Google Trends Scraper

Google Trends Scraper

Google Trends Scraper is a Google Trends scraping tool that programmatically collects interest-over-time data for your keywords from Google Trends using pytrends Google Trends under the hood. It solves the pain of manual checks by delivering clean Google Trends time series data you can analyze, automate, and integrate—serving as a practical Google Trends API alternative for marketers, developers, analysts, and researchers. At scale, it enables Google Trends automation scripts and Google Trends bulk keyword research to monitor shifts over days, weeks, or months, and export Google Trends to CSV or JSON for downstream use.

What data / output can you get?

The actor outputs a single, structured record per run with a timeline array of daily/weekly/monthly points (depending on your selected timeRange). Field names below reflect the exact JSON keys produced by the actor.

Data type	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords, preserved for traceability.	"chatgpt, AI, python"
searchTerm	Same as inputUrlOrTerm for search context.	"chatgpt, AI, python"
interestOverTime_timelineData	Array of timeline rows; each row contains the date plus a value for each input keyword.	[ { "date": "2025-08-24", "chatgpt": 87, "AI": 65, "python": 42 }, ... ]
interestOverTime_timelineData[].date	ISO-formatted date for the data point. Granularity depends on timeRange.	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Trend index value for the keyword "chatgpt" on that date.	87
interestOverTime_timelineData[].AI	Trend index value for the keyword "AI" on that date.	65
interestOverTime_timelineData[].python	Trend index value for the keyword "python" on that date.	42
interestOverTime_timelineData[].	Dynamic field for each keyword you provide; one numeric value per keyword per date.	"marketing": 31

Notes:

• The actor removes the isPartial column from Google Trends data and returns only finalized values in the timeline array.
• You can download results from the Apify dataset in common formats such as JSON or CSV to power Google Trends data mining and reporting.

Key features

• 🚀 Reliable time-series extraction Collect clean, structured Google Trends time series data (interest over time) for one or more keywords in a single run.

• 🧠 pytrends-based engine Built on pytrends Google Trends, a proven approach to scrape Google Trends data without official API access.

• ⚙️ Configurable scope Control timeframe (e.g., "today 3-m", "today 12-m", "today 5-y") and geographic location (geo) with category support.

• 🌐 Smart proxy fallback Starts with a direct connection and automatically falls back to datacenter or residential proxies if blocked.

• 🔁 Robust retry & backoff Handles rate limits and transient errors with multiple attempts and exponential backoff for higher success rates.

• 📦 Dataset-ready outputs Export Google Trends to CSV or JSON via the Apify dataset—perfect for dashboards, BI tools, and pipelines.

• 👨‍💻 Developer friendly Runs on Apify infrastructure with an API-ready dataset, making it simple to integrate into your Google Trends automation script or CI workflows.

• 🧰 Practical Google Trends API alternative Scrape Google Trends without API credentials and automate recurring analyses.

How to use Google Trends Scraper - step by step

1. Create or log in to your Apify account
   Get instant access to run actors and manage datasets.

2. Open the Google Trends Scraper actor
   You can find it in the Apify Store by searching for “Google Trends Scraper”.

3. Add your input keywords
   In the keywords field, provide a list of terms you want to compare (supports multiple keywords).

4. Configure scope and options

   • timeRange: choose among "today 1-m", "today 3-m", "today 12-m", "today 5-y" or provide a custom date range.
   • geo: set a country code like "US" or leave empty for global.
   • category: set Google Trends category ID (0 for all).
   • proxyConfiguration: leave default (direct), or enable Apify Proxy if needed.

5. Start the run
   Click Start. The scraper fetches interest-over-time data and logs expected data points based on your timeRange.

6. Monitor progress
   The actor will retry on transient errors and automatically attempt proxy fallbacks if access is blocked.

7. Download your results
   Access the run’s dataset and download as JSON or CSV to analyze locally or connect via API for downstream automation.

Pro Tip: Chain the dataset to your internal pipelines to download Google Trends data on a schedule and feed forecasting models or SEO dashboards.

Use cases

Use case name	Description
SEO + content timing	Analyze Google Trends time series data to plan content around peaks and seasonality with high confidence.
Bulk keyword research	Run Google Trends bulk keyword research by supplying multiple terms and exporting structured timelines.
Market demand tracking	Scrape Google Trends data over different ranges (days, months, years) to monitor demand shifts.
Product seasonality analysis	Quantify seasonal effects to optimize inventory, promotions, and campaign timing.
Competitive interest comparison	Compare brand or topic interest trajectories to inform positioning and messaging.
Academic & research projects	Export Google Trends to CSV for statistical analysis and reproducible research workflows.
Automation pipelines	Treat it as a Google Trends API alternative and connect the dataset to internal ETL or scripts via API.

Why choose Google Trends Scraper?

Built for precision, automation, and reliability, this actor focuses on clean interest-over-time extraction without browser hacks or unstable workarounds.

• 🎯 Accurate time-series output focused on interest over time
• ⚡ Scales from one-off checks to repeated monitoring with robust retries
• 🔌 Developer-first: API-ready dataset for integrations and scripts
• 🌍 Proxy fallback: direct → datacenter → residential when needed
• 🛡️ Ethical by design: scrapes public, aggregated Google Trends data only
• 💾 Easy exports: JSON/CSV from Apify datasets without extra setup
• 🧱 Production-ready infrastructure: consistent performance for recurring jobs

In short, it’s a stable Google Trends scraper GitHub-style solution, delivered as a managed actor—great for teams that want a dependable Google Trends scraping tool.

Is it legal / ethical to use Google Trends Scraper?

Yes—when used responsibly. This tool retrieves public, aggregated information from Google Trends and does not access private or personal data.

Guidelines for responsible use:

• Scrape responsibly and avoid excessive load on upstream services.
• Respect terms of service and applicable laws (e.g., GDPR/CCPA compliance).
• Use data for analysis, research, and legitimate business intelligence.
• Verify your specific use case with your legal team for edge cases.

Input parameters & output format

Example input JSON

{
  "keywords": ["chatgpt", "AI", "python"],
  "timeRange": "today 3-m",
  "geo": "US",
  "category": 0,
  "sortOrder": "",
  "maxComments": 100,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Input parameter reference

Parameter	Type	Description	Default	Required
keywords	array	List of keywords or search terms to analyze trends for (supports bulk input).	—	Yes
timeRange	string	Time range for the trends data. Data granularity varies by range: • 'today 1-m' → ~30 daily data points • 'today 3-m' → ~90-93 daily data points • 'today 12-m' → ~52 weekly data points • 'today 5-y' → ~60 monthly data points • Custom: 'YYYY-MM-DD YYYY-MM-DD' (e.g., '2023-01-01 2023-12-31').	"today 3-m"	No
geo	string	Geographic location code (e.g., "BD" for Bangladesh, "US" for United States). Leave empty for global.	""	No
category	integer	Google Trends category ID (0 for all categories).	0	No
sortOrder	string	Sort order for results (optional).	""	No
maxComments	integer	Maximum number of comments to retrieve (optional).	100	No
proxyConfiguration	object	Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked.	{ "useApifyProxy": false }	No

Notes:

• The actor uses keywords, timeRange, geo, category, and proxyConfiguration during execution.
• When blocked, it will automatically attempt a proxy fallback (datacenter, then residential if needed).

Example output JSON

{
  "inputUrlOrTerm": "chatgpt, AI, python",
  "searchTerm": "chatgpt, AI, python",
  "interestOverTime_timelineData": [
    {
      "date": "2025-08-24",
      "chatgpt": 87,
      "AI": 65,
      "python": 42
    },
    {
      "date": "2025-08-25",
      "chatgpt": 96,
      "AI": 72,
      "python": 48
    },
    {
      "date": "2025-08-26",
      "chatgpt": 91,
      "AI": 68,
      "python": 45
    }
  ]
}

Output notes:

• interestOverTime_timelineData contains one row per time bucket (daily/weekly/monthly) with a date and values for each input keyword.
• The isPartial flag from Google Trends is removed to simplify downstream use.

FAQ

Is the Google Trends Scraper free to use?

You can run the actor on Apify with platform-based pricing. The dataset output can be accessed and downloaded via the Apify UI or API based on your plan.

Can I scrape Google Trends without an official API?

Yes. This actor uses pytrends under the hood, making it a practical Google Trends API alternative that lets you scrape Google Trends data programmatically.

Does it support Python or API-based workflows?

Yes. You can integrate via the Apify API to download results and plug them into Python scripts or pipelines, enabling a smooth Google Trends automation script.

How many keywords can I include?

The input accepts an array of keywords. Google Trends places limitations on comparisons, so use concise keyword sets per run to ensure stable results.

What kinds of data does it return?

This version focuses on interest-over-time time series data for your keywords. It outputs a timeline with date values and the corresponding trend indices per keyword.

Do I need proxies?

Not necessarily. The actor starts with a direct connection and only falls back to datacenter or residential proxies if it encounters blocks.

Can I export results to CSV or JSON?

Yes. After a run completes, you can download Google Trends data from the Apify dataset in JSON or CSV, ready for BI tools or spreadsheets.

Is using this scraper legal and safe?

Yes—when done responsibly. It collects public, aggregated data from Google Trends. Ensure your use complies with applicable laws and terms.

Final thoughts

Google Trends Scraper is built to extract clean, structured interest-over-time data from Google Trends for analysis and automation. It combines configurable timeframes and geos with robust retries and proxy fallback to deliver reliable datasets fast. Ideal for marketers, developers, analysts, and researchers, it helps you scrape Google Trends data at scale and export Google Trends to CSV/JSON for dashboards and models. Developers can pull results via the Apify API and integrate them into pipelines. Start extracting smarter Google Trends data and turn time series into insights.