Tables mirror the structure of the data your robot extracts:
Each robot has its own dedicated Table.
The number of tabs depends on what (and how) your robot captured the data.
Tables are automatically structured based on how the robot was trained.
The Main tab: Your extraction summary
Every Table includes a Main tab that serves as your command center, showing one row for each time your robot runs.
Core columns
Extract Date: when the extraction occurred
Status: success, failed, queued, or in-progress
Origin URL: the webpage that was scraped
Dynamic columns
The Main tab will look different based on the data you're extracting:
Extracting a list of data
Max [List Name] - maximum rows you set to extract
[List name] - number of rows extracted
Extracting text
Each piece of 'text' that you click to capture will be represented as a column of data
These show the actual values captured (e.g., Price, Title, Description)
Input parameters
[Input parameter] - custom input parameters such as a search term.
List tabs: Your detailed data
When your robot extracts lists (products, search results, articles), each list gets its own dedicated tab.
For example - here's how a list tab appears for an Amazon keyword rank tracker.
Notice how the structure includes:
Input parameter column (Search Keyword): shows what triggered this extraction.
List limit (Max Products) - the maximum items setting.
Extract Date - when this was captured.
Data fields - each trained data point (Position, Title, Product link, Image, Price) gets its own column.
One row per item - each product found in the search results creates its own row.
💡 All rows in a list tab from the same extraction run will share the same Extract Date and input parameters, making it easy to group related data.
Cleaning and formatting data with calculated columns
While Tables automatically organize your scraped data, you can add calculated columns to clean, format, and transform any data you capture. You can:
Clean messy data: remove extra spaces, fix formatting inconsistencies.
Extract specific information: pull numbers from text, split combined fields.
Perform calculations: add formulas for pricing analysis, date calculations.
Standardize formats: convert dates, currencies, or text to consistent formats.
Create new insights - combine multiple columns to generate new data points.
💡 Calculated columns are perfect for preparing data for export without needing external tools. For example, you can clean product prices by removing currency symbols, or calculate days since an item was listed.
📖 To learn all about calculated columns, you can read our guide.
How data flows through Tables
Understanding the data flow helps you track information:
Robot runs → creates a new row in the Main tab
Lists extracted → populate their respective tabs with detailed data
Individual values → appear as columns in the Main tab
Historical runs → stack chronologically, creating your database
Table limitations
Tables have some structural constraints:
Fixed column order - you cannot rearrange columns.
Automatic tab generation - you cannot manually add custom tabs.
Permanent data storage - you cannot delete rows or data.
Structure tied to robot - changing extraction requires retraining.
💡 Think of Tables as a specialized database rather than a spreadsheet. They're optimized for storing and retrieving robot-extracted data, not for visual data display.