# Auto Dataset Analysis & HTML Reports (`moving_beacon-owner1/my-actor-74`) Actor
Simply input any tabular dataset, and you'll receive a detailed and elegantly formatted profiling report in returnβno coding skills necessary. This user-friendly process allows you to effortlessly explore the dataset's characteristics and insights, making data analysis accessible to everyone.
- **URL**: https://apify.com/moving\_beacon-owner1/my-actor-74.md
- **Developed by:** [Jamshaid Arif](https://apify.com/moving_beacon-owner1) (community)
- **Categories:** Automation, Developer tools, Integrations
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $10.00 / 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/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.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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
## π Automated dataset analysis and HTML report generation
**Automated dataset analysis and HTML report generation**, inspired by `ydata-profiling` but built natively for the [Apify](https://apify.com) platform.
Feed it any tabular dataset and get back a comprehensive, beautifully styled profiling report β no code required.
---
### What It Does
| Feature | Details |
|---|---|
| **Overview** | Row/column counts, memory usage, missing-cell %, duplicate rows |
| **Per-Column Stats** | Mean, median, std, quartiles, skewness, kurtosis (numeric); top values, avg string length (categorical); min/max/range (datetime) |
| **Histograms** | Auto-binned SVG charts for every numeric column |
| **Correlations** | Full heatmap + auto-alerts for highly correlated pairs |
| **Data Quality Alerts** | Flags high-missing columns and high-correlation pairs |
| **Sample Preview** | First & last 5 rows rendered in the report |
---
### Data Sources
| Source | Input Field |
|---|---|
| **CSV URL** | `sourceUrl` β any publicly accessible `.csv` link |
| **JSON URL** | `sourceUrl` β any publicly accessible `.json` link |
| **Raw JSON** | `rawJson` β paste a JSON array of objects directly |
| **Apify Dataset** | `apifyDatasetId` β ID of an existing Apify dataset |
---
### Input Example
```json
{
"dataSource": "csvUrl",
"sourceUrl": "https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv",
"reportTitle": "Air Travel Dataset Profile",
"sampleRows": 0,
"correlationThreshold": 0.8,
"missingThreshold": 10
}
````
***
### Output
#### 1. HTML Report
Saved to the **Key-Value Store** as `profiling_report.html`. Open it directly in your browser from the actor run's Storage tab.
#### 2. Structured Summary
Pushed to the **Dataset** output for programmatic access β includes overview stats, column profiles, and all alerts as JSON.
***
### Configuration Reference
| Parameter | Type | Default | Description |
|---|---|---|---|
| `dataSource` | enum | `csvUrl` | One of: `csvUrl`, `jsonUrl`, `rawJson`, `apifyDatasetId` |
| `sourceUrl` | string | β | URL to CSV or JSON file |
| `rawJson` | string | β | Raw JSON array of objects |
| `apifyDatasetId` | string | β | Existing Apify dataset ID |
| `reportTitle` | string | `Data Profiling Report` | Title in the HTML report header |
| `sampleRows` | integer | `0` | Max rows to analyze (0 = all) |
| `correlationThreshold` | number | `0.8` | Alert when |correlation| exceeds this |
| `missingThreshold` | number | `10` | Alert when missing % exceeds this |
### License
ISC
# Actor input Schema
## `dataSource` (type: `string`):
How to provide the dataset.
## `sourceUrl` (type: `string`):
URL to a CSV or JSON file. Used when Data Source is 'CSV URL' or 'JSON URL'.
## `rawJson` (type: `string`):
Paste a JSON array of objects directly. Used when Data Source is 'Raw JSON Array'.
## `apifyDatasetId` (type: `string`):
ID of an existing Apify dataset to profile. Used when Data Source is 'Apify Dataset ID'.
## `reportTitle` (type: `string`):
Title shown at the top of the HTML profiling report.
## `sampleRows` (type: `integer`):
Limit analysis to this many rows (0 = use all rows). Useful for very large datasets.
## `correlationThreshold` (type: `number`):
Flag column pairs whose absolute correlation exceeds this value.
## `missingThreshold` (type: `number`):
Flag columns where missing-value percentage exceeds this threshold.
## Actor input object example
```json
{
"dataSource": "csvUrl",
"reportTitle": "Data Profiling Report",
"sampleRows": 0,
"correlationThreshold": 0.8,
"missingThreshold": 10
}
```
# 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 = {
"dataSource": "csvUrl"
};
// Run the Actor and wait for it to finish
const run = await client.actor("moving_beacon-owner1/my-actor-74").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`πΎ 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);
});
// π Want to learn more π? Go to β 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 = { "dataSource": "csvUrl" }
# Run the Actor and wait for it to finish
run = client.actor("moving_beacon-owner1/my-actor-74").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("πΎ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# π Want to learn more π? Go to β https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"dataSource": "csvUrl"
}' |
apify call moving_beacon-owner1/my-actor-74 --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=moving_beacon-owner1/my-actor-74",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Auto Dataset Analysis & HTML Reports",
"description": "Simply input any tabular dataset, and you'll receive a detailed and elegantly formatted profiling report in returnβno coding skills necessary. This user-friendly process allows you to effortlessly explore the dataset's characteristics and insights, making data analysis accessible to everyone.",
"version": "0.0",
"x-build-id": "lbeQwJhvfo3pLjrDU"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/moving_beacon-owner1~my-actor-74/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-moving_beacon-owner1-my-actor-74",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/moving_beacon-owner1~my-actor-74/runs": {
"post": {
"operationId": "runs-sync-moving_beacon-owner1-my-actor-74",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/moving_beacon-owner1~my-actor-74/run-sync": {
"post": {
"operationId": "run-sync-moving_beacon-owner1-my-actor-74",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"required": [
"dataSource"
],
"properties": {
"dataSource": {
"title": "Data Source Type",
"enum": [
"csvUrl",
"jsonUrl",
"rawJson",
"apifyDatasetId"
],
"type": "string",
"description": "How to provide the dataset.",
"default": "csvUrl"
},
"sourceUrl": {
"title": "Source URL",
"type": "string",
"description": "URL to a CSV or JSON file. Used when Data Source is 'CSV URL' or 'JSON URL'."
},
"rawJson": {
"title": "Raw JSON Data",
"type": "string",
"description": "Paste a JSON array of objects directly. Used when Data Source is 'Raw JSON Array'."
},
"apifyDatasetId": {
"title": "Apify Dataset ID",
"type": "string",
"description": "ID of an existing Apify dataset to profile. Used when Data Source is 'Apify Dataset ID'."
},
"reportTitle": {
"title": "Report Title",
"type": "string",
"description": "Title shown at the top of the HTML profiling report.",
"default": "Data Profiling Report"
},
"sampleRows": {
"title": "Max Sample Rows",
"minimum": 0,
"type": "integer",
"description": "Limit analysis to this many rows (0 = use all rows). Useful for very large datasets.",
"default": 0
},
"correlationThreshold": {
"title": "Correlation Alert Threshold",
"minimum": 0,
"maximum": 1,
"type": "number",
"description": "Flag column pairs whose absolute correlation exceeds this value.",
"default": 0.8
},
"missingThreshold": {
"title": "Missing Data Alert (%)",
"minimum": 0,
"maximum": 100,
"type": "number",
"description": "Flag columns where missing-value percentage exceeds this threshold.",
"default": 10
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```