copydata 0.1.3

CLI to compare two tabular datasets and produce a concise markdown report

Data Whisperer

Intelligent dataset comparison that reveals what truly changed.

Data Whisperer (Copydata) is a command-line tool that compares two versions of tabular datasets and generates insightful reports highlighting meaningful changes. Unlike simple diff tools, it understands your data's structure and semantics.

Features

• 🔍 Smart Column Matching - Automatically detects renamed columns using similarity scoring
• 📊 Statistical Analysis - Compares numeric distributions, outliers, and trends
• 🏷️ Category Tracking - Identifies added/removed categories and value changes
• 📈 Change Scoring - Prioritizes the most significant changes for quick review
• 📝 Multiple Formats - Supports CSV, Excel (XLS/XLSX), and JSON files
• 📄 Flexible Output - Generate markdown reports and/or machine-readable JSON

Installation

pip install data-whisperer

Quick Start

Compare two datasets:

copydata old_data.csv new_data.csv

Save report to file:

copydata data_v1.xlsx data_v2.xlsx --output-save --output report.md

Generate both markdown and JSON output:

copydata before.csv after.csv --output-save --json

Usage

copydata [-h] [--output OUTPUT] [--output-save] [--json] [--rename-threshold THRESHOLD] a b

Arguments

• a - Path to dataset A (older version)
• b - Path to dataset B (newer version)

Options

• --output, -o - Output filename for markdown report (default: copydata_report.md)
• --output-save - Save reports to files instead of printing to stdout
• --json, -j - Also generate JSON output with full comparison data
• --rename-threshold - Similarity threshold for detecting renamed columns (default: 0.82)

What Does It Analyze?

Summary Statistics

• Row count changes
• Overall null value percentages
• Duplicate row detection

Structural Changes

• Added columns
• Removed columns
• Renamed columns (with similarity scores)

Column-Level Analysis

For Numeric Columns:

• Mean, median, and standard deviation
• Min/max value changes
• Outlier detection using IQR method
• Percentage changes in key metrics

For Categorical Columns:

• Unique value counts
• Top value distributions
• New categories added
• Categories removed
• Common category overlap

Example Output

# Data Whisperer Report

## Summary
- Row count A: 1000, B: 1200, Δ: 200
- Total nulls A: 50 (0.50%), B: 75 (0.62%)
- Duplicate rows A: 5, B: 3, Δ: -2

## Structural Changes
- Added columns (1): customer_segment
- Removed columns (0): None
- Probable renames (1):
  - user_id -> customer_id (similarity 0.850)

## Column Level Changes
### revenue
- Type A: numeric, Type B: numeric
- Mean: A: 1.23K, B: 1.45K
- Mean % change: 17.89%
- Outliers A: 12, B: 18

Requirements

• Python 3.7+
• pandas
• numpy

Use Cases

• Data Pipeline Monitoring - Track changes in daily/weekly data refreshes
• Model Retraining - Understand how training data evolved between versions
• ETL Validation - Verify transformations produced expected changes
• Schema Migration - Document structural changes during database updates
• Data Quality Auditing - Identify unexpected changes in production data

Advanced Features

Column Rename Detection

Data Whisperer uses fuzzy string matching to detect renamed columns. Adjust sensitivity:

copydata old.csv new.csv --rename-threshold 0.9  # More strict
copydata old.csv new.csv --rename-threshold 0.7  # More lenient

Type Inference

Automatically classifies columns as:

• Numeric - For statistical analysis
• Categorical - For tracking value changes (≤20 unique values)
• Text - For high-cardinality strings
• Datetime - For temporal data

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

License

MIT License

Author

Data Whisperer - Making dataset evolution transparent and actionable.

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Focus on meaningful numeric shifts, category churn, and schema changes.