koza
Details
| GitHub | monarch-initiative/koza |
| Language | Python |
| Description | Data transformation framework for LinkML data models |
Dependencies
External Dependencies
| Package | Version |
|---|---|
| coverage | >=7.13.0 |
| duckdb | |
| loguru | |
| biolink-model | >=4.3.6 |
| mergedeep | ==1.3.4 |
| ordered-set | >=4.1.0 |
| orjson | >=3.10 |
| pydantic | >=2.12.5 |
| pyyaml | >=6.0.3 |
| requests | >=2.32.5 |
| sssom | >=0.4 |
| tqdm | >=4.67.1 |
| typer | >=0.20.0 |
| LinkML | >=1.9.0 |
Documentation
Koza - Knowledge Graph Transformation and Operations Toolkit
Overview
Koza is a Python library and CLI tool for transforming biomedical data and performing graph operations on Knowledge Graph Exchange (KGX) files. It provides two main capabilities:
📊 Graph Operations (New!)
Powerful DuckDB-based operations for KGX knowledge graphs:
- Join multiple KGX files with schema harmonization
- Split files by field values with format conversion
- Prune dangling edges and handle singleton nodes
- Append new data to existing databases with schema evolution
- Multi-format support for TSV, JSONL, and Parquet files
🔄 Data Transformation (Core)
Transform biomedical data sources into KGX format:
- Transform csv, json, yaml, jsonl, and xml to target formats
- Output in KGX format
- Write data transforms in semi-declarative Python
- Configure source files, columns/properties, and metadata in YAML
- Create mapping files and translation tables between vocabularies
Installation
Koza is available on PyPi and can be installed via pip/pipx:
[pip|pipx] install koza
Usage
See the Koza documentation for complete usage information.
Key Features
🔧 Multi-Format Support
- Native support for TSV, JSONL, and Parquet KGX files
- Automatic format detection and conversion
- Mixed-format operations in single commands
🛡️ Schema Flexibility
- Automatic schema harmonization across heterogeneous files
- Schema evolution with backward compatibility
- Comprehensive schema reporting and validation
⚡ High Performance
- DuckDB-powered operations for fast bulk processing
- Memory-efficient handling of large knowledge graphs
- Parallel processing and streaming where possible
🔍 Rich CLI Experience
- Progress indicators for long-running operations
- Detailed statistics and operation summaries
- Dry-run modes for safe operation preview
🧹 Data Integrity
- Dangling edge detection and preservation
- Duplicate detection and removal strategies
- Non-destructive operations with data archiving