Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is a production-ready Google Trends data scraper that collects structured interest-over-time timelines for one or more keywords. It solves the manual, repetitive task of checking trends by turning it into a repeatable Google Trends web scraper workflow you can run on schedule, compare terms, and download Google Trends data for analysis. Built for marketers, developers, data analysts, and researchers, this Google Trends Python scraper uses pytrends under the hood and outputs clean, comparable time series at scale.

What data / output can you get?

This actor outputs a single, structured record per run containing the combined keyword string and the full timeline array of normalized values for each keyword. Results are stored in the Apify dataset (exportable to JSON, CSV, or Excel).

Data field	Description	Example value
inputUrlOrTerm	Combined input keywords string (for traceability)	"chatgpt, AI"
searchTerm	Mirror of the combined keywords string	"chatgpt, AI"
interestOverTime_timelineData	Array of timeline rows (date + one column per keyword)	[{"date":"2025-08-24","chatgpt":87,"AI":65}, ...]
interestOverTime_timelineData[].date	Date for the row in YYYY-MM-DD format	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Normalized interest value (0–100) for keyword "chatgpt" on that date	87
interestOverTime_timelineData[].AI	Normalized interest value (0–100) for keyword "AI" on that date	65
interestOverTime_timelineData[].python	Normalized interest value (0–100) for keyword "python" on that date (if provided)	42

Notes:

• Timeline granularity depends on the timeRange: daily for “today 1-m” and “today 3-m”, weekly for “today 12-m”, monthly for “today 5-y”, and “custom” for a custom date range.
• You can export the dataset to JSON, CSV, or Excel directly from Apify for downstream BI tools and bulk Google Trends data download.

Key features

• ⚙️ Configurable timeframes & geo Choose daily, weekly, or monthly series by setting timeRange and optionally filter by geo and category. Ideal when you need a Google Trends historical data extractor with precise control.

• 🧪 Reliable pytrends integration Uses the pytrends TrendReq client to scrape Google Trends, making it a developer-friendly pytrends scraper and Google Trends Python scraper.

• 📈 Multi-keyword comparison Provide an array of keywords to compare in a single timeline output. Great for keyword research and a lightweight Google Trends keyword scraper workflow.

• 🔁 Resilient retries & backoff Built-in retry logic with exponential backoff helps stabilize runs during transient errors or rate limits.

• 🧰 Smart proxy fallback Starts without a proxy, then automatically falls back to Apify datacenter and residential proxies if blocked (no proxy → datacenter → residential), improving reliability over unstable alternatives.

• 📦 Structured dataset output Clean, predictable JSON with interestOverTime_timelineData that you can integrate into Google Trends automation scripts, pipelines, and dashboards.

• 🔌 Apify-native orchestration Run on a schedule, store results in datasets, and connect to APIs or workflows—ideal if you want to scrape Google Trends repeatedly and automate bulk data exports.

How to use Google Trends Scraper - step by step

1. Sign up or log in to Apify
   Create or access your Apify account to run the actor.

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

3. Add your keywords
   Enter one or more search terms in keywords (array). This supports bulk comparisons and makes it easy to scrape Google Trends for multiple topics.

4. Configure the basics

• Set timeRange (e.g., "today 3-m", "today 12-m", "today 5-y")
• Optionally specify geo (e.g., "US") and category (e.g., 0 for all)

5. Adjust proxy settings if needed
   Leave proxyConfiguration.useApifyProxy as false to start without a proxy; the actor will automatically fall back to datacenter/residential proxies if blocked.

6. Start the run
   Click Start. The actor will fetch the interest-over-time series and log expected data point counts based on your timeRange.

7. Download your results
   Open the run’s dataset and export to JSON, CSV, or Excel for analysis in Python notebooks, BI tools, or automation pipelines.

Pro Tip: Chain this Google Trends scraping tool to an automation workflow (e.g., via Apify API) to refresh dashboards or trigger alerts when trend lines change.

Use cases

Use case	Description
SEO & content planning	Compare search interest across target keywords to prioritize topics and publish when demand peaks.
PPC & keyword research	Validate demand trends before adding or pausing paid terms using normalized, comparable time series.
Market & product research	Track shifts in consumer interest over weeks, months, or years with a repeatable Google Trends data scraper.
Brand & competitor tracking	Benchmark brand interest versus competitors across time ranges with a simple Google Trends automation script.
Academic & data science	Use clean timeline JSON to build forecasting models or add external signals for research pipelines.
Dashboards & reporting	Schedule runs and export datasets to power Looker Studio, Power BI, or custom analytics.

Why choose Google Trends Scraper?

This scraper is built for precision, automation, and reliability—without the fragility of browser extensions or manual copy-paste.

• ✅ Accurate, structured timelines from pytrends
• ⚡ Scales to multi-keyword comparisons in a single run
• 🧩 Developer-friendly Python stack with Apify SDK
• 🔁 Robust retry logic with exponential backoff
• 🛡️ Automatic proxy fallback (none → datacenter → residential)
• 📤 Easy dataset exports to JSON/CSV/Excel for downstream tools
• 🔗 Ideal for API pipelines and workflow automation

In short, it’s a dependable Google Trends scraping tool that delivers consistent, ready-to-use data for serious analysis and automation.

Is it legal / ethical to use Google Trends Scraper?

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

Guidelines for compliant use:

• Use only public, aggregated data for research and analysis
• Respect platform terms and avoid aggressive request patterns
• Comply with applicable data regulations (e.g., GDPR, CCPA)
• Consult your legal team for edge cases or commercial deployments

Input parameters & output format

Below is the exact input schema supported by the actor, followed by a real example output.

Example input JSON

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

Parameters

• keywords (array of string, required)
  • Description: List of keywords or search terms to analyze trends for (supports bulk input).
  • Default: none (must provide at least 1)
• timeRange (string, optional)
  • 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"
  • Allowed values: "today 1-m", "today 3-m", "today 12-m", "today 5-y"
• geo (string, optional)
  • Description: Geographic location code (e.g., 'BD' for Bangladesh, 'US' for United States). Leave empty for global.
  • Default: ""
• category (integer, optional)
  • Description: Google Trends category ID (0 for all categories).
  • Default: 0
• sortOrder (string, optional)
  • Description: Sort order for results (optional).
  • Default: ""
  • Allowed values: "", "relevance", "date"
• maxComments (integer, optional)
  • Description: Maximum number of comments to retrieve (optional).
  • Default: 100
  • Minimum: 1, Maximum: 1000
• proxyConfiguration (object, optional)
  • Description: Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked.
  • Default: {"useApifyProxy": false}

Note: The actor reads keywords, timeRange, geo, category, and proxyConfiguration in its current implementation. The sortOrder and maxComments fields are present in the input schema but are not used by the current version of the actor.

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
    }
  ]
}

FAQ

Is there a free way to try this Google Trends Scraper?

Yes. You can run the actor on Apify and export results from the dataset, making it a straightforward way to download Google Trends data for evaluation.

Does this use the official Google Trends API?

This actor uses the pytrends library (TrendReq) as a Google Trends Python scraper under the hood, integrated with the Apify platform for orchestration and storage.

Can I compare multiple keywords at once?

Yes. Provide an array of terms in keywords and the actor will return a timeline with one column per keyword, enabling side-by-side comparisons with a single run.

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 format "YYYY-MM-DD YYYY-MM-DD". Granularity adjusts automatically (daily/weekly/monthly) based on your selection.

Does it return interest by region, related queries, or related topics?

No. The current version outputs the interest-over-time series only, as shown in the interestOverTime_timelineData array.

Can I run it without a proxy?

Yes. By default it starts with no proxy. If a block is encountered, it automatically falls back to Apify datacenter and then residential proxies for reliability.

How do I export the results?

Open the run’s dataset and export to JSON, CSV, or Excel. This makes the tool a practical Google Trends data export tool for dashboards and analysis.

Is it suitable for automation and pipelines?

Yes. It’s an Apify actor you can schedule, run via API, and integrate into pipelines as a Google Trends scraping tool for bulk data download and recurring reporting.

Final thoughts

The Google Trends Scraper is built to deliver clean, comparable interest-over-time timelines for any set of keywords. With configurable ranges, geo and category filters, resilient proxy fallback, and structured dataset output, it’s ideal for marketers, developers, analysts, and researchers. Use it to scrape Google Trends at scale, power automated reports, or feed models—then export your data in seconds via JSON/CSV/Excel. Start extracting smarter trend insights and build your next automation with confidence.