# Dataset Profiler — Data Quality QA (`alaudinburki/dataset-profiler`) Actor

Profile any Apify dataset into a per-column report: inferred type, type consistency, null rate, distinct count, uniqueness, value ranges, and top values — plus duplicate-row and empty-column detection. QA any scrape before you trust it.

- **URL**: https://apify.com/alaudinburki/dataset-profiler.md
- **Developed by:** [alaudin burki](https://apify.com/alaudinburki) (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 $0.50 / 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?

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

## Dataset Profiler — Data Quality QA

Profile any Apify dataset into a per-column report: inferred type, type consistency, null rate, distinct count, uniqueness, value ranges, and top values — plus duplicate-row and empty-column detection. QA any scrape before you trust it.

Built reliability-first: **every row reports what was found and what was missing** — you never get a
silent blank, and a run summary tells you exactly what happened.

### What you get

| Field | Description |
|---|---|
| `column` | Column |
| `inferredType` | Inferred Type |
| `typeConsistency` | Type Consistency |
| `filled` | Filled |
| `nulls` | Nulls |
| `nullRate` | Null Rate |
| `distinct` | Distinct |
| `unique` | Unique |

### How to use it

1. Fill in the input (see the example below).
2. Run it once for a snapshot, or **schedule it** to keep the data fresh.
3. Export to CSV/JSON/Excel, or push straight to Google Sheets, Notion, Airtable, Zapier, Make, or n8n.

### Input

```json
{
  "items": []
}
```

### Sample output

```json
[
{
  "column": "example",
  "inferredType": "example",
  "typeConsistency": "example",
  "filled": "example",
  "nulls": "example",
  "nullRate": 3.14,
  "distinct": "example",
  "unique": "example"
}
]
```

### Typical uses

- **QA a scrape** — spot empty/mistyped columns before using the data.
- **Pick key fields** — see which columns are unique (good dedup keys) or high-null.
- **Monitor drift** — schedule and watch `nullRate`/`typeConsistency` change over time.

### Pricing

**$0.50 / 1,000 results** (`$0.0005` per result), plus a near-zero start fee. You are **never charged beyond your limit**, and blocked or
failed items are reported honestly — not billed as data.

### FAQ & limitations

- Public data only — no login walls, no cookies required.
- Rate limits on the source may require the proxy or a retry on very large pulls.
- Every row reports its own status, so partial results are always labeled, never faked.
- **Integrations:** output works with Zapier, Make, n8n, and any webhook via Apify's integrations.
- **Formats:** results export as JSON, CSV, Excel, or HTML from the dataset.

### Related actors

- **Format Converter**
- **Dataset Profiler**
- **Dataset → Google Sheets**

# Actor input Schema

## `datasetId` (type: `string`):

The Apify dataset to profile (e.g. a scraper run's defaultDatasetId).

## `items` (type: `array`):

Rows to profile directly.

## `sampleSize` (type: `integer`):

Profile only the first N rows (faster for huge datasets). Leave empty to profile all.

## Actor input object example

```json
{
  "items": [
    {
      "id": 1,
      "name": "Ann Lee",
      "email": "ann@example.com",
      "age": 34
    },
    {
      "id": 2,
      "name": "Ben Cole",
      "email": "ben@example.com",
      "age": null
    },
    {
      "id": 3,
      "name": "Ann Lee",
      "email": "ann@example.com",
      "age": 34
    },
    {
      "id": 4,
      "name": "Cy Park",
      "email": null,
      "age": "29"
    }
  ]
}
```

# Actor output Schema

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

The dataset of results produced by this run.

# 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 = {
    "items": [
        {
            "id": 1,
            "name": "Ann Lee",
            "email": "ann@example.com",
            "age": 34
        },
        {
            "id": 2,
            "name": "Ben Cole",
            "email": "ben@example.com",
            "age": null
        },
        {
            "id": 3,
            "name": "Ann Lee",
            "email": "ann@example.com",
            "age": 34
        },
        {
            "id": 4,
            "name": "Cy Park",
            "email": null,
            "age": "29"
        }
    ]
};

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

# Prepare the Actor input
run_input = { "items": [
        {
            "id": 1,
            "name": "Ann Lee",
            "email": "ann@example.com",
            "age": 34,
        },
        {
            "id": 2,
            "name": "Ben Cole",
            "email": "ben@example.com",
            "age": None,
        },
        {
            "id": 3,
            "name": "Ann Lee",
            "email": "ann@example.com",
            "age": 34,
        },
        {
            "id": 4,
            "name": "Cy Park",
            "email": None,
            "age": "29",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("alaudinburki/dataset-profiler").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 '{
  "items": [
    {
      "id": 1,
      "name": "Ann Lee",
      "email": "ann@example.com",
      "age": 34
    },
    {
      "id": 2,
      "name": "Ben Cole",
      "email": "ben@example.com",
      "age": null
    },
    {
      "id": 3,
      "name": "Ann Lee",
      "email": "ann@example.com",
      "age": 34
    },
    {
      "id": 4,
      "name": "Cy Park",
      "email": null,
      "age": "29"
    }
  ]
}' |
apify call alaudinburki/dataset-profiler --silent --output-dataset

```

## MCP server setup

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

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/6dDkmeCLwM38OJ7q2/builds/B0WxErlJ8jyy0De4u/openapi.json