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Generates, extends, and validates Frictionless Data Package descriptors (datapackage.json) from one or more data files. Supports optional package metadata, schema inference, and AI-powered metadata generation with --autodoc.

# Create datapackage.json for a single file
undatum package create data.csv --output datapackage.json
undatum package create workbook.xlsx --table Sheet2 --output datapackage.json
undatum package create nested.jsonl --flatten-nested --output datapackage.json

# Create a package directory with data file copies
undatum package create data.csv --package-dir out/package

# Zip the materialized package directory
undatum package create data.csv --package-dir out/package --zip out/package.zip

# Add another resource to an existing package
undatum package add-resource out/package/datapackage.json new.csv

# Validate a package descriptor
undatum package validate out/package/datapackage.json

# Provide metadata and enable AI metadata generation
undatum package create data.csv --title "Sales data" --keywords sales,finance \
  --autodoc --ai-provider openai --ai-model gpt-4o-mini

Subcommands:

  • create — generate a new descriptor (default workflow)
  • add-resource — append resources to an existing descriptor
  • validate — validate descriptor structure (full checks with pip install undatum[frictionless])

Metadata options:

  • --name, --title, --description, --keywords
  • --licenses (semicolon-separated entries, e.g. name=MIT;name=ODC-PDDL-1.0)
  • --sources (semicolon-separated entries, e.g. title=World Bank,path=https://...)
  • --contributors (semicolon-separated entries, e.g. title=Jane Doe,email=jane@example.com)
  • --version - Package version string

Features:

  • Frictionless profile: Emits profile: tabular-data-package with resource format/mediatype
  • Schema inference: Automatically infers field types, descriptions, and uniqueness constraints
  • Multiple resources: Package multiple files as separate resources
  • Remote URIs: Support for HTTP/HTTPS URLs as resource paths
  • Package directory: Bundle datapackage.json with data file copies
  • AI metadata: Use --autodoc to generate metadata with AI assistance (single-pass, no duplicate LLM calls)
  • Streaming-safe: Processes large datasets without loading everything into memory
  • Python SDK: Dataset.read("data.csv").package(output="datapackage.json")

Additional options:

  • --package-dir: Create a package directory with data file copies
  • --zip: Create a ZIP archive of the package directory (requires --package-dir)
  • --autodoc: Enable AI-powered metadata generation (reuses doc command logic)
  • --engine: Processing engine (auto or duckdb)
  • --delimiter, --encoding, --tagname, --start-line, --start-page: Passed through to analysis and sampling
  • --objects-limit: Maximum objects to analyze for schema inference (default: 10000)
  • --sample-size: Number of sample records for metadata inference (default: 10)