sheetwise 2.8.0

A Python package for encoding spreadsheets for Large Language Models, implementing the SpreadsheetLLM research framework

SheetWise

A Python package for encoding spreadsheets for Large Language Models, implementing the SpreadsheetLLM research framework.

Overview

SheetWise is a Python package that implements the key components from Microsoft Research's SpreadsheetLLM paper for efficiently encoding spreadsheets for use with Large Language Models. The package provides:

• SheetCompressor: Efficient encoding framework with three compression modules
• Chain of Spreadsheet: Multi-step reasoning approach for spreadsheet analysis
• Vanilla Encoding: Traditional cell-by-cell encoding methods
• Token Optimization: Significant reduction in token usage
• Formula Analysis: Extract and simplify Excel formulas
• Multi-Sheet Support: Process entire workbooks with cross-sheet references
• Visualization Tools: Generate visual reports of compression results

Key Features

• Intelligent Compression: Up to 96% reduction in token usage while preserving semantic information
• Auto-Configuration: Automatically optimizes compression settings based on spreadsheet characteristics
• Multi-Table Support: Handles complex spreadsheets with multiple tables and regions
• Structural Analysis: Identifies and preserves important structural elements
• Format-Aware: Preserves data type and formatting information
• Enhanced Algorithms: Improved range detection and contiguous cell grouping
• Easy Integration: Simple API for immediate use

Token Budgeting

Unsure if your spreadsheet fits in the context window? Use encode_to_token_limit:

from sheetwise import SpreadsheetLLM
import pandas as pd

df = pd.read_excel("large_file.xlsx")
sllm = SpreadsheetLLM()

# Automatically adjust compression to fit within 4000 tokens
encoded_text = sllm.encode_to_token_limit(df, max_tokens=4000)

Memory Optimization (Large Files)

For very large spreadsheets (100MB+), you can use inplace=True to significantly reduce RAM usage during compression.

# Standard Compression (Creates a copy in RAM)
result = compressor.compress(df)

# Memory-Efficient Compression (Modifies DataFrame in-place or minimizes copies)
# Use this when working with datasets close to your RAM limit.
result = compressor.compress(df, inplace=True)

Installation

Using pip

pip install sheetwise

Using Poetry

poetry add sheetwise

Development Installation

git clone https://github.com/yourusername/sheetwise.git
cd sheetwise
poetry install

Quick Start

Basic Usage

import pandas as pd
from sheetwise import SpreadsheetLLM

# Initialize the framework
sllm = SpreadsheetLLM()

# Load your spreadsheet
df = pd.read_excel("your_spreadsheet.xlsx")

# Compress and encode for LLM use
llm_ready_text = sllm.compress_and_encode_for_llm(df)

# Copy and paste this text directly into ChatGPT/Claude
print(llm_ready_text)

Advanced Usage

from sheetwise import SpreadsheetLLM, SheetCompressor

# Auto-configuration
sllm = SpreadsheetLLM(enable_logging=True)
auto_compressed = sllm.compress_with_auto_config(df)  # Automatically optimizes settings

# Manual configuration
compressor = SheetCompressor(
    k=2,  # Structural anchor neighborhood size
    use_extraction=True,
    use_translation=True, 
    use_aggregation=True
)

# Compress the spreadsheet
compressed_result = compressor.compress(df)
print(f"Compression ratio: {compressed_result['compression_ratio']:.1f}x")
print(f"Compressed shape: {compressed_result['compressed_df'].shape}")

# Or use with SpreadsheetLLM for full pipeline
sllm = SpreadsheetLLM(compression_params={
    'k': 2,
    'use_extraction': True,
    'use_translation': True, 
    'use_aggregation': True
})

# Get detailed statistics
stats = sllm.get_encoding_stats(df)
print(f"Token reduction: {stats['token_reduction_ratio']:.1f}x")

# Process QA queries
result = sllm.process_qa_query(df, "What was the total revenue in 2023?")

Enhanced Features Usage (v2.0+)

from sheetwise import (
    SpreadsheetLLM, 
    FormulaParser, 
    WorkbookManager, 
    CompressionVisualizer, 
    SmartTableDetector
)

# Formula extraction and analysis
formula_parser = FormulaParser()
formulas = formula_parser.extract_formulas("your_spreadsheet.xlsx")
formula_parser.build_dependency_graph()
impact = formula_parser.get_formula_impact("Sheet1!A1")
formula_text = formula_parser.encode_formulas_for_llm()

# Multi-sheet support
workbook = WorkbookManager()
sheets = workbook.load_workbook("your_workbook.xlsx")
cross_refs = workbook.detect_cross_sheet_references()
sllm = SpreadsheetLLM()
compressed = workbook.compress_workbook(sllm.compressor)
encoded = workbook.encode_workbook_for_llm(compressed)

# Visualization
visualizer = CompressionVisualizer()
df = sllm.load_from_file("your_spreadsheet.xlsx")
compressed_result = sllm.compress_spreadsheet(df)
fig = visualizer.create_data_density_heatmap(df)
fig.savefig("heatmap.png")
html_report = visualizer.generate_html_report(df, compressed_result)

# Advanced table detection
detector = SmartTableDetector()
tables = detector.detect_tables(df)
extracted_tables = detector.extract_tables_to_dataframes(df)

Command Line Interface

# Basic usage
sheetwise input.xlsx -o output.txt --stats

# Auto-configure compression
sheetwise input.xlsx --auto-config --verbose

# Run demo with sample data
sheetwise --demo --auto-config

# Use vanilla encoding instead of compression
sheetwise input.xlsx --vanilla

# Output in JSON format
sheetwise input.xlsx --format json

Enhanced CLI Features (v2.0+)

# Extract and analyze formulas
sheetwise your_spreadsheet.xlsx --extract-formulas

# Process all sheets in a workbook
sheetwise your_workbook.xlsx --multi-sheet

# Generate visualizations
sheetwise your_spreadsheet.xlsx --visualize

# Detect and extract tables
sheetwise your_spreadsheet.xlsx --detect-tables

# Generate an HTML report
sheetwise your_spreadsheet.xlsx --format html

Benchmarks & Visualization

SheetWise includes a benchmarking script to evaluate compression, speed, and memory usage across spreadsheets. This helps you understand performance and compare results visually.

Running Benchmarks

1. Place your sample spreadsheets in benchmarks/samples/ (supports .xlsx and .csv).
2. Run the benchmark script:

python scripts/generate_benchmarks.py

3. Results and charts will be saved in benchmarks/results/ and benchmarks/charts/.

---

Core Components

1. SheetCompressor

The main compression framework with three modules:

• Structural Anchor Extraction: Identifies and preserves structurally important rows/columns
• Inverted Index Translation: Creates efficient value-to-location mappings
• Data Format Aggregation: Groups cells by data type and format

2. Chain of Spreadsheet

Multi-step reasoning approach:

1. Table Identification: Automatically detects table regions
2. Compression: Applies SheetCompressor to reduce size
3. Query Processing: Identifies relevant regions for specific queries

3. Enhanced Modules (v2.0+)

• FormulaParser: Extracts and analyzes Excel formulas
• WorkbookManager: Handles multi-sheet workbooks and cross-references
• CompressionVisualizer: Generates visualizations and reports
• SmartTableDetector: Advanced table detection and classification

Examples

Working with Financial Data

from sheetwise import SpreadsheetLLM
from sheetwise.utils import create_realistic_spreadsheet

# Create sample financial spreadsheet
df = create_realistic_spreadsheet()

sllm = SpreadsheetLLM()

# Analyze the data
stats = sllm.get_encoding_stats(df)
print(f"Original size: {stats['original_shape']}")
print(f"Sparsity: {stats['sparsity_percentage']:.1f}% empty cells")
print(f"Compression: {stats['compression_ratio']:.1f}x smaller")

# Generate LLM-ready output
encoded = sllm.compress_and_encode_for_llm(df)
print("\nReady for LLM:")
print(encoded[:300] + "...")

Visualizing Compression

from sheetwise import SpreadsheetLLM, CompressionVisualizer
import pandas as pd

# Load your data
df = pd.read_excel("complex_spreadsheet.xlsx")

# Compress the data
sllm = SpreadsheetLLM()
compressed_result = sllm.compress_spreadsheet(df)

# Create visualizations
visualizer = CompressionVisualizer()

# Generate heatmap of data density
fig1 = visualizer.create_data_density_heatmap(df)
fig1.savefig("density_heatmap.png")

# Compare original vs compressed
fig2 = visualizer.compare_original_vs_compressed(df, compressed_result)
fig2.savefig("compression_comparison.png")

# Generate HTML report with all visualizations
html_report = visualizer.generate_html_report(df, compressed_result)
with open("compression_report.html", "w") as f:
    f.write(html_report)

# Compare different compression strategies
configs = [
    {"name": "Extraction Only", "use_translation": False, "use_aggregation": False},
    {"name": "Translation Only", "use_extraction": False, "use_aggregation": False}, 
    {"name": "All Modules", "use_extraction": True, "use_translation": True, "use_aggregation": True}
]

for config in configs:
    compressor = SheetCompressor(**{k: v for k, v in config.items() if k != "name"})
    result = compressor.compress(df)
    print(f"{config['name']}: {result['compression_ratio']:.1f}x compression")

Performance

SpreadsheetLLM achieves significant improvements over vanilla encoding:

Metric	Vanilla	SpreadsheetLLM	Improvement
Token Count	~25,000	~1,200	96% reduction
Sparsity Handling	Poor	Excellent	Removes empty regions
Multi-Table Support	Limited	Native	Preserves structure
Format Preservation	Basic	Advanced	Type-aware grouping

API Reference

SpreadsheetLLM Class

The main interface for the framework.

Methods

• compress_and_encode_for_llm(df): One-step compression and encoding
• compress_spreadsheet(df): Apply compression pipeline
• encode_vanilla(df): Traditional encoding
• get_encoding_stats(df): Detailed compression statistics
• process_qa_query(df, query): Chain of Spreadsheet reasoning
• load_from_file(filepath): Load spreadsheet from file

SheetCompressor Class

Core compression framework.

Parameters

• k: Structural anchor neighborhood size (default: 4)
• use_extraction: Enable structural extraction (default: True)
• use_translation: Enable inverted index translation (default: True)
• use_aggregation: Enable format aggregation (default: True)

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository

git clone https://github.com/yourusername/sheetwise.git
cd sheetwise

# Install development dependencies
poetry install

# Run tests
poetry run pytest

# Run linting
poetry run black src tests
poetry run isort src tests
poetry run flake8 src tests

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=src

# Run specific test file
poetry run pytest tests/test_core.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use SpreadsheetLLM in your research, please cite:

@article{spreadsheetllm2024,
  title={SpreadsheetLLM: Encoding Spreadsheets for Large Language Models},
  author={Microsoft Research Team},
  journal={arXiv preprint},
  year={2024}
}

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