# Name Insights - Age, Gender, Nationality from First Name (`gio21/name-insights-scraper`) Actor Predict age, gender, nationality from first names using agify.io + genderize.io + nationalize.io public APIs. Returns predicted age, gender (with probability), top 5 likely countries (with probability). No API key. For HR, dating apps, demographics, surveys. - **URL**: https://apify.com/gio21/name-insights-scraper.md - **Developed by:** [Gio](https://apify.com/gio21) (community) - **Categories:** E-commerce, Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 name analyzeds 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 ## Name Insights Scraper Predict **age**, **gender**, and **nationality** from first names. Bundles three free public APIs (agify.io, genderize.io, nationalize.io) into a single record per name. No auth. Returns: - **ageGuess** + sample size - **gender** + probability + sample size - **topCountries** (up to 5, with probability) + top country Useful for **HR analytics**, **dating apps**, **demographic studies**, **survey analysis**, **personalization**. ### Input | Field | Type | Description | |-------|------|-------------| | `names` | Array (required) | First names to analyze. | | `countryHintForAge` | String | ISO-2 country (e.g. `BR`, `US`) to scope age prediction to that country. | | `datasets` | Array | Which APIs to call: `agify`, `genderize`, `nationalize`. Default all three. | ### Output ```json { "name": "Maria", "ageGuess": 50, "ageSampleSize": 230000, "gender": "female", "genderProbability": 0.99, "genderSampleSize": 850000, "topCountry": "PT", "nationalitySampleSize": 380000, "topCountries": [ { "country": "PT", "probability": 0.18 }, { "country": "BR", "probability": 0.15 }, { "country": "ES", "probability": 0.12 }, { "country": "IT", "probability": 0.08 }, { "country": "AR", "probability": 0.06 } ] } ```` ### Pricing $0.001 per name analyzed. ### Limitations - Free APIs are rate-limited per IP (1000/day each). Heavy use may hit limits. - Predictions are statistical guesses from training corpora — useful as signal, not ground truth. If this actor helped you, please leave a review on the Apify Store. # Actor input Schema ## `names` (type: `array`): List of first names to analyze (e.g. 'Maria', 'Hiroshi', 'Liam'). ## `countryHintForAge` (type: `string`): ISO-2 country code (e.g. 'BR', 'US') to scope age predictions to that country's name distribution. ## `datasets` (type: `array`): Which predictions to fetch: agify (age), genderize (gender), nationalize (top countries). ## `debug` (type: `boolean`): Verbose logging. ## Actor input object example ```json { "names": [ "Maria", "Hiroshi", "Liam", "Aisha" ], "datasets": [ "agify", "genderize", "nationalize" ], "debug": false } ``` # Actor output Schema ## `results` (type: `string`): Insights record per name: ageGuess, gender, genderProbability, topCountry, topCountries (with probabilities), sampleSize. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "names": [ "Maria", "Hiroshi", "Liam", "Aisha" ], "countryHintForAge": "", "datasets": [ "agify", "genderize", "nationalize" ] }; // Run the Actor and wait for it to finish const run = await client.actor("gio21/name-insights-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "names": [ "Maria", "Hiroshi", "Liam", "Aisha", ], "countryHintForAge": "", "datasets": [ "agify", "genderize", "nationalize", ], } # Run the Actor and wait for it to finish run = client.actor("gio21/name-insights-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 '{ "names": [ "Maria", "Hiroshi", "Liam", "Aisha" ], "countryHintForAge": "", "datasets": [ "agify", "genderize", "nationalize" ] }' | apify call gio21/name-insights-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gio21/name-insights-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Name Insights - Age, Gender, Nationality from First Name", "description": "Predict age, gender, nationality from first names using agify.io + genderize.io + nationalize.io public APIs. Returns predicted age, gender (with probability), top 5 likely countries (with probability). No API key. For HR, dating apps, demographics, surveys.", "version": "0.0", "x-build-id": "MBTkA5plWQDRh6UkV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gio21~name-insights-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gio21-name-insights-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gio21~name-insights-scraper/runs": { "post": { "operationId": "runs-sync-gio21-name-insights-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gio21~name-insights-scraper/run-sync": { "post": { "operationId": "run-sync-gio21-name-insights-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "names" ], "properties": { "names": { "title": "First names", "uniqueItems": true, "type": "array", "description": "List of first names to analyze (e.g. 'Maria', 'Hiroshi', 'Liam').", "items": { "type": "string" } }, "countryHintForAge": { "title": "Country hint for age (optional)", "type": "string", "description": "ISO-2 country code (e.g. 'BR', 'US') to scope age predictions to that country's name distribution." }, "datasets": { "title": "Datasets to include", "uniqueItems": true, "type": "array", "description": "Which predictions to fetch: agify (age), genderize (gender), nationalize (top countries).", "items": { "type": "string" } }, "debug": { "title": "Debug logs", "type": "boolean", "description": "Verbose logging.", "default": false } } }, "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": 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"type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```