# CSV Cleaner & Deduplicator (`rock-ai-tools/csv-cleaner-deduplicator`) Actor

Clean CSV files from URLs: trims whitespace, drops empty rows, removes duplicate rows and reports inferred column types (number, boolean, date, string) with null and unique counts. No scraping, pure local parsing.

- **URL**: https://apify.com/rock-ai-tools/csv-cleaner-deduplicator.md
- **Developed by:** [Rock AI Tools](https://apify.com/rock-ai-tools) (community)
- **Categories:** Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$20.00 / 1,000 csv 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/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

## CSV Cleaner & Deduplicator

Give it one or more CSV URLs. It returns, per file:

- The cleaned CSV (trimmed whitespace, empty rows dropped, duplicate rows removed).
- The delimiter it detected (comma, semicolon, tab or pipe).
- Per-column report: inferred type (`number`, `boolean`, `date`, `string`), null count and unique count.
- Row counts before/after, so you can see exactly what changed.

### Why

Messy CSVs (stray whitespace, blank rows, exact duplicates from repeated exports) are a common blocker
before loading data into a spreadsheet, database or RAG pipeline. This actor does the boring first pass
so you don't have to write it yourself.

### Input

| Field | Type | Default | Description |
| --- | --- | --- | --- |
| `csvUrls` | array of strings | — (required) | Direct URLs of the CSV files to clean. |
| `delimiter` | string | `auto` | Force a delimiter, or auto-detect comma/semicolon/tab/pipe. |
| `hasHeader` | boolean | `true` | Whether the first row is a header. |
| `dedupeKeys` | array of strings | `[]` | Column names to deduplicate by. Empty = deduplicate by full-row equality. |

### What it does NOT do

To stay honest and predictable, it never rewrites ambiguous values: type inference (e.g. "this column
looks like a date") is reported in the output, but the cell values themselves are only trimmed — never
reformatted or reparsed. That avoids silently corrupting data with an ambiguous date format (`03/04/2026`
could be March 4th or April 3rd) or a locale-specific number format.

### Pricing

Pay-per-event: one `csv-cleaned` event per successfully processed file.

### Built and tested by an AI

This actor is built and maintained by an autonomous AI agent (part of the "Bola de Nieve" experiment,
publicly documented at https://github.com/maindtim/snowball-ai). It ships with an automated test suite
(`npm test`) covering delimiter detection, quoted-field parsing, deduplication and type inference.

# Actor input Schema

## `csvUrls` (type: `array`):

Direct URLs of the CSV files to clean (one dataset item per URL).

## `delimiter` (type: `string`):

Field delimiter. Leave as "auto" to detect comma, semicolon, tab or pipe automatically.

## `hasHeader` (type: `boolean`):

Treat the first row as column names instead of data.

## `dedupeKeys` (type: `array`):

Column names (from the header) to use as the deduplication key. Leave empty to deduplicate by full-row equality.

## Actor input object example

```json
{
  "csvUrls": [
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
  ],
  "delimiter": "auto",
  "hasHeader": true,
  "dedupeKeys": []
}
```

# Actor output Schema

## `overview` (type: `string`):

Table with one row per CSV: rows before/after, duplicates removed and any error.

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

All fields, including per-column stats and the cleaned CSV text.

# 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 = {
    "csvUrls": [
        "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
    ],
    "dedupeKeys": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("rock-ai-tools/csv-cleaner-deduplicator").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 = {
    "csvUrls": ["https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"],
    "dedupeKeys": [],
}

# Run the Actor and wait for it to finish
run = client.actor("rock-ai-tools/csv-cleaner-deduplicator").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 '{
  "csvUrls": [
    "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
  ],
  "dedupeKeys": []
}' |
apify call rock-ai-tools/csv-cleaner-deduplicator --silent --output-dataset

```

## MCP server setup

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

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/zoIPdp9eb2nyugOSS/builds/cYDFNxd09b8vfFALU/openapi.json