Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is a production-ready Google Trends data scraper that automates interest-over-time collection for any list of keywords. It solves the hassle of manual checks by letting you specify time range, geography, and category, then delivers clean time series for analysis. Built as a Google Trends scraper Python tool with robust Google Trends API scraping via pytrends, it helps marketers, developers, data analysts, and researchers automate Google Trends data extraction at scale for SEO, content planning, market analysis, and forecasting.

What data / output can you get?

This actor outputs a single structured record per run with a timeline array that includes one object per time point. Field names and structure mirror the implementation.

Data type	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords	"chatgpt, AI, python"
searchTerm	Same as inputUrlOrTerm for convenience	"chatgpt, AI, python"
interestOverTime_timelineData	Array of timeline objects (date + one numeric field per keyword)	[ … ]
date (inside interestOverTime_timelineData)	ISO date string for the data point	"2025-08-24"
chatgpt (inside interestOverTime_timelineData)	Interest score for keyword “chatgpt” at that date	87
AI (inside interestOverTime_timelineData)	Interest score for keyword “AI” at that date	65
python (inside interestOverTime_timelineData)	Interest score for keyword “python” at that date	42

Notes:

• interestOverTime_timelineData is an array of objects. Each object contains “date” and one numeric field per input keyword. The internal isPartial flag is removed for a clean timeline.
• Data granularity depends on timeRange (daily/weekly/monthly as described in the input schema).
• Export your results from the Apify dataset as JSON, CSV, or Excel for Google Trends CSV export, Google Trends bulk download, and downstream analysis.

Key features

• 🚀 Robust interest-over-time extraction
  Clean, comparable time series for multiple keywords using a reliable Google Trends API scraping approach powered by pytrends.

• 🎛️ Configurable time ranges, geo, and category
  Control timeframe, geographic location (global or country code), and Google Trends category ID directly through input parameters to scrape Google Trends by country and context.

• 🔁 Smart retry & backoff
  Built-in retries with exponential backoff handle transient errors and rate limits automatically for consistent Google Trends data downloader workflows.

• 🧰 Proxy fallback for reliability
  Starts without a proxy and falls back to datacenter, then residential proxies if blocked—ensuring resilient runs at scale.

• 🧑‍💻 Developer-friendly (Python + Apify SDK)
  Implemented in Python with the Apify SDK and pytrends, making it easy to integrate into Google Trends API Python pipelines and automation.

• 💾 Structured dataset outputs
  Results are pushed to the Apify dataset with consistent keys, ready for export to JSON, CSV, or Excel and use in BI tools, notebooks, and dashboards.

• ⚙️ Easy bulk comparisons
  Provide an array of keywords to compare trends side-by-side in a single timeline dataset for keyword research.

• 🛡️ Production-ready infrastructure
  Designed for stability on Apify’s cloud with logging, error handling, and proxy fallback baked in.

How to use Google Trends Scraper - step by step

1. Create or log in to your Apify account
   Start from the Apify Console to run the actor.

2. Open the Google Trends Scraper actor
   Search for “Google Trends Scraper” and click Try for free.

3. Add your keywords
   In the input, provide an array of keywords under keywords (e.g., ["chatgpt", "AI", "python"]) for Google Trends keyword research scraper comparisons.

4. Set time range, geo, and category

• timeRange: Choose “today 1-m”, “today 3-m”, “today 12-m”, “today 5-y”, or provide a custom "YYYY-MM-DD YYYY-MM-DD" string as described in the schema for historical data.
• geo: Leave empty for global or set a country code like "US" to scrape Google Trends by country.
• category: Use 0 for all categories or a specific Google Trends category ID.

5. Configure proxy (optional)
   By default, the actor starts without a proxy and will fallback to datacenter/residential if blocked. You can explicitly pass proxyConfiguration if needed.

6. Run the actor
   Click Start. The run will fetch interest-over-time data, retry on transient errors, and switch proxies if necessary.

7. Download your results
   Open the run’s Dataset tab and export results as JSON, CSV, or Excel. Use this in Python, BI tools, or spreadsheets to automate Google Trends data analysis.

Pro Tip: Trigger runs via the Apify API to build a scheduled Google Trends web scraping pipeline or integrate exports into a serverless job for Google Trends historical data download.

Use cases

Use case name	Description
SEO & content planning – keyword momentum	Track how target keywords rise or fall to guide content calendars and prioritization with a Google Trends data extraction workflow.
Market research – demand tracking	Quantify shifts in consumer interest across time ranges to validate product decisions and messaging.
Product analytics – seasonality checks	Measure recurring patterns to align campaigns, launches, and inventory with known peaks and troughs.
Data science & forecasting – time series	Feed structured interest-over-time signals into forecasting models, notebooks, and dashboards.
Competitive monitoring – topic comparisons	Compare brands or topics in one run for a clear picture of relative popularity over time.
Academic & policy research – longitudinal trends	Collect consistent historical timelines for publishable analyses and studies.
Automation pipelines – API integration	Schedule runs and export datasets for end-to-end Google Trends automation with Python or low-code tools.

Why choose Google Trends Scraper?

This Google Trends scraper tool is engineered for precision, automation, and reliability on Apify.

• 🎯 Accurate time-series output aligned to your inputs (keywords, timeRange, geo, category)
• 🌍 Scrape Google Trends by country with the geo parameter for location-specific timelines
• 📈 Scales for multi-keyword comparisons in a single, structured dataset
• 🧑‍💻 Developer access via Python (pytrends) with Apify SDK-friendly architecture
• 🛡️ Reliable proxy strategy with automatic fallback from no-proxy → datacenter → residential
• 💾 Easy exports for BI tools and reporting (JSON, CSV, Excel)
• ⚖️ Safer and more consistent than brittle browser extensions or manual copy-paste approaches

Bottom line: A dependable Google Trends data extractor purpose-built for production workflows.

Is it legal / ethical to use Google Trends Scraper?

Yes—when used responsibly. This actor extracts public, aggregated information from Google Trends and does not access private or authenticated data.

Guidelines for compliant use:

• Scrape only publicly available, aggregated trend data
• Respect platform terms and avoid excessive request rates
• Use outputs for analysis and lawful business or research purposes
• Verify compliance with your legal team for edge cases or large-scale deployments

Input parameters & output format

Below is the exact input schema supported by the actor and an example of the resulting output structure.

Example input JSON

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

Input parameters

• keywords (array of string)
  Description: List of keywords or search terms to analyze trends for (supports bulk input).
  Default: none (prefill: ["chatgpt", "AI"])
  Required: yes

• timeRange (string)
  Description: 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')
  Default: "today 3-m"
  Required: no

• geo (string)
  Description: Geographic location code (e.g., 'BD' for Bangladesh, 'US' for United States). Leave empty for global.
  Default: ""
  Required: no

• category (integer)
  Description: Google Trends category ID (0 for all categories).
  Default: 0
  Required: no

• sortOrder (string)
  Description: Sort order for results (optional).
  Default: ""
  Allowed values: "", "relevance", "date"
  Required: no

• maxComments (integer)
  Description: Maximum number of comments to retrieve (optional).
  Default: 100 (min 1, max 1000)
  Required: no

• proxyConfiguration (object)
  Description: Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked.
  Default: none (prefill: { "useApifyProxy": false })
  Required: no

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
    }
  ]
}

Notes:

• The interestOverTime_timelineData array contains one object per date with numeric values for each input keyword.
• The actor removes the isPartial flag internally to keep the timeline clean.
• Optional parameters like sortOrder and maxComments are accepted by the input schema but are not used in the time-series extraction logic.

FAQ

Is the Google Trends Scraper free to use?

You can run it on Apify with free trial resources and then upgrade as needed. Pricing depends on your plan and usage.

Can I scrape Google Trends by keyword with multiple terms?

Yes. Provide an array of keywords in the keywords field, and the output will include a timeline value for each term, making side-by-side comparisons easy.

Does this extract related queries, related topics, or regional breakdowns?

No. The current version focuses on interest-over-time time series for your input keywords. It does not output related queries, related topics, or regional interest.

Which time ranges are supported?

The input supports “today 1-m”, “today 3-m”, “today 12-m”, “today 5-y”, and a custom range in the "YYYY-MM-DD YYYY-MM-DD" format per the schema description. Granularity varies by range.

Can I scrape Google Trends by country?

Yes. Set the geo code (e.g., "US") to build the interest-over-time series for that location. Leaving geo empty returns global trends.

Is this a Google Trends scraper Python solution?

Yes. It’s implemented in Python and uses the pytrends library under the hood, making it suitable for Google Trends API Python workflows and automation.

How do I download Google Trends data from a run?

Open the run’s Dataset and export in JSON, CSV, or Excel. This supports Google Trends CSV export, Google Trends bulk download, and downstream analysis.

How reliable is it for large workloads?

It includes retries with exponential backoff and a proxy fallback strategy (no-proxy → datacenter → residential) to maximize success rates during repeated or higher-volume runs.

Closing CTA / Final thoughts

The Google Trends Scraper is built to extract clean, comparable interest-over-time data for any set of keywords. With configurable time ranges, geo, and category plus resilient proxy handling, it delivers structured datasets ready for SEO research, market analysis, and forecasting. It’s ideal for marketers, developers, data analysts, and researchers—whether you need manual exports or to automate Google Trends data with the Apify API in Python pipelines. Start extracting smarter trend insights today and stay ahead of the curve.