Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is a fast, reliable Google Trends data extractor that pulls clean interest-over-time timelines for your keywords directly from Google Trends. It eliminates manual checks by returning a normalized time series per keyword, configurable by time range, country, and category — ideal for marketers, developers, data analysts, and researchers. Built as a Google Trends scraper Python actor using pytrends, it enables automated, repeatable monitoring at scale so you can download Google Trends data and operationalize insights in your workflows.

What data / output can you get?

Below are the exact fields this actor saves to the Apify dataset for each run. The timeline array contains one item per date with a 0–100 score for each input keyword.

Data field	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords, echoed for traceability	"chatgpt, AI, python"
searchTerm	Same as inputUrlOrTerm, for compatibility with views	"chatgpt, AI, python"
interestOverTime_timelineData	Array of timeline points with date and per-keyword values	[{...}, {...}]
interestOverTime_timelineData[].date	Date for the trend sample in YYYY-MM-DD format	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Trend score for “chatgpt” at that date (0–100)	87
interestOverTime_timelineData[].AI	Trend score for “AI” at that date (0–100)	65
interestOverTime_timelineData[].python	Trend score for “python” at that date (0–100)	42
interestOverTime_timelineData[].	For every keyword you provide, a field with the same name appears in each timeline item	Integer 0–100

Notes:

• Granularity depends on timeRange and is determined automatically (daily for 1–3 months, weekly for 12 months, monthly for 5 years; custom if you provide dates).
• The actor drops partial flags and normalizes dates to YYYY-MM-DD.
• You can export results from the Apify dataset in JSON, CSV, or Excel formats to support Google Trends bulk download and export Google Trends to CSV.

Key features

• 🚀 Multi-keyword timeline extraction
  Build one payload for multiple keywords and get synchronized interest-over-time series in a single dataset record — perfect for Google Trends bulk download and keyword comparison.

• 🗓️ Configurable time ranges
  Choose “today 1-m”, “today 3-m”, “today 12-m”, “today 5-y”, or supply a custom range (YYYY-MM-DD YYYY-MM-DD) for historical data — a practical Google Trends historical data scraper use case.

• 🌍 Geographic and category filters
  Narrow results by country/region using geo (e.g., “US”) and by Google Trends category IDs.

• 🧰 Smart proxy fallback & retries
  Starts without a proxy for speed, then automatically falls back to datacenter and residential proxies if blocked, with exponential backoff and up to 3 retries — resilient Google Trends automation script behavior.

• 🧹 Clean, analysis-ready output
  Dates are normalized to YYYY-MM-DD and partial flags are removed, so you can chart and model right away.

• 💾 Flexible exports on Apify
  Access and download your dataset in JSON, CSV, or Excel formats to seamlessly download Google Trends data into BI tools and notebooks.

• 👩‍💻 Developer-friendly (Python + Apify SDK)
  Built with pytrends and the Apify Python SDK — a pytrends Google Trends scraper you can integrate into Python scripts, APIs, and pipelines.

• 🏗️ Production-ready reliability
  Robust logging, error handling, and proxy management designed for repeatable, scheduled runs.

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 your workspace.

2. 🔍 Open the Google Trends Scraper actor
   Find “Google Trends Scraper” in your Apify dashboard or the Apify Store.

3. 🧾 Add your input data

   • keywords: Provide a list of one or more keywords (array of strings).
   • timeRange: Select a range like “today 3-m” (default) or another supported option.
   • geo (optional): Add a country/region code (e.g., “US”) or leave empty for global.
   • category (optional): Set a Google Trends category ID (0 for all categories).

4. 🔐 Configure proxy settings (optional)
   Leave proxyConfiguration empty to start with no proxy. If needed, set useApifyProxy to true — the actor will fall back to datacenter and residential groups automatically when blocked.

5. ⚙️ Optional parameters
   sortOrder and maxComments are included in the input schema for compatibility but are not used by the current scraping logic.

6. ▶️ Run the actor
   Click Start. The actor fetches interest-over-time data for your keywords and selected configuration with automatic retries and proxy fallback.

7. 📤 View and export results
   Once complete, open the run’s Dataset. Export to JSON, CSV, or Excel for analysis, dashboards, or downstream automations.

Pro tip: Choose your timeRange intentionally — “today 3-m” yields ~90–93 daily points, “today 12-m” yields ~52 weekly points, and “today 5-y” yields ~60 monthly points.

Use cases

Use case name	Description
SEO trend monitoring	Track changing keyword interest daily/weekly/monthly to time content and campaigns.
Content planning	Prioritize topics backed by historical demand for higher engagement and organic reach.
Market research	Measure interest patterns across countries and categories to identify emerging opportunities.
Product demand tracking	Follow normalized trend signals (0–100) to inform merchandising and inventory decisions.
Academic & data science	Feed clean time series into models for forecasting, seasonality, and anomaly detection.
API/data pipeline integration	Pull from the Apify dataset API to power dashboards, alerts, and automated reports in your Google Trends scraping tool stack.

Why choose Google Trends Scraper?

Built for precision, automation, and reliability, this actor returns clean timelines you can trust for analysis and forecasting.

• 🎯 Accurate, normalized signals — Clean 0–100 scores and normalized dates ready for modeling.
• 🌎 Flexible filtering — Configure timeRange, geo, and category to match your research scope.
• 📦 Easy exports — Download results from the Apify dataset as JSON, CSV, or Excel.
• 👩‍💻 Developer access — Python-based implementation using pytrends and Apify SDK for seamless integration (great for Google Trends API Python workflows).
• 🛡️ Safe & responsible — Uses public Google Trends data; no login, cookies, or private data involved.
• ⚡ Resilient infrastructure — Automatic retries, backoff, and proxy fallback for stable runs at scale.
• 🔁 Production-ready alternative — Avoid copy-paste and unreliable extensions with a dependable Google Trends scraper GitHub alternative powered by Apify.

Is it legal / ethical to use Google Trends Scraper?

Yes — when done responsibly. This actor collects aggregated, public data from Google Trends and does not access personal or private information.

Guidelines for compliant use:

• Use data responsibly and in accordance with Google’s terms of service.
• Avoid excessive request rates or abusive behavior.
• Ensure your use complies with applicable laws and internal policies (e.g., GDPR, CCPA).
• Do not attempt to access private or authenticated data.
• For edge cases or large-scale deployments, consult your legal team.

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 (from the input schema):

• keywords (array of string) — List of keywords or search terms to analyze trends for (supports bulk input). Required: Yes. Default: none. Min items: 1.
• timeRange (string) — Time range for the trends data. Data granularity varies by range: ‘today 1-m’ → ~30 daily, ‘today 3-m’ → ~90–93 daily, ‘today 12-m’ → ~52 weekly, ‘today 5-y’ → ~60 monthly, or custom ‘YYYY-MM-DD YYYY-MM-DD’. Required: No. Default: "today 3-m". Options: ["today 1-m", "today 3-m", "today 12-m", "today 5-y"].
• geo (string) — Geographic location code (e.g., “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: "". Options: ["", "relevance", "date"]. Note: Not used by current scraping logic.
• maxComments (integer) — Maximum number of comments to retrieve (optional). Required: No. Default: 100. Note: Not used by current scraping logic.
• proxyConfiguration (object) — Configure proxy settings. Actor will start with no proxy and 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:

• Each timeline item includes a date and one field per input keyword with a 0–100 score.
• The actor drops partial flags and normalizes dates to “YYYY-MM-DD” for consistency.

FAQ

Is Google Trends Scraper free?

You can run the actor on Apify; usage depends on your plan and any actor-specific pricing. Check the Apify Store listing for current pricing and available trial minutes.

What data does this actor extract?

It returns interest-over-time timelines (0–100) for your input keywords, with one record containing a date field and a value per keyword for each time point.

Can I analyze multiple keywords at once?

Yes. Provide an array of keywords in the keywords input; the output includes a field for each keyword in every timeline item.

How granular is the data?

Granularity depends on timeRange: “today 1-m” yields ~30 daily points, “today 3-m” yields ~90–93 daily points, “today 12-m” yields ~52 weekly points, and “today 5-y” yields ~60 monthly points.

Can I filter by country or category?

Yes. Use geo to specify a country/region code (e.g., “US”), and category to set a Google Trends category ID (0 for all categories).

Does it handle proxies automatically?

Yes. The actor starts without a proxy and will fall back to datacenter and then residential proxies if it encounters blocks, with retry and backoff logic.

Can I export results to CSV or Excel?

Yes. Open the run’s Dataset on Apify to export your results in JSON, CSV, or Excel formats — an easy way to export Google Trends to CSV for analysis.

Is there Python or API support?

Yes. This is a Google Trends scraper Python implementation built with pytrends and the Apify SDK. You can pull results via the Apify dataset API to integrate with pipelines and dashboards.

Is it legal to scrape Google Trends?

Yes, when done responsibly. The actor accesses public, aggregated data and does not use private or authenticated sources. Ensure your usage complies with Google’s terms and applicable laws.

Closing CTA / Final thoughts

The Google Trends Scraper is built to deliver clean, reliable interest-over-time timelines for your keywords. With flexible time ranges, geo/category filters, and robust proxy fallback, it equips marketers, analysts, researchers, and developers with analysis-ready data. Export to JSON/CSV/Excel from the Apify dataset, or plug runs into your Python and API pipelines for a dependable Google Trends scraping tool. Start extracting smarter trend signals and turn search interest into actionable insights today.