Data: CSV Advanced Operations (Low Priority)

[jwesleye]

Overview

Add advanced CSV operations that maintain memory efficiency while providing more complex data transformations.

Motivation

We have 18 CSV functions including token-saving inspection tools. These advanced operations would enable more complex workflows while staying memory-efficient for large files.

Proposed Functions

Medium Priority - Data Transformation

• sort_csv_rows - Sort by column (with sampling strategy for large files)
• aggregate_csv_column - Compute sum/avg/min/max for column without full load
• deduplicate_csv_rows - Remove duplicates by column(s) with memory-efficient streaming

Medium Priority - File Operations

• merge_csv_files - Combine multiple CSV files (horizontal or vertical merge)
• split_csv_by_column - Split into multiple files based on column value
• transpose_csv - Swap rows and columns (memory-efficient for large files)

Lower Priority - Advanced Filtering

• join_csv_files - SQL-style join of two CSV files on key column
• pivot_csv_data - Create pivot table from CSV data
• group_csv_rows - Group rows by column value with aggregations

Design Principles

• Google ADK compliant (JSON-serializable types, no defaults)
• @strands_tool decorator
• Memory-efficient (streaming/chunking for large files)
• Include skip_confirm for file creation operations
• Consistent with existing CSV tools pattern
• Avoid loading entire files when possible

Related

• Extends existing data/csv_tools.py (18 functions)
• Related to issue Data: Future Enhancement Features #57 (Data Future Enhancements)
• Complements token-saving tools like select_csv_columns, filter_csv_rows

Module

data/csv_tools.py