# Data Cleaner — messy CSV/Excel/JSON to clean typed data (`amanatools/data-cleaner`) Actor

Clean, deduplicate, and type-infer CSV, Excel, and JSON files from URLs. Returns a quality report, preview rows, and a download link to the cleaned CSV. Built for AI agents and data pipelines.

- **URL**: https://apify.com/amanatools/data-cleaner.md
- **Developed by:** [Dos](https://apify.com/amanatools) (community)
- **Categories:** Developer tools, AI
- **Features:** Limited permissions
- **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 file 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Data Cleaner — messy CSV/Excel/JSON to clean, typed data

Point it at data files; get back **deduplicated, whitespace-trimmed, type-inferred data** plus an honest quality report — and a direct download link to the cleaned CSV. The un-glamorous work every data pipeline and AI agent needs done before anything else works.

### What it does per file

- Reads CSV (separator auto-detected), Excel (.xlsx, any sheet), JSON (records or JSON-lines)
- Normalizes headers; trims whitespace; blanks become nulls
- Drops empty rows/columns; removes duplicate rows
- Infers column types (numbers, dates) when >=90% of values parse
- Outputs: `quality_report`, `preview_rows` (first 20), `cleaned_csv_url` (full file)

### Pricing

Pay-per-event: a small fee per file plus a micro-fee per 1,000 cleaned rows. No subscription.

### Limits

Up to 50 files/run, 100 MB and 250,000 rows per file. Failures return `status: "error"` so pipelines can branch.

### Other actors by amanatools

- [PDF Text Extractor](https://apify.com/amanatools/pdf-table-extractor) — tables and text from PDF files into clean JSON rows
- [PDF OCR Extractor](https://apify.com/amanatools/pdf-ocr-extractor) — scanned and image-only PDFs into searchable text
- [Greenhouse Lever Workday ATS Jobs API](https://apify.com/amanatools/greenhouse-lever-workday-ats-jobs-api) — open jobs straight from Greenhouse, Lever, Workday and more
- [Doc to Markdown](https://apify.com/amanatools/doc-to-markdown) — DOCX, PDF and web pages into clean markdown

# Actor input Schema

## `file_urls` (type: `array`):

Direct links to data files (up to 50 per run, 100 MB / 250k rows each).

## `deduplicate` (type: `boolean`):

Drop rows that are exact duplicates of an earlier row, keeping the first.

## `infer_types` (type: `boolean`):

Cast text columns to numbers or dates when at least 90% of values parse.

## `drop_empty_columns` (type: `boolean`):

Remove columns where every value is empty or null.

## `excel_sheet` (type: `string`):

Which sheet to read from Excel files. Default: first sheet.

## Actor input object example

```json
{
  "file_urls": [
    "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"
  ],
  "deduplicate": true,
  "infer_types": true,
  "drop_empty_columns": true,
  "excel_sheet": "0"
}
```

# Actor output Schema

## `results` (type: `string`):

Default dataset: one item per file with url, status, error, cleaned_csv_url, quality_report (rows_in, rows_out, duplicate_rows_removed, empty_rows_removed, empty_columns_removed, columns) and preview_rows.

## `cleanedFiles` (type: `string`):

Key-value store of this run: each cleaned file is stored as cleaned-N.csv, the same file cleaned_csv_url points at.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "file_urls": [
        "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("amanatools/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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "file_urls": ["https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"] }

# Run the Actor and wait for it to finish
run = client.actor("amanatools/data-cleaner").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "file_urls": [
    "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv"
  ]
}' |
apify call amanatools/data-cleaner --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,amanatools/data-cleaner"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Lm9UwfgDqPY8BVskA/builds/nlKBUW5oy1cTQCaAc/openapi.json