# CSV Schema Profiler (`junipr/csv-schema-profiler`) Actor Profile CSV columns for inferred types, null rates, uniqueness, sample values, duplicates, and drift risks. - **URL**: https://apify.com/junipr/csv-schema-profiler.md - **Developed by:** [junipr](https://apify.com/junipr) (community) - **Categories:** SEO tools, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.90 / 1,000 record processeds 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## CSV Schema Profiler ### Store Positioning **Store title:** CSV Schema Profiler **Short description:** Profile CSV columns for inferred types, null rates, uniqueness, sample values, duplicates, and drift risks. **SEO title:** CSV Schema Profiler — data QA, validation, and cleanup utility **SEO description:** Profile CSV columns for inferred types, null rates, uniqueness, sample values, duplicates, and drift risks. Use it to validate rows, schemas, duplicates, field quality, and delivery-readiness before handing data to clients or automations. **Categories:** SEO_TOOLS, DEVELOPER_TOOLS **Keywords:** csv, schema, profiler, csv qa, data/schema qa utility ### Pricing This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model. - Tier: U2 — Data/schema QA utility - Primary event: `row-checked` at $0.00390 base - Default max charge: $5.00 - Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount Event set: - `actor-start`: base $0.00500, GOLD $0.00400. CSV Schema Profiler: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `row-checked`: base $0.00390, GOLD $0.00312. CSV Schema Profiler: charged when row checked is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `issue-detected`: base $0.00372, GOLD $0.00298. CSV Schema Profiler: charged when issue detected is completed. The price includes Apify platform usage; no separate usage pass-through is intended. - `qa-report-generated`: base $0.05000, GOLD $0.04000. CSV Schema Profiler: charged when qa report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended. ### Public Task Concepts - Extract CSV Schema rows from supplied public inputs - Inventory CSV Schema fields across a bounded input - Normalize CSV Schema evidence for delivery - Flag missing CSV Schema data before delivery - Export CSV column profiles with warnings ### What It Does Profile CSV columns for inferred types, null rates, uniqueness, sample values, duplicates, and drift risks. Use it to inspect type consistency, missing values, uniqueness, and representative values before importing or delivering a CSV dataset. ### What It Does Not Do - It does not modify, enrich, or upload the supplied CSV to external services. - It does not infer business meaning or guarantee that a value is correct. - It does not replace a full data contract or warehouse constraint suite. - It processes only the bounded CSV content supplied in the run input. ### Input Fields Provide `csvText`, choose the delimiter and header behavior, and set bounded item and charge limits. ### Output Fields - `columnName` - `columnIndex` - `inferredType` - `nonEmptyCount` - `blankCount` - `nullRate` - `uniqueCount` - `sampleValues` - `warnings` ### Public Task Examples #### Profile CSV column types in a customer export - Input: A capped CSV customer export with its delimiter and header setting. - Output: Column rows with inferred type, fill rate, uniqueness, samples, and warnings. - Use case: Identify schema drift before loading a customer export. #### Find blank values before importing a spreadsheet - Input: A spreadsheet export containing optional and required columns. - Output: Per-column blank counts, null rates, and warning labels. - Use case: Find incomplete fields before a bulk import. #### Summarize unique values for feed cleanup - Input: A product or partner feed with repeated category and status values. - Output: Unique counts and representative values for each column. - Use case: Spot inconsistent enumerations before feed cleanup. #### Detect numeric columns with malformed values - Input: A CSV containing expected numeric columns and malformed entries. - Output: Inferred types and warning evidence for mixed-value columns. - Use case: Catch malformed price or quantity fields before delivery. #### Create a CSV data-quality table for operations - Input: A bounded operational export with headers enabled. - Output: A normalized column-quality table and optional summary artifact. - Use case: Attach consistent profiling evidence to a data delivery. ### Tests - Actor-specific tests live in `test/csv-schema-profiler.test.ts`. - The suite validates output artifacts, issue/no-result cases, report billing, and max-charge stops. ### FAQ #### Does the actor change my CSV? No. It returns profile rows and report artifacts without rewriting the supplied content. #### How should I choose a sample? Use a representative bounded extract that includes headers and known edge values. # Actor input Schema ## `csvText` (type: `string`): Bounded CSV text to profile. ## `delimiter` (type: `string`): Single field delimiter used by the CSV content. ## `hasHeader` (type: `boolean`): Use the first CSV row as column names. ## `includeSampleValues` (type: `boolean`): Include up to five distinct non-empty values per column. ## `maxRows` (type: `integer`): Hard cap for emitted column profile rows. ## `includeReport` (type: `boolean`): Create charged Markdown and JSON report artifacts. ## `maxChargeUsd` (type: `number`): Stop before an event would exceed this run budget. ## Actor input object example ```json { "csvText": "Name,Code\nAfghanistan,AF\nAlbania,AL\nAlgeria,DZ\n", "delimiter": ",", "hasHeader": true, "includeSampleValues": true, "maxRows": 10, "includeReport": true, "maxChargeUsd": 1 } ```` # Actor output Schema ## `dataset` (type: `string`): Structured rows emitted by the actor. ## `report` (type: `string`): Run summary report stored in the default key-value store. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("junipr/csv-schema-profiler").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 = {} # Run the Actor and wait for it to finish run = client.actor("junipr/csv-schema-profiler").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 '{}' | apify call junipr/csv-schema-profiler --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=junipr/csv-schema-profiler", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CSV Schema Profiler", "description": "Profile CSV columns for inferred types, null rates, uniqueness, sample values, duplicates, and drift risks.", "version": "1.0", "x-build-id": "O63CED1Qz3E2SgD7m" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/junipr~csv-schema-profiler/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-junipr-csv-schema-profiler", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/junipr~csv-schema-profiler/runs": { "post": { "operationId": "runs-sync-junipr-csv-schema-profiler", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/junipr~csv-schema-profiler/run-sync": { "post": { "operationId": "run-sync-junipr-csv-schema-profiler", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "csvText": { "title": "CSV content", "type": "string", "description": "Bounded CSV text to profile.", "default": "Name,Code\nAfghanistan,AF\nAlbania,AL\nAlgeria,DZ\n" }, "delimiter": { "title": "Delimiter", "minLength": 1, "maxLength": 1, "type": "string", "description": "Single field delimiter used by the CSV content.", "default": "," }, "hasHeader": { "title": "First row contains headers", "type": "boolean", "description": "Use the first CSV row as column names.", "default": true }, "includeSampleValues": { "title": "Include sample values", "type": "boolean", "description": "Include up to five distinct non-empty values per column.", "default": true }, "maxRows": { "title": "Maximum output columns", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap for emitted column profile rows.", "default": 10 }, "includeReport": { "title": "Include report", "type": "boolean", "description": "Create charged Markdown and JSON report artifacts.", "default": true }, "maxChargeUsd": { "title": "Maximum charge USD", "minimum": 0, "maximum": 100, "type": "number", "description": "Stop before an event would exceed this run budget.", "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 } } } } } } } } } } ```