# Data Cleaner & Normalizer (JSON/CSV) (`zenomastro/data-cleaner-normalizer`) Actor Clean and normalize JSON/CSV data: trim whitespace, lowercase emails, normalize phone numbers and dates, drop empty values/rows, and deduplicate by a field. - **URL**: https://apify.com/zenomastro/data-cleaner-normalizer.md - **Developed by:** [Rosario Vitale](https://apify.com/zenomastro) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 item cleaneds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 & Normalizer (JSON/CSV) Clean and normalize messy datasets in one step. Feed a JSON array of records and get back tidy, consistent data โ€” perfect as the **cleaning step before deduplication, import, or analysis**. ### Features - โœ‚๏ธ **Trim & collapse whitespace** in every string field. - ๐Ÿ“ง **Lowercase emails** so `Mario.Rossi@EXAMPLE.com` becomes `mario.rossi@example.com`. - โ˜Ž๏ธ **Normalize phone numbers** โ€” strips spaces, dashes and brackets, keeps a leading `+`. - ๐Ÿ“… **Normalize dates** to ISO `YYYY-MM-DD` (optional, best-effort). - ๐Ÿงน **Drop empty fields and empty rows**. - ๐Ÿ” **Deduplicate** by any field (e.g. keep one record per `email`). ### Input | Field | Type | Description | |---|---|---| | `items` | array | JSON array of objects to clean (required). | | `trimWhitespace` | boolean | Trim spaces. Default `true`. | | `collapseSpaces` | boolean | Collapse inner whitespace. Default `true`. | | `lowercaseEmails` | boolean | Lowercase email values. Default `true`. | | `cleanPhones` | boolean | Strip phone formatting. Default `true`. | | `normalizeDates` | boolean | Convert dates to ISO. Default `false`. | | `removeEmptyValues` | boolean | Drop null/empty fields. Default `false`. | | `dropEmptyRows` | boolean | Skip empty records. Default `true`. | | `dedupeKey` | string | Field to deduplicate by (optional). | #### Example input ```json { "items": [ { "name": " Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567" }, { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567" } ], "dedupeKey": "email" } ```` ### Output One cleaned record per kept item: ```json { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567" } ``` Export as **JSON, CSV, or Excel**, or pull via the Apify API. ### Common use cases - Clean **scraped leads** before importing to a CRM. - Normalize **emails and phone numbers** for matching and dedup. - Tidy any dataset before **deduplication**, analysis, or upload. - A reliable **cleaning step** in an automated data pipeline. ### Notes - Pure in-memory processing โ€” no external services, nothing to break over time. - Date normalization is best-effort; ambiguous formats may not convert. # Actor input Schema ## `items` (type: `array`): The records to clean โ€” a JSON array of objects. ## `trimWhitespace` (type: `boolean`): Trim leading/trailing spaces from all string values. ## `collapseSpaces` (type: `boolean`): Collapse runs of inner whitespace into a single space. ## `lowercaseEmails` (type: `boolean`): Detect email-looking values and lowercase them. ## `cleanPhones` (type: `boolean`): Strip spaces, dashes and brackets from phone-looking values (keeps a leading +). ## `normalizeDates` (type: `boolean`): Best-effort convert date-looking values to ISO format (YYYY-MM-DD). ## `removeEmptyValues` (type: `boolean`): Drop keys whose value is null or an empty string. ## `dropEmptyRows` (type: `boolean`): Skip records that have no remaining fields after cleaning. ## `dedupeKey` (type: `string`): Optional field name. If set, keeps only the first record for each unique value of this field (e.g. "email"). ## Actor input object example ```json { "items": [ { "name": " Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567", "city": "Rome" }, { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567", "city": "" }, { "name": "Lucia Bianchi", "email": " LUCIA@Example.COM", "phone": "06 1234 5678", "city": "Milan" } ], "trimWhitespace": true, "collapseSpaces": true, "lowercaseEmails": true, "cleanPhones": true, "normalizeDates": false, "removeEmptyValues": false, "dropEmptyRows": true } ``` # 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 = { "items": [ { "name": " Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567", "city": "Rome" }, { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567", "city": "" }, { "name": "Lucia Bianchi", "email": " LUCIA@Example.COM", "phone": "06 1234 5678", "city": "Milan" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("zenomastro/data-cleaner-normalizer").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 = { "items": [ { "name": " Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567", "city": "Rome", }, { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567", "city": "", }, { "name": "Lucia Bianchi", "email": " LUCIA@Example.COM", "phone": "06 1234 5678", "city": "Milan", }, ] } # Run the Actor and wait for it to finish run = client.actor("zenomastro/data-cleaner-normalizer").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 '{ "items": [ { "name": " Mario Rossi ", "email": "Mario.Rossi@EXAMPLE.com ", "phone": "+39 (333) 123-4567", "city": "Rome" }, { "name": "Mario Rossi", "email": "mario.rossi@example.com", "phone": "+393331234567", "city": "" }, { "name": "Lucia Bianchi", "email": " LUCIA@Example.COM", "phone": "06 1234 5678", "city": "Milan" } ] }' | apify call zenomastro/data-cleaner-normalizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zenomastro/data-cleaner-normalizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Data Cleaner & Normalizer (JSON/CSV)", "description": "Clean and normalize JSON/CSV data: trim whitespace, lowercase emails, normalize phone numbers and dates, drop empty values/rows, and deduplicate by a field.", "version": "0.1", "x-build-id": "1QrRVzAJbWbhsCfK9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zenomastro~data-cleaner-normalizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zenomastro-data-cleaner-normalizer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/zenomastro~data-cleaner-normalizer/runs": { "post": { "operationId": "runs-sync-zenomastro-data-cleaner-normalizer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/zenomastro~data-cleaner-normalizer/run-sync": { "post": { "operationId": "run-sync-zenomastro-data-cleaner-normalizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "items" ], "properties": { "items": { "title": "Items (JSON array)", "type": "array", "description": "The records to clean โ€” a JSON array of objects." }, "trimWhitespace": { "title": "Trim whitespace", "type": "boolean", "description": "Trim leading/trailing spaces from all string values.", "default": true }, "collapseSpaces": { "title": "Collapse inner spaces", "type": "boolean", "description": "Collapse runs of inner whitespace into a single space.", "default": true }, "lowercaseEmails": { "title": "Lowercase emails", "type": "boolean", "description": "Detect email-looking values and lowercase them.", "default": true }, "cleanPhones": { "title": "Clean phone numbers", "type": "boolean", "description": "Strip spaces, dashes and brackets from phone-looking values (keeps a leading +).", "default": true }, "normalizeDates": { "title": "Normalize dates to ISO", "type": "boolean", "description": "Best-effort convert date-looking values to ISO format (YYYY-MM-DD).", "default": false }, "removeEmptyValues": { "title": "Remove empty fields", "type": "boolean", "description": "Drop keys whose value is null or an empty string.", "default": false }, "dropEmptyRows": { "title": "Drop empty rows", "type": "boolean", "description": "Skip records that have no remaining fields after cleaning.", "default": true }, "dedupeKey": { "title": "Deduplicate by field", "type": "string", "description": "Optional field name. If set, keeps only the first record for each unique value of this field (e.g. \"email\")." } } }, "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 } } } } } } } } } } ```