# Google Trends Scraper (`apify/google-trends-scraper`) Actor Scrape data from Google Trends by search terms or URLs. Specify locations, define time ranges, select categories to get interest by subregion and over time, related queries and topics, and more. Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools. - **URL**: https://apify.com/apify/google-trends-scraper.md - **Developed by:** [Apify](https://apify.com/apify) (Apify) - **Categories:** SEO tools, News - **Stats:** 9,657 total users, 910 monthly users, 94.9% runs succeeded, 225 bookmarks - **User rating**: 2.73 out of 5 stars ## Pricing from $0.30 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ### ๐ŸŒ What is Google Trends Scraper? Our Google Trends Scraper crawls specified search queries and Google Trends URLs, and extracts data from Google Trends pages in structured formats such as JSON, CSV, XML, or Excel. With this Google Trends API, you will be able to extract the following Google trends data:
๐Ÿ” Search term ๐Ÿ™ Interest by city
๐ŸŒ Interest by subregion ๐Ÿ—บ Geo code and Geo name
๐Ÿ“Š Average interest over time ๐Ÿ“† Timeline of the interest over time
๐Ÿ”บ Rising related queries ๐Ÿ” Top related queries
โฌ†๏ธ Rising related topics โญ๏ธ Top related topics
### ๐ŸŒ How do I use Google Trends Scraper? Google Trends Scraper is designed to be user-friendly, even for those who have never extracted data from the web before. Hereโ€™s how you can use Google Trends Scraper to extract data from Google trends: 1. [Create](https://console.apify.com/sign-up) an Apify account 2. Open [Google Trends Scraper](https://apify.com/apify/google-trends-scraper). 3. Enter your search term, location, and time range. Or use a Google Trends URL with applied filters instead. 4. Click the โ€œStartโ€ button and wait for the data to be extracted. 5. Download your data in JSON, CSV, Excel, XML or HTML. If you need guidance on how to run the scraper, you can read our [step-by-step guide to scraping Google Trends](https://blog.apify.com/step-by-step-guide-to-scraping-google-trends/), it includes use cases, screenshots, and examples. Or [watch a short video tutorial](https://www.youtube.com/watch?v=qtgkqKAIxjk) โ–ท on YouTube. [Google Trends scraping tool](https://www.youtube.com/watch?v=qtgkqKAIxjk) ### ๐Ÿ“‰ What can you do with scraped Google Trends data? ๐Ÿ” **Back up your market research** by tracking the popularity of search terms and identifying trending topics ๐Ÿ“Š **Find business insights** by comparing the popularity of multiple search terms and see how they relate to each other ๐Ÿ“ **Generate content ideas** by exploring regional interest and see which areas are searching for specific topics the most ๐Ÿ’ก **Discover related queries and topics** to gain a deeper understanding of user interests ๐Ÿ•’ **Improve your SEO game** by analyzing search trends over time to identify seasonal patterns or long-term changes in user behavior ### โฌ‡๏ธย Input example You can scrape Google Trends either **by search query** or **by a Google Trends URL**. For a full explanation of input including an example in JSON, head over to the [input tab](https://apify.com/apify/google-trends-scraper/input-schema). You can set up the input programmatically or use the fields in scraperโ€™s interface: ### โฌ†๏ธย Output example The scraped Google Trends data will be shown as a dataset which you can find in the **Output** tab. Note that the output is organized as a table for viewing convenience: You can preview all the fields and choose in which format to download Google Trends data youโ€™ve extracted: JSON, Excel, HTML table, CSV, or XML. Here below is the same dataset in JSON: ```json { "inputUrlOrTerm": "web scraping", "searchTerm": "web scraping", "interestOverTime_timelineData": [ { "time": "1673136000", "formattedTime": "Jan 8โ€‰โ€“โ€‰14, 2023", "formattedAxisTime": "Jan 8, 2023", "value": [ 99 ], "hasData": [ true ], "formattedValue": [ "99" ] }, { "time": "1673740800", "formattedTime": "Jan 15โ€‰โ€“โ€‰21, 2023", "formattedAxisTime": "Jan 15, 2023", "value": [ 96 ], "hasData": [ true ], "formattedValue": [ "96" ] }, { "time": "1674345600", "formattedTime": "Jan 22โ€‰โ€“โ€‰28, 2023", "formattedAxisTime": "Jan 22, 2023", "value": [ 99 ], "hasData": [ true ], "formattedValue": [ "99" ] }, { "time": "1674950400", "formattedTime": "Jan 29โ€‰โ€“โ€‰Feb 4, 2023", "formattedAxisTime": "Jan 29, 2023", "value": [ 98 ], "hasData": [ true ], "formattedValue": [ "98" ] }, { "time": "1675555200", "formattedTime": "Feb 5โ€‰โ€“โ€‰11, 2023", "formattedAxisTime": "Feb 5, 2023", "value": [ 100 ], "hasData": [ true ], "formattedValue": [ "100" ] }, { "time": "1676160000", "formattedTime": "Feb 12โ€‰โ€“โ€‰18, 2023", "formattedAxisTime": "Feb 12, 2023", "value": [ 91 ], "hasData": [ true ], "formattedValue": [ "91" ] }, { "time": "1676764800", "formattedTime": "Feb 19โ€‰โ€“โ€‰25, 2023", "formattedAxisTime": "Feb 19, 2023", "value": [ 98 ], "hasData": [ true ], "formattedValue": [ "98" ] }, { "time": "1703376000", "formattedTime": "Dec 24โ€‰โ€“โ€‰30, 2023", "formattedAxisTime": "Dec 24, 2023", "value": [ 80 ], "hasData": [ true ], "formattedValue": [ "80" ] }, { "time": "1703980800", "formattedTime": "Dec 31, 2023โ€‰โ€“โ€‰Jan 6, 2024", "formattedAxisTime": "Dec 31, 2023", "value": [ 81 ], "hasData": [ true ], "formattedValue": [ "81" ] }, { "time": "1704585600", "formattedTime": "Jan 7โ€‰โ€“โ€‰13, 2024", "formattedAxisTime": "Jan 7, 2024", "value": [ 91 ], "hasData": [ true ], "formattedValue": [ "91" ], "isPartial": true } ], "interestOverTime_averages": [], "interestBySubregion": [], "interestByCity": [], "relatedTopics_top": [ { "topic": { "mid": "/m/07ykbs", "title": "Web scraping", "type": "Topic" }, "value": 100, "formattedValue": "100", "hasData": true, "link": "/trends/explore?q=/m/07ykbs&date=today+12-m" }, { "topic": { "mid": "/m/0828v", "title": "World Wide Web", "type": "Topic" }, "value": 97, "formattedValue": "97", "hasData": true, "link": "/trends/explore?q=/m/0828v&date=today+12-m" }, { "topic": { "mid": "/m/05z1_", "title": "Python", "type": "Programming language" }, "value": 29, "formattedValue": "29", "hasData": true, "link": "/trends/explore?q=/m/05z1_&date=today+12-m" }, { "topic": { "mid": "/m/026sq", "title": "Data", "type": "Topic" }, "value": 14, "formattedValue": "14", "hasData": true, "link": "/trends/explore?q=/m/026sq&date=today+12-m" }, { "topic": { "mid": "/m/085n4", "title": "Website", "type": "Topic" }, "value": 9, "formattedValue": "9", "hasData": true, "link": "/trends/explore?q=/m/085n4&date=today+12-m" }, { "topic": { "mid": "/m/0c828v", "title": "Selenium", "type": "Software" }, "value": 5, "formattedValue": "5", "hasData": true, "link": "/trends/explore?q=/m/0c828v&date=today+12-m" }, { "topic": { "mid": "/m/0z5n", "title": "Application programming interface", "type": "Type of software" }, "value": 5, "formattedValue": "5", "hasData": true, "link": "/trends/explore?q=/m/0z5n&date=today+12-m" }, { "topic": { "mid": "/m/022qrc8", "title": "Beautiful Soup", "type": "HTML parser" }, "value": 5, "formattedValue": "5", "hasData": true, "link": "/trends/explore?q=/m/022qrc8&date=today+12-m" }, { "topic": { "mid": "/m/07k1x", "title": "Tool", "type": "Topic" }, "value": 5, "formattedValue": "5", "hasData": true, "link": "/trends/explore?q=/m/07k1x&date=today+12-m" }, { "topic": { "mid": "/m/086nh", "title": "Web page", "type": "Topic" }, "value": 5, "formattedValue": "5", "hasData": true, "link": "/trends/explore?q=/m/086nh&date=today+12-m" }, { "topic": { "mid": "/m/0g57xn", "title": "Sentiment analysis", "type": "Field of study" }, "value": 50, "formattedValue": "+50%", "link": "/trends/explore?q=/m/0g57xn&date=today+12-m" } ], "relatedQueries_top": [ { "query": "python scraping", "value": 100, "formattedValue": "100", "hasData": true, "link": "/trends/explore?q=python+scraping&date=today+12-m" }, { "query": "python", "value": 96, "formattedValue": "96", "hasData": true, "link": "/trends/explore?q=python&date=today+12-m" }, { "query": "web scraping python", "value": 94, "formattedValue": "94", "hasData": true, "link": "/trends/explore?q=web+scraping+python&date=today+12-m" }, { "query": "web data scraping", "value": 39, "formattedValue": "39", "hasData": true, "link": "/trends/explore?q=web+data+scraping&date=today+12-m" }, { "query": "data scraping", "value": 38, "formattedValue": "38", "hasData": true, "link": "/trends/explore?q=data+scraping&date=today+12-m" }, { "query": "web scraping in python", "value": 18, "formattedValue": "18", "hasData": true, "link": "/trends/explore?q=web+scraping+in+python&date=today+12-m" }, { "query": "what is web scraping", "value": 17, "formattedValue": "17", "hasData": true, "link": "/trends/explore?q=what+is+web+scraping&date=today+12-m" }, { "query": "selenium web scraping", "value": 17, "formattedValue": "17", "hasData": true, "link": "/trends/explore?q=selenium+web+scraping&date=today+12-m" }, { "query": "selenium", "value": 16, "formattedValue": "16", "hasData": true, "link": "/trends/explore?q=selenium&date=today+12-m" }, { "query": "web scraping google", "value": 16, "formattedValue": "16", "hasData": true, "link": "/trends/explore?q=web+scraping+google&date=today+12-m" }, { "query": "api", "value": 16, "formattedValue": "16", "hasData": true, "link": "/trends/explore?q=api&date=today+12-m" }, { "query": "website scraping", "value": 16, "formattedValue": "16", "hasData": true, "link": "/trends/explore?q=website+scraping&date=today+12-m" }, { "query": "web scraping api", "value": 16, "formattedValue": "16", "hasData": true, "link": "/trends/explore?q=web+scraping+api&date=today+12-m" }, { "query": "web scraping using python", "value": 15, "formattedValue": "15", "hasData": true, "link": "/trends/explore?q=web+scraping+using+python&date=today+12-m" }, { "query": "web scraping with python", "value": 14, "formattedValue": "14", "hasData": true, "link": "/trends/explore?q=web+scraping+with+python&date=today+12-m" }, { "query": "web scraping tools", "value": 12, "formattedValue": "12", "hasData": true, "link": "/trends/explore?q=web+scraping+tools&date=today+12-m" }, { "query": "beautifulsoup", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=beautifulsoup&date=today+12-m" }, { "query": "beautifulsoup web scraping", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=beautifulsoup+web+scraping&date=today+12-m" }, { "query": "web scraping free", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=web+scraping+free&date=today+12-m" }, { "query": "web scraper", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=web+scraper&date=today+12-m" }, { "query": "scraper", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=scraper&date=today+12-m" }, { "query": "scrape", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=scrape&date=today+12-m" }, { "query": "web scrape", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=web+scrape&date=today+12-m" }, { "query": "python selenium web scraping", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=python+selenium+web+scraping&date=today+12-m" }, { "query": "python selenium", "value": 11, "formattedValue": "11", "hasData": true, "link": "/trends/explore?q=python+selenium&date=today+12-m" } ], "relatedQueries_rising": [ { "query": "chatgpt web scraping", "value": 4250, "formattedValue": "+4,250%", "link": "/trends/explore?q=chatgpt+web+scraping&date=today+12-m" }, { "query": "analisis data adalah", "value": 1150, "formattedValue": "+1,150%", "link": "/trends/explore?q=analisis+data+adalah&date=today+12-m" }, { "query": "analisis data", "value": 750, "formattedValue": "+750%", "link": "/trends/explore?q=analisis+data&date=today+12-m" }, { "query": "playwright", "value": 50, "formattedValue": "+50%", "link": "/trends/explore?q=playwright&date=today+12-m" }, { "query": "web scraping tool free", "value": 50, "formattedValue": "+50%", "link": "/trends/explore?q=web+scraping+tool+free&date=today+12-m" } ], "interestBy": [ { "geoCode": "SH", "geoName": "St. Helena", "value": [ 100 ], "formattedValue": [ "100" ], "maxValueIndex": 0, "hasData": [ true ] }, { "geoCode": "TN", "geoName": "Tunisia", "value": [ 60 ], "formattedValue": [ "60" ], "maxValueIndex": 0, "hasData": [ true ] }, { "geoCode": "SG", "geoName": "Singapore", "value": [ 56 ], "formattedValue": [ "56" ], "maxValueIndex": 0, "hasData": [ true ] }, { "geoCode": "PK", "geoName": "Pakistan", "value": [ 46 ], "formattedValue": [ "46" ], "maxValueIndex": 0, "hasData": [ true ] }, { "geoCode": "KM", "geoName": "Comoros", "value": [ 0 ], "formattedValue": [ "" ], "maxValueIndex": 0, "hasData": [ false ] }, { "geoCode": "IO", "geoName": "British Indian Ocean Territory", "value": [ 0 ], "formattedValue": [ "" ], "maxValueIndex": 0, "hasData": [ false ] } ] }] ```` ### ๐Ÿ“ย Want to try other Google scrapers? Use the dedicated scrapers below if you want to scrape specific public data from Google services. Each of them is built particularly for the relevant scraping case be it Google places, images, Google trending searches or Google SERP data. Feel free to browse them:
๐Ÿ” Google Search Results Scraper ๐Ÿ‘ Google Lens Scraper
๐Ÿ“ย Google Maps Scraper ๐Ÿ“ฉ Google Maps Email Extractor
๐Ÿ“‰ย Google Trending Searches ๐Ÿ“ฐ Google News Scraper
โญ๏ธGoogle Maps Reviews Scraper ๐Ÿค–ย AI Text Analyzer for Google Reviews
๐Ÿ’ผ Google Jobs Scraper ๐ŸŽฎ Google Play Scraper
### โ“FAQ #### Is there a Google Trends API? Google Trends does not offer an official API for public use. This means there isn't a dedicated Application Programming Interface provided by Google specifically for Google Trends data. But you can use scrapers and crawlers as a stand-in for a web scraping API, e.g. **๐Ÿ“ˆ๐Ÿ“‰[Google Trends Scraper](https://apify.com/apify/google-trends-scraper)**. #### Can I use Google Trends Scraper data with API? Yes, you can do so by using Apify API. It gives you programmatic access to the Apify platform. The API is organized around RESTful HTTP endpoints that enable you to manage, schedule, and run Apify Actors such as this one. The API also lets you access any datasets, monitor actor performance, fetch results, create and update versions, and more. To access the API using Node.js, use the `apify-client` [NPM package](https://apify.com/apify/google-trends-scraper/api/client/nodejs). To access the API using Python, use the `apify-client` [PyPI package](https://apify.com/apify/google-trends-scraper/api/client/python). Check out the Apify API reference docs for full details or click on the API tab for code examples and API Endpoints. #### Can I create a Google Trends integration using data from this scraper? Yes. Google Trends Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform. You can integrate with Make, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, LangChain and more. Or you can use webhooks to carry out an action whenever an event occurs, e.g. get a notification whenever Google Trends Scraper successfully finishes a run. #### Can I use this Google Trends API in Python? Yes, by using Apify API. To access the [Google Trends API using Python](https://apify.com/apify/google-trends-scraper/api/client/python), use the `apify-client` PyPI package. You can find more details about the client [in our Docs](https://docs.apify.com/api/client/python/). #### Is it legal to scrape Google Trends data? It is legal to scrape publicly available data from Google Trends. However, you should always review and comply with Google's terms of service and any applicable laws and regulations regarding web scraping. Additionally, be mindful of any rate limits or restrictions imposed by Google to avoid potential issues. #### Not your cup of tea? Build your own scraper. Google Trends Scraper doesnโ€™t exactly do what you need? You can always build one of your own! We have various [scraper templates](https://apify.com/templates) in Python, JavaScript, and TypeScript to get you started. Alternatively, you can write it from scratch using our [open-source library Crawlee](https://crawlee.dev/). You can keep the scraper to yourself or make it public by adding it to Apify Store (and [find users](https://apify.com/partners/actor-developers) for it). #### Your feedback Weโ€™re always working on improving the performance of our Actors. So if youโ€™ve got any technical feedback for Google Trends Scraper or simply found a bug, please create an issue on the Actorโ€™sย [Issues tab](https://console.apify.com/actors/DyNQEYDj9awfGQf9A/console)ย in Apify Console. # Actor input Schema ## `searchTerms` (type: `array`): (Required if 'spreadsheetId' is not provided) List of search terms to be scraped. ## `isMultiple` (type: `boolean`): If checked comma will be handled as search for multiple terms ## `timeRange` (type: `string`): Choose a predefined search's time range (defaults to 'Past 12 months') ## `geo` (type: `string`): Get results from a specific geo area (defaults to 'Worldwide'). This maps to different fields in the dataset results: smaller countries like Greece return just 'interestByCity' data, while larger ones like the United States return 'interestBySubregion' data as well. When omitted (Worldwide), 'interestBy' is returned instead, which corresponds to the 'Interest by area' section on Google Trends. Note: When using Google Trends URLs from the '๐Ÿ”— Use a Google Trends URL instead of search terms' section, the geo parameter from the URL's query string takes precedence over this setting. You can also use start URLs to retrieve 'interestByMetro' data for metropolitan areas by providing URLs with more specific geo regions like California (US) directly copied from Google Trends. ## `viewedFrom` (type: `string`): Country for residential proxies to ensure correct results ## `skipDebugScreen` (type: `boolean`): By default snapshot of each visited Google Trends page saved to KV Store ## `startUrls` (type: `array`): Copy a Google Trends URL and paste it here. ## `spreadsheetId` (type: `string`): (Optional) Id of the google sheet from where search terms will be loaded. Spreadsheet must have only one column. Row 1 is considered the title of the column so it will not be loaded as a search term. The provided spreadsheet must be publicly available. Check the actor documentation for more info. ## `category` (type: `string`): Choose a category to filter the search for (defaults to 'All categories') ## `maxItems` (type: `integer`): Limit of product items to be scraped. Zero value means no limit. ## `customTimeRange` (type: `string`): Provide custom time range. If present, it takes precedence over regular timeRange. Format is: `YYYY-MM-DD YYYY-MM-DD`. See examples in the documentation. ## `maxConcurrency` (type: `integer`): How many pages will be opened in parallel if enough CPU is available. ## `maxRequestRetries` (type: `integer`): How many times will the actor retry a request if it fails. ## `pageLoadTimeoutSecs` (type: `integer`): How long it will wait for page loading before it abandons the loading and tries again. ## Actor input object example ```json { "searchTerms": [ "webscraping" ], "isMultiple": false, "timeRange": "today 3-m", "geo": "", "skipDebugScreen": false, "startUrls": [ { "url": "https://trends.google.com/trends/explore?q=web+scraping&geo=US" } ], "spreadsheetId": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgVE2upms", "category": "", "maxItems": 0, "customTimeRange": "2024-01-01 2024-12-31", "maxConcurrency": 10, "maxRequestRetries": 7, "pageLoadTimeoutSecs": 180 } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset containing all scraped results # 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 = { "searchTerms": [ "webscraping" ], "timeRange": "", "viewedFrom": "" }; // Run the Actor and wait for it to finish const run = await client.actor("apify/google-trends-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "searchTerms": ["webscraping"], "timeRange": "", "viewedFrom": "", } # Run the Actor and wait for it to finish run = client.actor("apify/google-trends-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "webscraping" ], "timeRange": "", "viewedFrom": "" }' | apify call apify/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=apify/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper", "description": "Scrape data from Google Trends by search terms or URLs. Specify locations, define time ranges, select categories to get interest by subregion and over time, related queries and topics, and more. Export scraped data, run the scraper via API, schedule and monitor runs, or integrate with other tools.", "version": "0.0", "x-build-id": "S0ClPXhefwPUckUcx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/apify~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-apify-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/apify~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-apify-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/apify~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-apify-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchTerms": { "title": "Search terms", "type": "array", "description": "(Required if 'spreadsheetId' is not provided) List of search terms to be scraped.", "items": { "type": "string" } }, "isMultiple": { "title": "Multiple terms", "type": "boolean", "description": "If checked comma will be handled as search for multiple terms", "default": false }, "timeRange": { "title": "Time range", "enum": [ "", "now 1-H", "now 4-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 5-y", "all" ], "type": "string", "description": "Choose a predefined search's time range (defaults to 'Past 12 months')", "default": "" }, "geo": { "title": "Geo area", "enum": [ "", "AF", "AL", "DZ", "AS", "AD", "AO", "AI", "AQ", "AG", "AR", "AM", "AW", "AU", "AT", "AZ", "BS", "BH", "BD", "BB", "BY", "BE", "BZ", "BJ", "BM", "BT", "BO", "BQ", "BA", "BW", "BV", "BR", "IO", "BN", "BG", "BF", "BI", "CV", "KH", "CM", "CA", "KY", "CF", "TD", "CL", "CN", "CX", "Keeling Islands ", "CO", "KM", "CG", "CD", "CK", "CR", "CI", "HR", "CU", "CW", "CY", "CZ", "DK", "DJ", "DM", "DO", "EC", "EG", "SV", "GQ", "ER", "EE", "ET", "Malvinas ", "FO", "FJ", "FI", "FR", "GF", "PF", "TF", "GA", "GM", "GE", "DE", "GH", "GI", "GR", "GL", "GD", "GP", "GU", "GT", "GG", "GN", "GW", "GY", "HT", "HM", "Vatican City State ", "HN", "HK", "HU", "IS", "IN", "ID", "IR", "IQ", "IE", "IM", "IL", "IT", "JM", "JP", "JE", "JO", "KZ", "KE", "KI", "the Democratic People's Republic of ", "the Republic of ", "KW", "KG", "LA", "LV", "LB", "LS", "LR", "LY", "LI", "LT", "LU", "MO", "MK", "MG", "MW", "MY", "MV", "ML", "MT", "MH", "MQ", "MR", "MU", "YT", "MX", "FM", "MD", "MC", "MN", "ME", "MS", "MA", "MZ", "MM", "NA", "NR", "NP", "NL", "NC", "NZ", "NI", "NE", "NG", "NU", "NF", "MP", "NO", "OM", "PK", "PW", "PS", "PA", "PG", "PY", "PE", "PH", "PN", "PL", "PT", "PR", "QA", "RE", "RO", "RU", "RW", "BL", "SH", "KN", "LC", "French part ", "PM", "VC", "WS", "SM", "ST", "SA", "SN", "RS", "SC", "SL", "SG", "Dutch part ", "SK", "SI", "SB", "SO", "ZA", "GS", "SS", "ES", "LK", "SD", "SR", "SJ", "SZ", "SE", "CH", "SY", "TW", "TJ", "TZ", "TH", "TL", "TG", "TK", "TO", "TT", "TN", "TR", "TM", "TC", "TV", "UG", "UA", "AE", "GB", "US", "UM", "UY", "UZ", "VU", "VE", "VN", "VG", "VI", "WF", "EH", "YE", "ZM", "ZW" ], "type": "string", "description": "Get results from a specific geo area (defaults to 'Worldwide'). This maps to different fields in the dataset results: smaller countries like Greece return just 'interestByCity' data, while larger ones like the United States return 'interestBySubregion' data as well. When omitted (Worldwide), 'interestBy' is returned instead, which corresponds to the 'Interest by area' section on Google Trends. Note: When using Google Trends URLs from the '๐Ÿ”— Use a Google Trends URL instead of search terms' section, the geo parameter from the URL's query string takes precedence over this setting. You can also use start URLs to retrieve 'interestByMetro' data for metropolitan areas by providing URLs with more specific geo regions like California (US) directly copied from Google Trends.", "default": "" }, "viewedFrom": { "title": "Country to view Trends from", "enum": [ "", "us", "af", "al", "dz", "as", "ad", "ao", "ai", "aq", "ag", "ar", "am", "aw", "au", "at", "az", "bs", "bh", "bd", "bb", "by", "be", "bz", "bj", "bm", "bt", "bo", "ba", "bw", "bv", "br", "io", "bn", "bg", "bf", "bi", "kh", "cm", "ca", "cv", "ky", "cf", "td", "cl", "cn", "cx", "cc", "co", "km", "cg", "cd", "ck", "cr", "ci", "hr", "cu", "cy", "cz", "dk", "dj", "dm", "do", "ec", "eg", "sv", "gq", "er", "ee", "et", "fk", "fo", "fj", "fi", "fr", "gf", "pf", "tf", "ga", "gm", "ge", "de", "gh", "gi", "gr", "gl", "gd", "gp", "gu", "gt", "gn", "gw", "gy", "ht", "hm", "va", "hn", "hk", "hu", "is", "in", "id", "ir", "iq", "ie", "il", "it", "jm", "jp", "jo", "kz", "ke", "ki", "kp", "kr", "kw", "kg", "la", "lv", "lb", "ls", "lr", "ly", "li", "lt", "lu", "mo", "mk", "mg", "mw", "my", "mv", "ml", "mt", "mh", "mq", "mr", "mu", "yt", "mx", "fm", "md", "mc", "mn", "me", "ms", "ma", "mz", "mm", "na", "nr", "np", "nl", "an", "nc", "nz", "ni", "ne", "ng", "nu", "nf", "mp", "no", "om", "pk", "pw", "ps", "pa", "pg", "py", "pe", "ph", "pn", "pl", "pt", "pr", "qa", "re", "ro", "ru", "rw", "sh", "kn", "lc", "pm", "vc", "ws", "sm", "st", "sa", "sn", "rs", "sc", "sl", "sg", "sk", "si", "sb", "so", "za", "gs", "ss", "es", "lk", "sd", "sr", "sj", "sz", "se", "ch", "sy", "tw", "tj", "tz", "th", "tl", "tg", "tk", "to", "tt", "tn", "tr", "tm", "tc", "tv", "ug", "ua", "ae", "gb", "um", "uy", "uz", "vu", "ve", "vn", "vg", "vi", "wf", "eh", "ye", "zm", "zw" ], "type": "string", "description": "Country for residential proxies to ensure correct results" }, "skipDebugScreen": { "title": "Do not save snapshot", "type": "boolean", "description": "By default snapshot of each visited Google Trends page saved to KV Store", "default": false }, "startUrls": { "title": "Google Trends URLs", "type": "array", "description": "Copy a Google Trends URL and paste it here.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "spreadsheetId": { "title": "Google sheet ID (To get input data from Google sheet)", "type": "string", "description": "(Optional) Id of the google sheet from where search terms will be loaded.\n Spreadsheet must have only one column. Row 1 is considered the title of the column so it will not be loaded as a search term. The provided spreadsheet must be publicly available. Check the actor documentation for more info." }, "category": { "title": "Category of trends", "enum": [ "", "3", "47", "44", "22", "12", "5", "7", "71", "8", "45", "65", "11", "13", "958", "19", "16", "299", "14", "66", "29", "533", "174", "18", "20", "67" ], "type": "string", "description": "Choose a category to filter the search for (defaults to 'All categories')", "default": "" }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Limit of product items to be scraped. Zero value means no limit.", "default": 0 }, "customTimeRange": { "title": "Custom time range", "type": "string", "description": "Provide custom time range. If present, it takes precedence over regular timeRange. Format is: `YYYY-MM-DD YYYY-MM-DD`. See examples in the documentation." }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "type": "integer", "description": "How many pages will be opened in parallel if enough CPU is available.", "default": 10 }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "type": "integer", "description": "How many times will the actor retry a request if it fails.", "default": 7 }, "pageLoadTimeoutSecs": { "title": "Page load timeout", "minimum": 30, "type": "integer", "description": "How long it will wait for page loading before it abandons the loading and tries again.", "default": 180 } } }, "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 } } } } } } } } } } ```