Google Trends Scraper

Google Trends Scraper

Google Trends Scraper is a fast, reliable Google Trends scraping tool that extracts interest-over-time data for one or more keywords. It solves the pain of manual trend checks by automating Google Trends data extraction with clean, structured output you can analyze at scale. Built on pytrends (a popular Google Trends Python scraper), this actor is ideal for marketers, developers, data analysts, and researchers who want to scrape Google Trends, run Google Trends keyword research automation, and power dashboards or models with historical trend timelines. With minimal setup, it enables repeatable, large-scale trend monitoring and comparison across keywords.

What data / output can you get?

Get a single, structured record per run with a human-readable keyword label and a complete timeline array. Each timeline entry includes the date and a 0–100 relative interest score for each keyword you provide.

Data type	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords (label for your run)	"chatgpt, AI, python"
searchTerm	Comma-separated list of the same keywords (alias for labeling)	"chatgpt, AI, python"
interestOverTime_timelineData	Array of timeline points with date and per-keyword interest values	[ { "date": "2025-08-24", "chatgpt": 87, "AI": 65, "python": 42 }, … ]
interestOverTime_timelineData[].date	Timeline date in YYYY-MM-DD format	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Interest score (0–100) for "chatgpt" on that date	87
interestOverTime_timelineData[].AI	Interest score (0–100) for "AI" on that date	65
interestOverTime_timelineData[].python	Interest score (0–100) for "python" on that date	42

Notes:

• The array length and granularity depend on the selected timeRange (daily/weekly/monthly).
• You can download Google Trends data from the Apify dataset in common formats like JSON or CSV for further analysis or to export Google Trends to CSV in your pipeline.

Key features

• 🔎 Accurate interest-over-time timelines Collect normalized 0–100 interest scores over time for each keyword in your list. Perfect for a Google Trends interest over time scraper focused on clean, comparable data.

• 🧪 Multi-keyword comparisons in one run Provide a list of keywords and get aligned timelines for side-by-side benchmarking. Ideal for Google Trends batch download workflows and keyword research automation.

• 🗓️ Configurable time ranges with clear granularity Choose from today 1-m, today 3-m, today 12-m, or today 5-y. The actor logs expected daily/weekly/monthly granularity so you know exactly what to expect.

• 🌍 Country-level targeting Set the geo code (e.g., "US") to scope results to a specific country, enabling localized trend analysis without extra setup.

• 🛡️ Resilient proxy fallback Starts with no proxy and automatically falls back to datacenter (GOOGLE_SERP) and then residential proxies if blocked. Retries include exponential backoff for reliability.

• 🐍 Developer-friendly Python stack Built with pytrends and the Apify SDK. A practical choice if you need a pytrends Google Trends scraper you can trigger via Apify and integrate into pipelines.

• 📤 Easy data export Results are pushed to the Apify dataset, ready to download Google Trends data as JSON or CSV and plug into BI tools or notebooks.

• ⚙️ Production-ready reliability Includes retry logic, informative logging (date range, data point counts, granularity), and structured JSON output for downstream automation.

How to use Google Trends Scraper - step by step

1. Create or log in to your Apify account
2. Open the Google Trends Scraper actor on Apify
3. Add input data
   • keywords: A list of search terms (e.g., ["chatgpt", "AI", "python"]) for multi-term comparisons
   • timeRange: Select one of "today 1-m", "today 3-m", "today 12-m", "today 5-y" (granularity varies)
   • geo: Country code like "US" or leave empty for global
   • category: Category ID (0 for all categories)
   • proxyConfiguration: Use default direct connection or enable Apify Proxy if needed
4. Review settings
   • Confirm the time range matches your granularity needs (daily/weekly/monthly)
   • Optionally set geo and category for scoped analysis
5. Run the actor
   • The scraper will fetch the time series and log the actual date range and number of data points
6. Monitor logs
   • You’ll see retry/backoff details and proxy fallback info if any blocking occurs
7. Download results
   • Open the run’s Dataset in Apify to export JSON or CSV for analysis in Python, R, or BI tools

Pro tip: For a Google Trends API scraper workflow, trigger this actor via the Apify API and automate Google Trends batch download jobs into your data warehouse or dashboards.

Use cases

Use case name	Description
SEO keyword research automation	Track interest over time for target keywords to prioritize topics and monitor demand shifts.
Content planning & timing	Identify seasonal peaks and schedule posts or videos when interest is highest.
Competitive benchmarking	Compare brand or product terms in one run to see relative momentum over time.
Market trend validation	Validate hypotheses with historical trend lines before campaigns or launches.
Data science time-series	Feed normalized 0–100 timelines into forecasting or anomaly detection models.
Reporting & dashboards	Export Google Trends to CSV/JSON and power BI dashboards for ongoing monitoring.
Academic research	Use reproducible interest timelines for studies that require transparent, public data sources.

Why choose Google Trends Scraper?

A precise, automation-ready Google Trends scraping tool built for stability and clean, structured output.

• 🎯 Accuracy-first output: Delivers normalized 0–100 timelines without noise or extra post-processing.
• 🧰 Developer access: Python-based internals using pytrends with Apify SDK orchestration for smooth integration.
• 🚀 Scales with your needs: Supports multiple keywords per run for quick comparisons and batch trend checks.
• 🛡️ Safe and resilient: Starts direct and falls back to datacenter/residential proxies with retries and backoff.
• 💾 Easy exports: One-click JSON/CSV downloads from Apify Datasets for fast ingestion downstream.
• 🔄 Better than brittle alternatives: Avoids extension-based approaches and unstable scraping patterns with production-ready logic.
• 💸 Predictable pricing: Flat monthly plan with a trial period (see FAQ) so teams can evaluate before committing.

In short, it’s a focused Google Trends interest over time scraper that prioritizes reliability, developer usability, and clean outputs.

Is it legal / ethical to use Google Trends Scraper?

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

Guidelines for compliant use:

• Scrape responsibly and avoid overloading services.
• Use the data for analysis and lawful business or research purposes.
• Be aware of and respect applicable terms of service and local laws (e.g., GDPR/CCPA where relevant).
• Verify edge cases with your legal team for enterprise deployments.

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 details:

• keywords (array of string)
  • Required: Yes (minItems: 1)
  • Default: None (example prefill: ["chatgpt", "AI"])
  • Description: List of keywords or search terms to analyze trends for (supports bulk input).
• timeRange (string)
  • Required: No
  • Default: "today 3-m"
  • Enum: "today 1-m", "today 3-m", "today 12-m", "today 5-y"
  • 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')
• geo (string)
  • Required: No
  • Default: ""
  • Description: Geographic location code (e.g., "BD" for Bangladesh, "US" for United States). Leave empty for global.
• category (integer)
  • Required: No
  • Default: 0
  • Description: Google Trends category ID (0 for all categories).
• sortOrder (string)
  • Required: No
  • Default: "" (enum: "", "relevance", "date")
  • Description: Sort order for results (optional). Note: This parameter is accepted but not used by the current version.
• maxComments (integer)
  • Required: No
  • Default: 100 (min: 1, max: 1000)
  • Description: Maximum number of comments to retrieve (optional). Note: This parameter is accepted but not used by the current version.
• proxyConfiguration (object)
  • Required: No
  • Default/Prefill: { "useApifyProxy": false }
  • Description: Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked.

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 timeline array contains one object per date with per-keyword interest values.
• The actor drops the internal "isPartial" column before producing output.

FAQ

Is Google Trends Scraper free?

There is a flat monthly plan, and the actor listing includes a trial period of 120 minutes so you can evaluate before subscribing. Check the Apify listing for the current $19.99/month plan and trial details.

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

No. This version focuses on interest-over-time timelines for your keywords. It does not fetch related queries/topics or region-by-region breakdowns.

Can I scrape Google Trends with Python or an API?

Yes. The actor is built with Python (pytrends) and runs on Apify. You can trigger it via the Apify API to build a Google Trends API scraper workflow or integrate it into pipelines.

How many keywords can I compare at once?

You can pass multiple keywords in the keywords array and the scraper will return aligned time series for each term in one run. The exact practical limit depends on Google Trends responsiveness.

What time ranges are supported and what granularity do I get?

Supported values are "today 1-m", "today 3-m", "today 12-m", and "today 5-y". Granularity varies by range (daily for 1–3 months, weekly for 12 months, monthly for 5 years). A custom 'YYYY-MM-DD YYYY-MM-DD' range is also supported.

Can I set a country or run global queries?

Yes. Use the geo parameter with a country code like "US". Leave it empty for global.

How do I download Google Trends data to CSV or JSON?

After the run finishes, open the run’s Dataset in Apify and export the results in JSON or CSV. This makes it easy to download Google Trends data and feed BI tools or notebooks.

Do I need login or cookies?

No. The actor uses pytrends and HTTP requests without requiring user authentication. It also includes proxy fallback and retry logic to improve stability.

Final thoughts

Google Trends Scraper is built for accurate, structured interest-over-time extraction at scale. It delivers clean timelines for multiple keywords, configurable time ranges, and resilient scraping with proxy fallback — ideal for marketers, developers, analysts, and researchers. Run it from Apify, export to JSON/CSV, or automate via the API to power your Google Trends data extraction pipeline. Start extracting smarter, comparable trend insights and turn them into actionable decisions.