# Wikipedia Table Extractor (`lafuan/wikipedia-data`) Actor

Extract structured data from Wikipedia tables as clean JSON. Supports all languages, multiple tables and header detection. Ideal for AI training data and research.

- **URL**: https://apify.com/lafuan/wikipedia-data.md
- **Developed by:** [Muhammad Naufal](https://apify.com/lafuan) (community)
- **Categories:** AI, Developer tools, Education
- **Stats:** 2 total users, 0 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 and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#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

## 📗 Wikipedia Data Scraper

Extract tables from any Wikipedia page as clean JSON. Supports wikitable, infobox, sortable, and various Wikipedia table layouts.

### Features

- **Table extraction** — wikitable, infobox, sortable, toccolours, plain tables
- **colspan/rowspan** — merged cells properly replicated
- **Page metadata** — categories, short description, last-modified, infobox key-values
- **Section context** — nearest h2/h3 heading for each table
- **Link extraction** — href links from anchor elements within cells
- **Multiple tables** — extract one or all tables on a page
- **Dict or array** — column-header keys or positional col0/col1 format

### Sample output

```json
{
  "page": "https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue",
  "title": "List of largest companies by revenue - Wikipedia",
  "tablesFound": 3,
  "totalRows": 5,
  "metadata": {
    "categories": ["Lists of companies by revenue", "Economic lists"],
    "description": "Wikimedia list article"
  },
  "tables": [
    {
      "tableIndex": 0,
      "tableType": "data",
      "section": "2024 list",
      "headers": ["rank", "company", "country"],
      "count": 5,
      "rows": [
        {"_rowNumber": 1, "rank": "1", "company": "Walmart", "country": "United States", "url": "https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue"},
        {"_rowNumber": 2, "rank": "2", "company": "Saudi Aramco", "country": "Saudi Arabia", "url": "https://en.wikipedia.org/wiki/List_of_largest_companies_by_revenue"}
      ]
    }
  ]
}
```

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `pageUrl` | string | List of largest companies | Full Wikipedia URL |
| `tableIndex` | integer | `0` | Which table (0 = first) |
| `maxResults` | integer | `20` | Max rows per table |
| `outputFormat` | string | `dict` | `dict` (headers as keys) or `array` |
| `includeHeader` | boolean | `false` | Include header row? |
| `allTables` | boolean | `false` | Extract all tables? |
| `extractMetadata` | boolean | `true` | Page categories, description, infobox, last-modified |
| `includeLinks` | boolean | `false` | Extract href links from anchor elements |
| `sectionContext` | boolean | `false` | Include nearest h2/h3 heading per table |

### Pricing

$0.001 per result ($0.50 per 1k rows).

## Actor input object example

```json
{}
```

# 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("lafuan/wikipedia-data").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("lafuan/wikipedia-data").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).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 lafuan/wikipedia-data --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=lafuan/wikipedia-data",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/nzweAqjDEukBAqHNf/builds/4tdI57mxmwpdwG1X1/openapi.json