective tool for
firms wanting to keep ahead of market trends.
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Once we transform the initial request information into dataframes, use the Pandas
library in order to ease keyword comparison. This approach will be of more use in
analyzing the volume of searches for multiple keywords or in multiple areas.
Adjusting your existing code to support a number of keywords and developing a
solution for gathering all data in one place is critical. By analyzing Google Trends
data, you can look at the geolocation of the searches to learn about geographical
interest trends.
Now let’s move to the control of multiple keywords at once. In order to make your
code flexible, divide it into individual reusable functions.
First, create a function that takes care of the API request with a query and returns
requested data after completion.
Next, you will require a function that converts a raw resolve into pandas
dataframes, saves them as CSV files, and returns them:
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After completing the request and dataframe building, you can create comparisons:
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This function will receive the dataframes for all of the queries you’ve written, review
them, and merge them for comparison on important metrics.
Finally, create your application’s main logic.
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When the code is run, comparative CSV files containing the aggregated data of the
provided keywords for each category will be produced:
● interest_over_time
● breakdown_by_region
● related_topics
● Related_queries
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Best Practices of Scraping Google Trends Data
While using Google Trends data can help, it should be collected thoughtfully and
ethically. When you follow best practices, your data stays correct, you won’t get
your IP blocked, and you follow Google’s terms.
● Limit Request Frequency
You should space out your requests from your Google account with intentional time
intervals to avoid complications with your Google account. For example, use
time.sleep(5) between calls.
● Use Rotating Proxy
In a large scale or international scraping, using a rotating proxy or a VPN to rotate
your requests can help manage and distribute your requests. Don’t forget to follow
Google’s terms and conditions strictly.
● Schedule Scraping During Low-Traffic Hours
In order to avoid traffic spikes, plan the timing of your automated scraping efforts
so they coincide with periods when Google’s servers are loaded the least; late at
night or early morning would be ideal.
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● Clean and Validate Data Regularly
The obtained data scraped often has missing points or an unusual pattern. Check
that your scraped data have no missing entries or unsolicited surges.
● Respect Google’s Terms of Service
It is possible to scrape public data normally as long as you adhere to Google’s terms
and do not overload their system.
● Log Your Requests and Errors
Monitor the running of your scraping tasks by recording the time, context, and
problems you face. By doing so, you provide for easier maintenance and guarantee
the reliability of your data over time.
Challenges of Google Trends Data Scraping
Google Trends provides comprehensive data that helps businesses analyze trends.
When scraping Google Trends data, there are various challenges that occur. These
are:
1. Normalized Data, Not Absolute Volume
The tool shows data as a percentage of peak interest, not as actual query volume.
A score of 100 implies the term got as popular as it can get, but it doesn’t
necessarily mean that it has to be some exact number of searches. Though it is
good at establishing trend comparisons, its limitations render it unable to be used
to measure exact search numbers.
2. Data Sampling and Smoothing
To facilitate viewing, Google can automatically sample or smooth and time or limit
keywords with poor visibility. Such a method reduces data too easily, concealing
short-term elevation or spikes. As such, small but important trends in search
patterns may be overlooked.
3. Limited Keyword Comparisons
The platform only allows five comparisons of keywords at any given time. This
limitation obstructs the opportunities to explore several terms in common in search
of detailed insights. To overcome this, you need to perform individual analysis for
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various sets of keywords and then manually integrate the data, which can take a lot
of time.
4. Rate Limiting and Temporary Blocks
Too many requests in a short time, especially when scraping, can result in throttling
or blocking you by Google. As a result, you may undergo IP blockage by way of
throttling, damaging incomplete data sets. In order to avoid this problem, it is
required to slow down your request rate by inserting delays or through proxying
your queries.
5. Changing Data Over Time
With Google collecting more insights or changing its methods, the trend data can be
changed. You can get varying outcomes depending on the time you search for the
same terms. In order to provide consistency in your analysis, make sure you note
the data and as well associate a time stamp as you collect the data.
Conclusion
Using Google Trends, companies can identify fresh trends, measure the interests of
consumers, and rely on data to plan their marketing, product development, and
strategy choices. Since people often search online for new ideas, companies can use
this information to spot new opportunities. So, they can introduce products that
match consumers’ wants and lead the competition. The expert team of X-Byte
provides Good Trends data scraping to get this comprehensive data smoothly. With
Python and the pytrends library, our team extracts data securely and conveniently
by exploring search trends on Google. Analyzing how trends, regions, and user
behavior change allows marketers, developers, researchers, and entrepreneurs to
make better decisions.
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The Ultimate Guide to Google Trends Scraping with Python

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