Extract Tables from Wikipedia with AI
How to extract tables from Wikipedia
1. Enter Wikipedia page URL or title
Paste any Wikipedia page URL (e.g., en.wikipedia.org/wiki/Artificial_intelligence) or just the page title. Supports all language editions (de, fr, ja, etc.).
2. AI detects all tables on the page
Our AI identifies infoboxes (key-value summaries), statistical tables, timeline tables, comparison charts, and classification tables — even those inside collapsible sections.
3. Download as Excel or CSV
Export each table as a separate sheet in Excel or individual CSV files, ready for research, analysis, or knowledge base import.
AI vs Manual Wikipedia Table Extraction
Stop copying Wikipedia tables by hand
- × Stop manually copying infobox fields into spreadsheets
- × Stop dealing with multi-row headers and merged cells
- × Stop losing data from collapsed or sortable tables
Let AI extract Wikipedia tables instantly
- ✓ Automatically detects all tables including infoboxes and navboxes
- ✓ Preserves hierarchical structure, footnotes, and references
- ✓ Works across 300+ Wikipedia language editions
Best AI tool for Wikipedia table extraction
Extracts all Wikipedia table types
Infoboxes (key-value attribute lists), statistical tables (population, GDP, election results), comparison tables (product specs, software features), timeline tables, classification tables (taxonomy, sports standings), and sortable data tables.

Preserves multi-language & multi-column layouts
Handles Wikipedia's complex table markup including rowspan/colspan, collapsible sections, and multi-language column headers. Extracted tables retain original structure and footnotes.

Export to Excel, CSV, or JSON
Download infoboxes as structured key-value tables, statistical data as numeric grids, or full datasets for knowledge graph construction and research projects.

Common Wikipedia table extraction use cases
Research & Data Journalism
• Extract country comparison tables (GDP, population, HDI)
• Build historical datasets from election result tables
• Collect scientific classification data (species, minerals)
Knowledge Base & Ontology Building
• Convert infoboxes into structured entity attributes
• Extract product specification tables for comparison engines
• Build academic knowledge graphs from Wikipedia data
Business Intelligence & Market Research
• Extract company infoboxes (revenue, employees, founded)
• Collect software version history tables
• Build competitive landscapes from feature comparison tables



