# 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/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

## 📗 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 (incl. multi-column rowspans)
- **2-row thead headers** — group + subheader rows merged (`subheader (group)`), with correct handling for both group-on-bottom (labels under columns) and group-on-top (coarse group row above the real column names) layouts
- **Row-header tables** — tables without a header row whose data rows lead with `<th>` (e.g. `Country | US | Canada`) get the leading `<th>` as the column name plus positional `col1..colN`
- **Clean cell text** — footnote/citation markers (`[1]`), edit links, "citation needed" tags, and `<abbr>` stripped from values (abbr titles preserved: `<abbr title="United States">US</abbr>` → `United States`); `<br>` word-splits in headers/cells keep their space (`Total<br>in km2` → `Total in km2`)
- **Sortable tables** — human-readable display text kept; raw sort key exposed as `{key}_sortValue` (dict) or parallel `_sortValues` array (array format)
- **Image-only cells** — X/check icon cells fall back to `data-sort-value` or `img[alt]` text
- **Multi-language** — relative links resolve against the page's own origin (fr.wikipedia.org stays on fr.wikipedia.org); bare article titles / mobile URLs (`m.wikipedia.org`) auto-normalized to `en.wikipedia.org`
- **Page metadata** — categories, short description, last-modified, infobox key-values, and accurate page stats (edit count, page length, creator, latest editor, watchers) pulled from Wikipedia's page-info page
- **Identity metadata** — pageId, revisionId, wikidataId, canonicalTitle, namespaceName, pageName, protectionLevel, lastEditor, language
- **Page classification** — `isRedirect` (+ `redirectedFrom`), `isDisambiguation`, `isStub` flags
- **Section context** — nearest h2/h3 heading for each table
- **Link extraction** — href links from anchor elements within cells (footnote/anchor links filtered out)
- **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",
    "pageId": "27453603",
    "wikidataId": "Q918765",
    "sitelinksCount": 38,
    "talkPageUrl": "https://en.wikipedia.org/wiki/Talk:List_of_largest_companies_by_revenue",
    "editCount": "15699",
    "contributorsCount": "412",
    "lastEdited": "2026-07-28T09:14:22Z"
  },
  "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 (bare article titles and `m.wikipedia.org` links also accepted) |
| `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 | `false` | Page categories, description, infobox, last-modified, page stats |
| `includeLinks` | boolean | `false` | Extract href links from anchor elements |
| `sectionContext` | boolean | `false` | Include nearest h2/h3 heading per table |
| `caption` | string | — | Table `<caption>` text auto-extracted |

**New metadata fields (when extractMetadata=true):**

- `lastEdited` — ISO date of the latest edit (from Wikipedia's page-info page)
- `pageLength` — page content length in bytes (accurate, from page-info)
- `editCount` — total number of edits to the page (accurate, from page-info)
- `pageId` — Wikipedia article ID
- `wikiDataId` — Wikidata entity ID (Q...)
- `revisionId` — current revision number
- `canonicalTitle` — page title (wgTitle)
- `namespaceName` — namespace name (e.g. "Talk", "Template")
- `pageName` — full page path with underscores (wgPageName)
- `protectionLevel` — edit protection level(s), comma-joined
- `lastEditor` — username of the last editor
- `language` — page language code
- `pageCreator` — username of the page creator
- `createdOn` — date the page was created (ISO when parseable)
- `watchersCount` — number of page watchers
- `templateCount` — number of transcluded templates
- `contributorsCount` — number of distinct page contributors (from page-info)
- `sitelinksCount` — number of Wikidata sitelinks for the article
- `talkPageUrl` — URL of the article's talk page
- `isOrphan` — set to `true` when the article has no incoming links from other articles
- `incomingLinksCount` — number of incoming article links (article namespace only, from the backlinks API; capped at ~1500)
- `externalLinksCount` — number of distinct external links in the article content
- `referencesCount` — number of reference-list items in the article
- `sections` — list of the article's section headings (h2)
- `isRedirect` / `redirectedFrom` — set when the requested URL was a redirect
- `isDisambiguation` — set on disambiguation pages
- `isStub` — set on stub pages

### 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(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 lafuan/wikipedia-data --silent --output-dataset

```

## MCP server setup

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

```

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/nzweAqjDEukBAqHNf/builds/AX8a48j7PXgsGTZNy/openapi.json