Grooper Help - Version 25.0
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Data Table

Data Field Container Grooper.Core

Represents a configurable, extractable table structure for capturing and validating tabular data from documents.

Remarks

Overview

A Data Table defines the structure, extraction logic, and validation rules for tabular data within a document. It is used to capture repeating data—such as line items, transactions, or lists—by organizing content into rows and columns.

Data Tables are highly flexible and can model a wide range of tabular layouts, from simple lists to complex multi-column grids, and even non-traditional tables such as delimited or pattern-based repeating data.

Key Features

  • Tabular Extraction: Supports extraction of data arranged in rows and columns, including grid-like tables, delimited lists, and pattern-based repeating data.
  • Configurable Columns: Each Data Table contains one or more Data Columns as children, defining the fields to extract for each row.
  • Flexible Extraction Methods: Multiple Table Extract Methods are available to target different table layouts, including:
    • Tabular Layout (header-driven, grid-based)
    • Row Match (pattern-based row extraction)
    • Grid Layout, Delimited, Fixed Width, and more
  • Validation and Row Count Control: Properties such as 'Row Count Range' and 'Initial Row Count' allow you to enforce minimum/maximum row counts and ensure data completeness.
  • UI and Display Options: Control how tables are displayed, including maximum visible rows, dynamic column ordering, and whether to hide empty tables.
  • Footer and Totals: Optionally generate a footer row for totals or summary data, with support for computed columns.
  • Scripting and Events: Supports event-driven scripting for custom validation and post-processing.

Usage Scenarios

  • Invoice Line Items: Extract itemized lists of products or services from invoices.
  • Transaction Logs: Capture repeating transaction records from statements or logs.
  • Delimited Lists: Parse lists of values separated by delimiters (e.g., CSV, TSV).
  • Pattern-Based Tables: Extract repeating data using regular expressions or custom patterns.

Extraction Workflow

  1. The configured Table Extract Method is used to locate and extract rows from the document.
  2. Each Data Column extracts its value for each row, using its own extractor or by mapping named groups from the row pattern.
  3. Validation is performed based on row count and column requirements.
  4. The resulting Table Instance contains Table Row Instance objects, each with cell values for the defined columns.

Configuration and Best Practices

  • Choose the Table Extract Method that best matches your document's table layout.
  • Define Data Columns for each field you wish to extract from each row.
  • Use 'Row Count Range' and 'Initial Row Count' to enforce data completeness and add blank rows if needed.
  • Enable 'Dynamic Column Ordering' if column order varies between documents.
  • Use 'Generate Footer Row' and column footer modes to compute totals or summaries.
  • Hide empty tables with 'Hide If Empty' to reduce UI clutter.

Example

For an invoice with a line item table:

Extraction will produce a table instance with one row per line item, and columns populated with the extracted values.


For more information, see the documentation for Data Column, Table Extract Method, Table Instance, and related data extraction objects.

Properties

NameTypeDescription
General
Import Source String

Used with import operations.. The source element name.

Behavior
Appearance

Design Tabs

General View properties of this element, along with a preview of its layout in a Data Grid.
Reports View reports for a node.
Scripting Create, debug, modify, and compile scripts for scriptable nodes.
Tester Test data extraction for a Data Element.
Advanced View or edit advanced details about a node.

Child Types

See Also

Used By

Notification