> ## Documentation Index
> Fetch the complete documentation index at: https://docs.askui.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Table Data Extraction

> Extract structured data from tables in your UI

Tables are common UI elements for displaying structured data. Learn how to extract table data into structured Python objects using Pydantic models.

## Basic Table Extraction

```python theme={null}
from askui import VisionAgent, ResponseSchemaBase
from typing import List

class Entry(ResponseSchemaBase):
    company: str
    contact: str
    country: str

class Table(ResponseSchemaBase):
    rows: List[Entry]

with VisionAgent() as agent:
    agent.tools.webbrowser.open_new("https://www.w3schools.com/html/html_tables.asp")
    agent.wait(3)  # Wait for page to load

    result = agent.get("Can you extract me the table?", response_schema=Table)
    print(result.model_dump_json(indent=2))
```

## Best Practices

1. **Match Schema to Table Structure**: Design your Pydantic model to match the actual table columns
   ```python theme={null}
   # Look at the table first
   headers = agent.get("What are the table headers?", response_schema=List[str])
   print(f"Table columns: {headers}")

   # Then design your model accordingly
   ```

2. **Type Conversion**: Specify correct types for numeric columns
   ```python theme={null}
   class DataRow(ResponseSchemaBase):
       product: str
       quantity: int  # Will convert "5" to 5
       price: float   # Will convert "$9.99" to 9.99
   ```