# Data Cleaning Actor (`tonic_teak/data-cleaning-actor`) Actor

This actor automatically cleans, analyzes, and summarizes spreadsheet data.
It handles different file types (CSV, XLSX), fixes missing values, detects outliers, generates charts, computes correlations, and returns a cleaned dataset along with downloadable files and visual insights.

- **URL**: https://apify.com/tonic\_teak/data-cleaning-actor.md
- **Developed by:** [Mitchell Wanjiru](https://apify.com/tonic_teak) (community)
- **Categories:** Automation
- **Stats:** 6 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$15.00 / 1,000 results

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?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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\_cleaning\_actor

A Python-based Apify Actor for automatic data cleaning, summarization, and visualization of CSV or Excel files.

### Features

- Upload CSV or Excel files
- Clean data (remove missing, standardize types)
- Compute summary statistics (mean, median, missing values)
- Generate correlation matrix and plots (histogram, heatmap)
- Outputs cleaned CSV, JSON summary, and optional plot image

### Usage

#### Inputs

- `dataFile`: Path to CSV or Excel file to process
- `columnsToAnalyze`: (optional) List of columns to include in analysis
- `summaryType`: "stats", "correlations", or "charts"

Example `key_value_stores/default/INPUT.json`:

```json
{
  "dataFile": "sample.csv",
  "columnsToAnalyze": [],
  "summaryType": "stats"
}
```

#### Outputs

- `key_value_stores/default/cleaned.csv`: Cleaned data file
- `key_value_stores/default/summary.json`: JSON summary (mean, median, missing values, correlations)
- `key_value_stores/default/plot.png`: Plot image (if requested)

### Running Locally

1. Install Python dependencies:
   ```powershell
   pip install -r requirements.txt
   ```
2. Prepare your input file and `INPUT.json` as above.
3. Run the Actor:
   ```powershell
   python src/main.py
   ```
4. Check outputs in `key_value_stores/default/`

### Publishing to Apify

1. Ensure all required files are present:
   - `.actor/actor.json`, `input_schema.json`, `output_schema.json`, `dataset_schema.json`
   - `src/main.py`, `requirements.txt`, `Dockerfile`, `AGENTS.md`, `README.md`
2. Test with real data and input options.
3. Log in and push:
   ```powershell
   apify login
   apify push
   ```

### Notes

- Only numeric columns are used for correlations and plots.
- All NaN values in JSON output are converted to `null` for compatibility.
- For questions or issues, see AGENTS.md for Apify Actor guidance.

# Actor input Schema

## `dataFile` (type: `string`):

Upload a CSV or Excel file.

## `columnsToAnalyze` (type: `array`):

Optional: List of columns to include in analysis.

## `summaryType` (type: `string`):

Type of summary to generate.

## Actor input object example

```json
{
  "columnsToAnalyze": [],
  "summaryType": "stats"
}
```

# Actor output Schema

## `cleanedFile` (type: `string`):

No description

## `cleanedExcel` (type: `string`):

No description

## `summaryJson` (type: `string`):

No description

## `outlierReport` (type: `string`):

No description

## `dataTypeSummary` (type: `string`):

No description

## `boxplotImage` (type: `string`):

No description

## `scatterPlotImage` (type: `string`):

No description

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

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("tonic_teak/data-cleaning-actor").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("tonic_teak/data-cleaning-actor").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 '{}' |
apify call tonic_teak/data-cleaning-actor --silent --output-dataset

```

## MCP server setup

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

```

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/CQZ5NgSL8iTGru6fr/builds/ydCwbkaAOe0y4CsPa/openapi.json