# Google Trends Scraper — Interest, Related, Now (`scrape.badger/google-trends-scraper`) Actor
Scrape Google Trends at scale: interest over time, interest by region, related topics & queries, trending-now searches with category/hours/status/sort filters, unified explore (TIMESERIES / GEO\_MAP / RELATED\_TOPICS / RELATED\_QUERIES) and topic autocomplete.
- **URL**: https://apify.com/scrape.badger/google-trends-scraper.md
- **Developed by:** [Scrape Badger](https://apify.com/scrape.badger) (community)
- **Categories:** Developer tools, SEO tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $0.25 / 1,000 trends calls
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
### What does Google Trends Scraper do?
Pull [Google Trends](https://trends.google.com) data via the official internal API — interest over time, by region, related topics / queries, trending now, explore, and autocomplete. Seven modes, one actor.
### Why use Google Trends Scraper?
- **7 modes.** Interest Over Time / By Region / Related / Trending Now / Trending (legacy) / Explore / Autocomplete.
- **No consent-wall failures.** ScrapeBadger handles Google's recent consent redirect automatically.
- **GEO handled correctly.** Google's API rejects lowercase geo codes — we uppercase for you.
- **Time range, category, property filters.** Full Trends filter set exposed.
- **Dirt cheap.** $0.25 / 1k calls — ~17% under the official Apify actor.
### What data can Google Trends Scraper extract?
| Field | Type | Description |
|---|---|---|
| mode | string | Which Trends endpoint produced this record |
| query | string | Source query |
| geo | string | ISO country code (uppercased) |
| date / timestamp | string / number | Time-series index point |
| value / extracted_value | number | Interest score 0-100 |
| keyword / topic | string | Related-mode payload |
### How to scrape Google Trends
1. Click **Try for free**.
2. Pick `mode`: Interest Over Time, Interest By Region, Related, Trending Now, Trending (legacy), Explore, or Autocomplete.
3. Fill in the required input for that mode (usually `q`).
4. Optional: `geo` (country), `hl` (language), `date` (time range), `cat` (category), `gprop` (property).
5. Click **Start** — data points stream into the dataset.
### How much will it cost?
**$0.00025 per call (≈ $0.25 per 1,000 calls).** One call per mode invocation. Trending Now with `max_pages: 5` = 5 calls = $0.00125.
#### Competitor benchmark
| Actor | Author | Price | Notes |
|---|---|---|---|
| apify/google-trends-scraper | Apify | ~$0.30 / 1k results | Official |
| nexgendata/google-trends-scraper | nexgendata | ~$5 / 1k data points | Per-result pricing |
| consummate_mandala/google-trends-scraper | consummate_mandala | ~$0.75 / 1k results | Community |
| **scrape-badger/google-trends-scraper** | **ScrapeBadger** | **$0.25 / 1k calls** | **17% under official** |
### Input
Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.
| Field | Required | Description |
|---|---|---|
| mode | ✅ | One of 7 modes (see above). |
| q | mode-dependent | Search term. |
| geo | — | ISO country code (auto-uppercased). |
| hl | — | Language code. |
| date | — | Google time range: `today 12-m`, `today 5-y`, `2023-01-01 2023-12-31`, etc. |
| cat | — | Google category ID. |
| gprop | — | Property: web / images / news / youtube / froogle. |
| max_pages | Trending Now only | Pagination budget. |
### Output
Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.
Example record:
```json
{
"mode": "Interest Over Time",
"query": "machine learning",
"geo": "US",
"date": "Apr 20 \u2013 26, 2025",
"timestamp": 1745107200,
"value": 86,
"extracted_value": 86
}
````
### Tips / Advanced options
- **Interest values are relative, not absolute.** Google normalises to 0-100 within the time range. Don't compare values across different queries in separate runs.
- **Use Explore for widget tokens.** Explore returns tokens that other modes need for advanced queries.
- **Category filters dramatically change results.** E.g. `'python'` in Category 31 (Programming) vs. unfiltered.
- **Schedule daily for trending-topic tracking.** Trending Now updates every hour on Google's side.
### FAQ, Disclaimers, Support
#### Does this use the official Google Trends API?
Google Trends has no public official API — we use the same internal API their web UI calls, handled via ScrapeBadger's residential proxy pool to avoid rate limits.
#### Why `geo` uppercasing?
Google's internal API returns 400 Bad Request for lowercase `us`; only `US` works. The actor handles that for you.
#### What time ranges are supported?
All of Google Trends' presets: `today 1-H`, `today 4-H`, `today 1-d`, `today 7-d`, `today 1-m`, `today 3-m`, `today 12-m`, `today 5-y`, `all`. Custom ranges: `YYYY-MM-DD YYYY-MM-DD`.
#### Can I compare multiple queries?
Yes — Interest Over Time accepts comma-separated queries (up to 5, matching Google's UI limit).
#### Disclaimer
This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.
#### Support
Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).
#### Powered by
[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.
# Actor input Schema
## `mode` (type: `string`):
Pick the Trends call to run. Each mode has different required inputs — see descriptions below.
## `q` (type: `string`):
Search term — up to 5 comma-separated terms for comparison (e.g. python,javascript). Required for Interest Over Time, Interest By Region, Related Topics & Queries, Explore, Autocomplete. Ignored by Trending Now / Trending (legacy).
## `geo` (type: `string`):
ISO 3166-1 alpha-2 country code (will be uppercased). Empty = worldwide (not valid for Trending modes). Examples: US, GB, DE, LT.
## `hl` (type: `string`):
Language code. Used by Trending Now and Autocomplete. Example: en-US, de-DE.
## `date` (type: `string`):
Time range. Presets: now 1-H, now 4-H, now 1-d, now 7-d, today 1-m, today 3-m, today 12-m, today 5-y, all. Or custom range YYYY-MM-DD YYYY-MM-DD.
## `cat` (type: `integer`):
Numeric Google Trends category ID (0 = all categories). Applied only to Explore.
## `gprop` (type: `string`):
Google property to search. Empty = Web. Other: images, news, youtube, froogle. Applied only to Explore.
## `data_type` (type: `string`):
Which slice of Trends data the Explore mode should return.
## `tz` (type: `string`):
Timezone offset in minutes from UTC. Example: 0 (UTC), -240 (EDT), 120 (CEST). Used by Explore and Autocomplete.
## `hours` (type: `string`):
How far back to look for trending searches.
## `category` (type: `string`):
Filter trending searches by category.
## `status` (type: `string`):
all or active (only trends with non-zero search volume).
## `sort` (type: `string`):
How Trending Now results are ordered.
## `max_pages` (type: `integer`):
Currently applied only to Trending Now for forward-compatibility. Every mode = 1 API call per page.
## Actor input object example
```json
{
"mode": "Interest Over Time",
"q": "python,javascript",
"geo": "US",
"hl": "en-US",
"date": "today 12-m",
"cat": 0,
"gprop": "",
"data_type": "TIMESERIES",
"tz": "0",
"hours": "24",
"category": "all",
"status": "all",
"sort": "relevance",
"max_pages": 1
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"q": "python,javascript"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/google-trends-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "q": "python,javascript" }
# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-trends-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"q": "python,javascript"
}' |
apify call scrape.badger/google-trends-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrape.badger/google-trends-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google Trends Scraper — Interest, Related, Now",
"description": "Scrape Google Trends at scale: interest over time, interest by region, related topics & queries, trending-now searches with category/hours/status/sort filters, unified explore (TIMESERIES / GEO_MAP / RELATED_TOPICS / RELATED_QUERIES) and topic autocomplete.",
"version": "1.0",
"x-build-id": "wflyQwediWJABkRJg"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrape.badger~google-trends-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrape.badger-google-trends-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrape.badger~google-trends-scraper/runs": {
"post": {
"operationId": "runs-sync-scrape.badger-google-trends-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrape.badger~google-trends-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrape.badger-google-trends-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"mode"
],
"properties": {
"mode": {
"title": "Mode",
"enum": [
"Interest Over Time",
"Interest By Region",
"Related Topics & Queries",
"Trending Now",
"Trending (legacy)",
"Explore",
"Autocomplete"
],
"type": "string",
"description": "Pick the Trends call to run. Each mode has different required inputs — see descriptions below.",
"default": "Interest Over Time"
},
"q": {
"title": "Search term(s)",
"type": "string",
"description": "Search term — up to 5 comma-separated terms for comparison (e.g. python,javascript). Required for Interest Over Time, Interest By Region, Related Topics & Queries, Explore, Autocomplete. Ignored by Trending Now / Trending (legacy)."
},
"geo": {
"title": "Geo (country)",
"type": "string",
"description": "ISO 3166-1 alpha-2 country code (will be uppercased). Empty = worldwide (not valid for Trending modes). Examples: US, GB, DE, LT.",
"default": "US"
},
"hl": {
"title": "Language",
"type": "string",
"description": "Language code. Used by Trending Now and Autocomplete. Example: en-US, de-DE.",
"default": "en-US"
},
"date": {
"title": "Date range",
"type": "string",
"description": "Time range. Presets: now 1-H, now 4-H, now 1-d, now 7-d, today 1-m, today 3-m, today 12-m, today 5-y, all. Or custom range YYYY-MM-DD YYYY-MM-DD.",
"default": "today 12-m"
},
"cat": {
"title": "Category ID",
"minimum": 0,
"type": "integer",
"description": "Numeric Google Trends category ID (0 = all categories). Applied only to Explore.",
"default": 0
},
"gprop": {
"title": "Property",
"enum": [
"",
"images",
"news",
"youtube",
"froogle"
],
"type": "string",
"description": "Google property to search. Empty = Web. Other: images, news, youtube, froogle. Applied only to Explore.",
"default": ""
},
"data_type": {
"title": "Explore data type",
"enum": [
"TIMESERIES",
"GEO_MAP",
"GEO_MAP_0",
"RELATED_TOPICS",
"RELATED_QUERIES"
],
"type": "string",
"description": "Which slice of Trends data the Explore mode should return.",
"default": "TIMESERIES"
},
"tz": {
"title": "Timezone offset (minutes)",
"type": "string",
"description": "Timezone offset in minutes from UTC. Example: 0 (UTC), -240 (EDT), 120 (CEST). Used by Explore and Autocomplete.",
"default": "0"
},
"hours": {
"title": "Look-back window (hours)",
"enum": [
"4",
"24",
"48",
"168"
],
"type": "string",
"description": "How far back to look for trending searches.",
"default": "24"
},
"category": {
"title": "Trending category",
"enum": [
"all",
"business",
"entertainment",
"health",
"sci_tech",
"sports",
"top_stories"
],
"type": "string",
"description": "Filter trending searches by category.",
"default": "all"
},
"status": {
"title": "Trend status",
"enum": [
"all",
"active"
],
"type": "string",
"description": "all or active (only trends with non-zero search volume).",
"default": "all"
},
"sort": {
"title": "Sort order",
"enum": [
"relevance",
"search_volume",
"title",
"recency"
],
"type": "string",
"description": "How Trending Now results are ordered.",
"default": "relevance"
},
"max_pages": {
"title": "Max pages",
"minimum": 1,
"maximum": 10,
"type": "integer",
"description": "Currently applied only to Trending Now for forward-compatibility. Every mode = 1 API call per page.",
"default": 1
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```