# Data Cleaner (`parsebird/data-cleaner`) Actor Clean messy data — remove nulls, normalize case, trim whitespace, format phone numbers and emails, extract domains, convert types, and more. Works with Apify datasets or direct JSON input. - **URL**: https://apify.com/parsebird/data-cleaner.md - **Developed by:** [ParseBird](https://apify.com/parsebird) (community) - **Categories:** Agents, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.49 / 1,000 items cleaneds 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 ### Data Cleaner Clean messy data — remove nulls, normalize case, trim whitespace, format phone numbers and emails, extract domains, convert types, and more. Works with Apify datasets or direct JSON input. The first general-purpose data cleaner on Apify.
Clean messy scraped data in one step — trim whitespace, normalize casing, format phone numbers to E.164, lowercase emails, extract domains from URLs, convert strings to numbers, remove null rows, and deduplicate.

ParseBird Infra Suite   •  Utility tools for data pipelines
🔗  HTTP Request
Send API calls from the cloud 		📚  Data Deduplicator
Merge & deduplicate datasets by any field 		🗡  Data Cleaner
➤ You are here
##### Copy to your AI assistant Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor. ```` parsebird/data-cleaner on Apify. Call: ApifyClient("TOKEN").actor("parsebird/data-cleaner").call(run\_input={...}), then client.dataset(run\["defaultDatasetId"]).list\_items().items for cleaned results. Key inputs: datasetId (string, Apify dataset ID), jsonData (array of objects, direct JSON input), operations (array of {field, action, options} — required), outputDatasetId (string, optional), maxItems (integer, default 1000000). Actions: trim\_whitespace, normalize\_case (options: {case: "lower"|"upper"|"title"}), format\_email, format\_phone (options: {countryCode: "US"}), extract\_domain, to\_number, to\_date, fill\_nulls (options: {value: "..."}), remove\_nulls, remove\_duplicates, replace\_value (options: {find, replace}). Full actor spec: fetch build via GET https://api.apify.com/v2/acts/parsebird~data-cleaner (Bearer TOKEN). Get token: https://console.apify.com/account/integrations ```` ### What does Data Cleaner do? This Actor takes messy scraped or imported data and applies a configurable pipeline of cleaning operations. Each operation targets a specific field and transforms its values — trimming whitespace, normalizing case, formatting phone numbers, and more. Use cases: - **CRM cleanup** — normalize names, emails, and phone numbers before import - **Lead list hygiene** — remove rows with missing emails, deduplicate by company - **Post-scrape processing** — extract domains from URLs, convert price strings to numbers - **Data pipeline prep** — standardize data format before analysis or export ### Supported operations | Action | Description | Options | Before | After | |--------|-------------|---------|--------|-------| | `trim_whitespace` | Remove leading/trailing spaces | — | `" John Doe "` | `"John Doe"` | | `normalize_case` | Convert to lower/upper/title case | `{"case": "title"}` | `"john doe"` | `"John Doe"` | | `format_email` | Lowercase and trim emails | — | `" JOHN@CO.COM "` | `"john@co.com"` | | `format_phone` | Normalize to E.164 format | `{"countryCode": "US"}` | `"(555) 123-4567"` | `"+15551234567"` | | `extract_domain` | Extract domain from URL or email | — | `"https://www.example.com/page"` | `"example.com"` | | `to_number` | Convert string to number | — | `"$1,234,567"` | `1234567` | | `to_date` | Parse date to ISO 8601 | — | `"March 15, 2024"` | `"2024-03-15T00:00:00"` | | `fill_nulls` | Replace null/empty with default | `{"value": "N/A"}` | `null` | `"N/A"` | | `remove_nulls` | Remove rows where field is null/empty | — | *(row removed)* | — | | `remove_duplicates` | Deduplicate by this field | — | *(duplicate removed)* | — | | `replace_value` | Find and replace text | `{"find": "Inc.", "replace": "Inc"}` | `"Acme Inc."` | `"Acme Inc"` | ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `datasetId` | string | No* | — | Apify dataset ID to clean | | `jsonData` | array | No* | — | Direct JSON array of objects to clean | | `operations` | array | **Yes** | — | List of `{field, action, options}` cleaning operations | | `outputDatasetId` | string | No | — | Named output dataset (defaults to run dataset) | | `maxItems` | integer | No | `1000000` | Max items to process | *Provide either `datasetId` or `jsonData` (or both). ### Operations format Each operation is a JSON object with: ```json { "field": "email", "action": "format_email", "options": {} } ```` Operations are applied in order. You can chain multiple operations on the same field: ```json [ {"field": "name", "action": "trim_whitespace"}, {"field": "name", "action": "normalize_case", "options": {"case": "title"}}, {"field": "email", "action": "format_email"}, {"field": "phone", "action": "format_phone", "options": {"countryCode": "US"}}, {"field": "website", "action": "extract_domain"}, {"field": "revenue", "action": "to_number"}, {"field": "email", "action": "remove_nulls"} ] ``` ### Before and after example **Input (dirty data)** ```json [ {"name": " john doe ", "email": " JOHN@EXAMPLE.COM ", "phone": "(555) 123-4567", "website": "https://www.example.com/about", "revenue": "$1,234,567"}, {"name": "JANE SMITH", "email": "Jane.Smith@Company.IO", "phone": "555.987.6543", "website": "info@company.io", "revenue": "2345678"}, {"name": "", "email": null, "phone": "1-800-555-0199", "website": "company.io", "revenue": "$99.99"}, {"name": "bob wilson", "email": "bob@test.com", "phone": "+14155550100", "website": "https://test.com/page?id=1", "revenue": "not a number"} ] ``` **Output (cleaned data)** ```json [ {"name": "John Doe", "email": "john@example.com", "phone": "+15551234567", "website": "example.com", "revenue": 1234567}, {"name": "Jane Smith", "email": "jane.smith@company.io", "phone": "+15559876543", "website": "company.io", "revenue": 2345678}, {"name": "Bob Wilson", "email": "bob@test.com", "phone": "+14155550100", "website": "test.com", "revenue": "not a number"} ] ``` Row 3 was removed (null email with `remove_nulls`). All names are title-cased, emails lowercased, phones in E.164, domains extracted, and revenues converted to numbers. ### How to use via API **Python** ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("parsebird/data-cleaner").call(run_input={ "datasetId": "YOUR_DATASET_ID", "operations": [ {"field": "email", "action": "format_email"}, {"field": "name", "action": "trim_whitespace"}, {"field": "name", "action": "normalize_case", "options": {"case": "title"}}, {"field": "phone", "action": "format_phone", "options": {"countryCode": "US"}}, ], }) items = client.dataset(run["defaultDatasetId"]).list_items().items print(f"Cleaned items: {len(items)}") ``` **cURL** ```bash curl -X POST "https://api.apify.com/v2/acts/parsebird~data-cleaner/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "jsonData": [ {"name": " JOHN DOE ", "email": " JOHN@CO.COM "} ], "operations": [ {"field": "name", "action": "trim_whitespace"}, {"field": "name", "action": "normalize_case", "options": {"case": "title"}}, {"field": "email", "action": "format_email"} ] }' ``` ### Output Cleaned items retain their original structure. A `stats` key is stored in the key-value store: ```json { "totalLoaded": 5000, "totalCleaned": 4800, "operationsApplied": 7, "fieldsCleaned": 5, "totalChanges": 15200 } ``` ### Pricing This Actor uses a pay-per-event pricing model. | Event | Price per event | Price per 1,000 | |-------|----------------|-----------------| | `items-cleaned` | $0.00149 | **$1.49** | Charged per 1,000 items loaded. Platform compute costs are additional. # Actor input Schema ## `datasetId` (type: `string`): Apify dataset ID to load and clean. Leave empty if providing data via jsonData. ## `jsonData` (type: `array`): Direct JSON array of objects to clean. Use this instead of datasetId for ad-hoc cleaning. ## `operations` (type: `array`): List of cleaning operations to apply. Each operation specifies a field, an action, and optional settings. Operations are applied in order. ## `outputDatasetId` (type: `string`): Optional named dataset for output. If empty, results go to the default run dataset. ## `maxItems` (type: `integer`): Maximum number of items to process. ## Actor input object example ```json { "jsonData": [ { "name": " john doe ", "email": " JOHN@EXAMPLE.COM " }, { "name": "JANE SMITH", "email": "Jane.Smith@Company.IO" }, { "name": "bob wilson", "email": "bob@test.com" } ], "operations": [ { "field": "email", "action": "format_email" }, { "field": "name", "action": "trim_whitespace" }, { "field": "name", "action": "normalize_case", "options": { "case": "title" } } ], "maxItems": 1000000 } ``` # 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 = { "jsonData": [ { "name": " john doe ", "email": " JOHN@EXAMPLE.COM " }, { "name": "JANE SMITH", "email": "Jane.Smith@Company.IO" }, { "name": "bob wilson", "email": "bob@test.com" } ], "operations": [ { "field": "email", "action": "format_email" }, { "field": "name", "action": "trim_whitespace" }, { "field": "name", "action": "normalize_case", "options": { "case": "title" } } ] }; // Run the Actor and wait for it to finish const run = await client.actor("parsebird/data-cleaner").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 = { "jsonData": [ { "name": " john doe ", "email": " JOHN@EXAMPLE.COM ", }, { "name": "JANE SMITH", "email": "Jane.Smith@Company.IO", }, { "name": "bob wilson", "email": "bob@test.com", }, ], "operations": [ { "field": "email", "action": "format_email", }, { "field": "name", "action": "trim_whitespace", }, { "field": "name", "action": "normalize_case", "options": { "case": "title" }, }, ], } # Run the Actor and wait for it to finish run = client.actor("parsebird/data-cleaner").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 '{ "jsonData": [ { "name": " john doe ", "email": " JOHN@EXAMPLE.COM " }, { "name": "JANE SMITH", "email": "Jane.Smith@Company.IO" }, { "name": "bob wilson", "email": "bob@test.com" } ], "operations": [ { "field": "email", "action": "format_email" }, { "field": "name", "action": "trim_whitespace" }, { "field": "name", "action": "normalize_case", "options": { "case": "title" } } ] }' | apify call parsebird/data-cleaner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parsebird/data-cleaner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Data Cleaner", "description": "Clean messy data — remove nulls, normalize case, trim whitespace, format phone numbers and emails, extract domains, convert types, and more. Works with Apify datasets or direct JSON input.", "version": "1.1", "x-build-id": "MUNNOpDfms4c8muO4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parsebird~data-cleaner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parsebird-data-cleaner", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parsebird~data-cleaner/runs": { "post": { "operationId": "runs-sync-parsebird-data-cleaner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parsebird~data-cleaner/run-sync": { "post": { "operationId": "run-sync-parsebird-data-cleaner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "operations" ], "properties": { "datasetId": { "title": "Dataset ID", "type": "string", "description": "Apify dataset ID to load and clean. Leave empty if providing data via jsonData." }, "jsonData": { "title": "JSON Data", "type": "array", "description": "Direct JSON array of objects to clean. Use this instead of datasetId for ad-hoc cleaning." }, "operations": { "title": "Cleaning Operations", "type": "array", "description": "List of cleaning operations to apply. Each operation specifies a field, an action, and optional settings. Operations are applied in order." }, "outputDatasetId": { "title": "Output Dataset ID", "type": "string", "description": "Optional named dataset for output. If empty, results go to the default run dataset." }, "maxItems": { "title": "Max Items", "minimum": 1, "type": "integer", "description": "Maximum number of items to process.", "default": 1000000 } } }, "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 } } } } } } } } } } ```