Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is a fast, reliable Google Trends data scraper that collects interest-over-time metrics for one or more keywords and returns clean, structured time-series data. It solves the manual effort of checking Google Trends by automating bulk keyword requests with configurable time ranges, geo filters, and category IDs. Built for marketers, developers, data analysts, and researchers, this Google Trends scraping tool enables repeatable, automation-ready extraction at scale using a Python-based (pytrends) backend.

What data / output can you get?

The actor outputs a single structured item per run with a joined keyword label and a timeline array of date-stamped interest values (0–100) for each input keyword. You can export results as JSON or CSV from the Apify dataset. Below are the field-level details with examples:

Data field	Description	Example value
inputUrlOrTerm	Comma-separated list of the input keywords, used as a label for the result item.	"chatgpt, AI, python"
searchTerm	Same as inputUrlOrTerm, mirrors the comma-separated keywords for convenience.	"chatgpt, AI, python"
interestOverTime_timelineData	Array of timeline points; each object contains date plus one column per keyword with normalized interest (0–100).	[{"date":"2025-08-24","chatgpt":87,"AI":65,"python":42}, …]
interestOverTime_timelineData[].date	Date of the data point. Granularity depends on the selected timeRange.	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Interest value for the “chatgpt” keyword at that date (0–100).	87
interestOverTime_timelineData[].AI	Interest value for the “AI” keyword at that date (0–100).	65
interestOverTime_timelineData[].python	Interest value for the “python” keyword at that date (0–100).	42

Notes:

• Timeline granularity adapts to the selected timeRange (daily/weekly/monthly).
• Export results to JSON or CSV directly from the Apify dataset.

Key features

• 🚦 Robust proxy fallback & retries
  Built-in rate-limit and block handling with automatic fallback from no proxy → datacenter → residential, plus retry with backoff for resilient runs.

• 📆 Configurable time ranges with smart granularity
  Choose standard ranges like “today 1-m”, “today 3-m”, “today 12-m”, or “today 5-y” and the scraper fetches daily, weekly, or monthly points accordingly.

• 🧪 Bulk keyword support
  Provide a list of keywords to compare side-by-side in a single pull — ideal for bulk Google Trends keyword scraping.

• 🌍 Geo and category filters
  Set a geographic location code (e.g., “US”) and a Google Trends category ID to refine data to your market and topic.

• 📦 Structured outputs for analytics
  Clean interest-over-time arrays ready for Google Trends data export CSV or JSON pipelines, dashboards, and modeling.

• 🐍 Python-powered, developer-friendly
  Built on pytrends Google Trends under the hood, making it easy to integrate into Google Trends Python scraper and Google Trends API Python workflows.

• ⚙️ Apify-native automation
  Run in Apify, schedule via platform tools, and connect dataset exports to your BI stack or automation workflows.

• 🧱 Production-ready reliability
  Clear logging (date range and data point counts), proxy management, and straightforward configuration for stable operations.

How to use Google Trends Scraper - step by step

1. Sign up or log in to Apify
   Create a free Apify account or log in to access the actor.

2. Open the Google Trends Scraper actor
   Find “Google Trends Scraper” in the Apify Store and click Try for free.

3. Add your input data

   • keywords: Provide a list of one or more keywords (e.g., ["chatgpt", "AI", "python"]).
   • timeRange: Select a supported range (e.g., "today 3-m").
   • geo: Optionally specify a country code (e.g., "US") or leave empty for global.
   • category: Optionally set a Google Trends category ID (0 for all categories).
   • proxyConfiguration: Optionally use Apify Proxy or run without proxy (default).

4. Review advanced fields
   The input schema includes sortOrder and maxComments for compatibility; they are optional and not required for interest-over-time extraction.

5. Run the actor
   Click Start. The run logs will show the configuration, expected granularity, and how many data points were fetched.

6. Monitor progress
   The actor automatically handles rate limits and blocking with retry and proxy fallback if needed.

7. Download your results
   Open the run’s Dataset and export to JSON or CSV for downstream analysis in your Google Trends data extraction workflows.

Pro tip: Automate recurring pulls and wire the dataset to your pipelines for a hands-off Google Trends downloader setup in BI dashboards or code-based ETL.

Use cases

Use case	Description
SEO keyword seasonality mapping	Analyze weekly or monthly swings to prioritize topics and publish when interest peaks.
Content planning at scale	Compare multiple topics in one run to plan editorial calendars with a Google Trends scraping tool.
PPC trend checks	Validate ad themes against recent search interest changes before scaling budgets.
Regional demand analysis	Filter by geo to assess market-by-market interest and tailor campaigns accordingly.
Competitive keyword benchmarking	Compare brands or topics side-by-side using bulk Google Trends keyword scraping.
Data science feature engineering	Feed normalized 0–100 signals into forecasting models with Google Trends data export CSV.
Academic & policy research	Study public interest over time for societal topics and events.
API/ETL pipeline integration	Integrate structured outputs with Apify API or Python for automated reporting.

Why choose Google Trends Scraper?

A precision-built, automation-ready Google Trends data scraper that prioritizes reliability and clean outputs.

• 🎯 Accurate, structured time-series data ready for analysis
• ⚡ Scales from single keywords to bulk comparisons in one run
• 🧩 Developer-friendly (pytrends-powered) for Google Trends Python scraper workflows
• 🔐 Safe and compliant — collects only public, aggregated data from Google Trends
• 🔁 Reliable with retry, backoff, and proxy fallback baked in
• 🔗 Easy exports (JSON/CSV) for seamless BI and analytics integration
• 🧰 Better than manual browsing or unstable extensions — consistent, reproducible, and automation-ready

In short: a production-grade Google Trends scraping tool that delivers clean data without the headaches.

Is it legal / ethical to use Google Trends Scraper?

Yes — when used responsibly. The scraper accesses public, aggregated search interest data from Google Trends and does not collect personal or private information.

Guidelines for responsible use:

• Respect Google’s terms and avoid overloading services.
• Use proxies responsibly and only where necessary.
• Export and use data for analysis, research, and compliant business purposes.
• Verify compliance with your legal team for edge cases and jurisdiction-specific requirements.

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
  }
}

Parameter reference:

• keywords (array of string) — List of keywords or search terms to analyze trends for (supports bulk input). Required: Yes. Default: none (prefill: ["chatgpt", "AI"]).
• 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' Required: No. Default: "today 3-m".
• geo (string) — Geographic location code (e.g., "BD", "US"). Leave empty for global. Required: No. Default: "".
• category (integer) — Google Trends category ID (0 for all categories). Required: No. Default: 0.
• sortOrder (string) — Sort order for results (optional). Required: No. Default: "" (allowed: "", "relevance", "date").
• maxComments (integer) — Maximum number of comments to retrieve (optional). Required: No. Default: 100 (min 1, max 1000).
• proxyConfiguration (object) — Configure proxy settings. Actor starts with no proxy and can fallback to datacenter/residential if blocked. Required: No. Default prefill: {"useApifyProxy": false}.

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:

• interestOverTime_timelineData length and date granularity depend on timeRange.
• Keyword keys inside each timeline object exactly match your input keywords.
• Export as JSON or CSV from the dataset for downstream use.

FAQ

Is there a free tier or trial?

Yes. The actor can be tried on Apify, and you can run it with trial minutes. For ongoing usage, choose a plan that fits your volume.

Can I use this with Python?

Yes. The actor itself is a Google Trends Python scraper powered by pytrends, and you can integrate results with your Python pipelines or Apify API.

Does it use the Google Trends API?

The actor leverages the pytrends Google Trends library under the hood. You can orchestrate runs via the Apify API to fit “Google Trends API Python” workflows.

What data does it extract?

It returns interest over time for your keywords as a normalized 0–100 timeline, including date-stamped values per term. You can export the Google Trends data to CSV or JSON.

Can I filter by country or category?

Yes. Use the geo parameter for geographic filtering and category for Google Trends category ID selection.

How many keywords can I compare?

You can provide multiple keywords in the keywords array for bulk Google Trends keyword scraping. The actor processes them in a single payload and outputs side-by-side timelines.

Is scraping Google Trends legal?

Yes, when done responsibly. The actor accesses public, aggregated information and does not collect personal data. Always comply with platform terms and applicable laws.

Where do I download results?

After a run finishes, open the run’s Dataset in Apify and export as JSON or CSV for your Google Trends data export CSV workflows.

Final thoughts

The Google Trends Scraper is built to automate clean, structured interest-over-time data collection from Google Trends. With proxy fallback, configurable ranges, geo/category filters, and CSV/JSON exports, it fits marketers, developers, analysts, and researchers alike. Use it as a reliable Google Trends data extraction backbone in Apify, integrate via API or Python, and start extracting smarter trend insights at scale.