Google Trends Scraper

Google Trends Scraper

The Google Trends Scraper is a focused Google Trends data scraper that collects interest-over-time time series directly from Google Trends using a pytrends scraper under the hood. It solves the repetitive task of manually checking trend charts by exporting a clean, comparable time series for multiple keywords so you can analyze seasonality, momentum, and trend shifts at a glance. Built for marketers, developers, data analysts, and researchers, this Google Trends scraper tool enables scalable Google Trends data extraction and Google Trends automation with simple inputs and reliable outputs.

What data / output can you get?

This actor outputs a single, structured record per run with a consolidated time series for all requested keywords. The dataset can be exported for Google Trends CSV download or JSON for downstream analytics.

Data type	Description	Example value
inputUrlOrTerm	Comma-separated list of input keywords for reference	"chatgpt, AI, python"
searchTerm	Echo of the search terms used (same as inputUrlOrTerm)	"chatgpt, AI, python"
interestOverTime_timelineData	Array of time-series points with one row per date	[ { "date": "2025-08-24", "chatgpt": 87, "AI": 65, "python": 42 }, … ]
interestOverTime_timelineData[].date	ISO-formatted date for the data point	"2025-08-24"
interestOverTime_timelineData[].chatgpt	Trend value (0–100) for keyword “chatgpt” on that date	87
interestOverTime_timelineData[].AI	Trend value (0–100) for keyword “AI” on that date	65
interestOverTime_timelineData[].python	Trend value (0–100) for keyword “python” on that date	42

Notes:

• The timeline granularity depends on the selected time range (daily/weekly/monthly based on your input).
• You can export results to JSON, CSV, or Excel from the Apify dataset for easy Google Trends dataset download.

Key features

• 🚦 Robust proxy fallback Automatically starts with no proxy and falls back to datacenter or residential proxies if blocked. Stable extraction for reliable runs.

• 📈 Multi-keyword time series Provide a list of keywords to compare in a single run. The output contains one column per keyword, ideal for a Google Trends time series scraper.

• 🕒 Flexible time ranges Supports popular ranges like “today 1-m”, “today 3-m”, “today 12-m”, and “today 5-y”, plus custom YYYY-MM-DD ranges for precise Google Trends data extraction.

• 🌐 Geographic and category filters Configure geo (e.g., US) and category ID to refine the dataset you collect.

• 🐍 Google Trends API Python via pytrends Powered by pytrends, a popular Google Trends API Python library, making this a practical Google Trends scraping Python workflow.

• 💾 Easy exports Download clean datasets from Apify in JSON, CSV, or Excel for BI tools, dashboards, or further processing in Python.

• 🔁 Resilient retries and backoff Built-in retry logic with exponential backoff helps handle transient errors and rate limits smoothly.

• ⚙️ Production-ready on Apify Runs in the Apify cloud with logging, dataset storage, and one-click exports—ideal for Google Trends automation.

How to use Google Trends Scraper - step by step

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

2. Open the Google Trends Scraper Find the “Google Trends Scraper” actor and click Try it.

3. Add your input data

   • keywords: Provide a list of keywords (supports multiple terms).
   • timeRange: Choose from presets like “today 3-m” or set a custom date range.
   • geo: Set a country code (leave empty for global).
   • category: Provide a Google Trends category ID (0 for all categories).
   • proxyConfiguration: Choose whether to use Apify Proxy (starts with no proxy and falls back if needed). Optional fields available in the input (sortOrder, maxComments) do not affect the time series output and can be left at defaults.

4. Configure time range and scope Select a timeRange that matches your analysis needs. The actor automatically aligns data granularity to the range (e.g., daily for “today 3-m”, weekly for “today 12-m”).

5. Run the actor Click Start. The scraper will fetch interest over time for your keywords, handle retries, and apply proxy fallback if necessary.

6. Monitor logs The run logs display the detected date range, number of data points, and any retry/backoff actions taken.

7. Download results Go to the Dataset tab and export your results as JSON, CSV, or Excel for analysis, dashboards, or pipelines.

Pro Tip: Use the same keyword set across different timeRange values in separate runs to create multi-horizon trend comparisons and export via Google Trends CSV download for your BI stack.

Use cases

Use case name	Description
SEO & content planning	Compare keyword momentum over time to prioritize topics and schedule posts when interest peaks.
PPC & paid media optimization	Track relative interest to adjust bids and budgets around seasonal or trending terms.
Market trend monitoring	Measure demand cycles and trend shifts with a Google Trends time series scraper for strategic decisions.
Product research	Validate interest in features or product ideas by studying long-term trendlines.
Data science time-series analysis	Feed normalized 0–100 trend series into forecasting models and anomaly detection.
Academic & research projects	Collect reproducible, aggregated public data for longitudinal studies.
Dashboarding & reporting	Export datasets for recurring analysis in Python, Excel, or BI tools using a Google Trends exporter workflow.

Why choose Google Trends Scraper?

Positioned for precision and reliability, this actor focuses on clean, comparable interest-over-time data with robust infrastructure.

• ✅ Accurate, structured output — A single consolidated record with a clear time series for each keyword.
• 🐍 Developer-friendly — Built on pytrends for Google Trends scraping Python workflows.
• 🔐 Reliable operations — No-proxy start with automatic fallback to datacenter/residential proxies when blocked.
• 📦 Easy exports — One-click JSON/CSV/Excel downloads for immediate analysis and reporting.
• ⚙️ Cloud-native — Runs on Apify with logs, retries, and dataset storage—no browser extensions or fragile setups.
• 💡 Practical scope — Avoids unstable features and focuses on interest-over-time data you can trust for analysis.

In short: a focused Google Trends API workflow vs browser-extension alternatives—stable, structured, and production-ready.

Is it legal / ethical to use Google Trends Scraper?

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

Guidelines for responsible use:

• Use data in compliance with applicable laws and Google’s terms.
• Avoid excessive request rates; rely on built-in retries and proxy fallback.
• Do not attempt to access private or authenticated resources.
• Consult your legal team for edge cases or large-scale 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
  }
}

Parameters

Field	Type	Required	Default	Description
keywords	array of strings	Yes	—	List of keywords or search terms to analyze trends for (supports bulk input).
timeRange	string	No	"today 3-m"	Time range for the trends data. Granularity varies by range. Presets: "today 1-m", "today 3-m", "today 12-m", "today 5-y". Custom: "YYYY-MM-DD YYYY-MM-DD".
geo	string	No	""	Geographic location code (e.g., "BD" for Bangladesh, "US" for United States). Leave empty for global.
category	integer	No	0	Google Trends category ID (0 for all categories).
sortOrder	string	No	""	Sort order for results (optional).
maxComments	integer	No	100	Maximum number of comments to retrieve (optional).
proxyConfiguration	object	No	{ "useApifyProxy": false }	Configure proxy settings. Actor starts with no proxy and falls back 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 contains one entry per date with a value for each keyword (0–100). Fields may vary based on the keywords you provide.
• Exports are available from the dataset for Google Trends CSV download, JSON, or Excel.

FAQ

Is there a free tier or trial?

Yes. On Apify, actors can be tested with trial minutes; this listing includes trial minutes so you can evaluate the workflow before upgrading.

Does this scrape related queries, topics, or regional data?

No. This version focuses on interest over time only. It outputs a time series per keyword. If you need additional data types, you can combine multiple runs or extend your pipeline accordingly.

Can I use it with Python?

Yes. The actor uses pytrends (a popular Google Trends API Python library) under the hood. You can also export the dataset and process it further in your own Python scripts.

How many keywords can I compare?

You can pass multiple keywords in the keywords array to get parallel columns in the time series. The number of terms you include should align with Google Trends and pytrends constraints for reliable results.

Do I need to log in to Google?

No. The scraper operates without a Google login, using public Google Trends endpoints via pytrends.

Can I export the results to CSV?

Yes. After the run finishes, open the Dataset and export to CSV, JSON, or Excel for analysis in spreadsheets or BI tools.

How does it handle blocking or rate limits?

The actor starts without a proxy, retries with exponential backoff, and automatically falls back to datacenter or residential proxies if it detects blocking.

Is scraping Google Trends legal?

Yes—Google Trends provides aggregated public data. Use the tool responsibly, comply with Google’s terms and applicable laws, and consult legal counsel for large-scale or specialized use cases.

Closing CTA / Final thoughts

The Google Trends Scraper is built to deliver clean, comparable interest-over-time time series for fast, data-driven decisions. With flexible inputs, resilient execution, and simple exports, it’s ideal for marketers, developers, analysts, and researchers who need a dependable Google Trends exporter. Use it as a streamlined Google Trends keyword scraper in your pipelines, export results for BI, or integrate with your Python workflows. Start extracting smarter trend signals and keep your strategy aligned with real market interest.