sunstone-py 1.10.0

Python library for managing datasets with lineage tracking in Sunstone projects

sunstone-py

A Python library for managing datasets with lineage tracking in data science projects.

Features

• Automatic Lineage Tracking: Track data provenance through all operations automatically
• Dataset Management: Integration with datasets.yaml for organized dataset registration
• Pandas-Compatible API: Familiar pandas-like interface via from sunstone import pandas as pd (CSV, Excel, JSON)
• Plugin System: Extensible architecture for custom auth providers, URL handlers, and format handlers via entry points
• Strict/Relaxed Modes: Control whether operations can modify datasets.yaml
• Validation Tools: Check notebooks and scripts for correct import usage
• Full Type Hints: Complete type hint support for better IDE integration

Installation

# Using uv (recommended)
uv add sunstone-py

# Using pip
pip install sunstone-py

To use the latest commit from github:

dependencies = [
    "sunstone-py @ git+https://github.com/sunstoneinstitute/sunstone-py.git",
]

If you are making changes to a local checkout of sunstone-py and want to test them from your project, add a [tool.uv.sources] override to your project's pyproject.toml:

[tool.uv.sources]
sunstone-py = { path = "../path/to/sunstone-py", editable = true }

The path is relative to your project's pyproject.toml. Leave the regular PyPI dependency in [project.dependencies] unchanged — the sources override takes precedence locally. Remember to remove the [tool.uv.sources] block before committing.

For Development

git clone https://github.com/sunstoneinstitute/sunstone-py.git
cd sunstone-py
uv venv
uv sync
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Quick Start

1. Set Up Your Project with datasets.yaml

Create a datasets.yaml file in your project directory:

inputs:
  - name: School Data
    slug: school-data
    location: data/schools.csv
    source:
      name: Ministry of Education
      location:
        data: https://example.com/schools.csv
      attributedTo: Ministry of Education
      acquiredAt: 2025-01-15
      acquisitionMethod: manual-download
      license: CC-BY-4.0
    fields:
      - name: school_id
        type: string
      - name: enrollment
        type: integer

outputs: []

2. Use Pandas-Like API with Lineage Tracking

from sunstone import pandas as pd
from pathlib import Path

# Set project path (where datasets.yaml lives)
PROJECT_PATH = Path.cwd()

# Read data - lineage automatically tracked
df = pd.read_csv('data/schools.csv', project_path=PROJECT_PATH)

# Transform using familiar pandas operations
result = df[df['enrollment'] > 100].groupby('district').sum()

# Save with automatic lineage tracking and dataset registration
result.to_csv(
    'outputs/summary.csv',
    slug='school-summary',
    name='School Enrollment Summary',
    index=False
)

3. Check Lineage Metadata

# View lineage information
print(result.lineage.sources)      # Source datasets
print(result.lineage.operations)   # Operations performed
print(result.lineage.get_licenses())  # All source licenses

Core Concepts

Pandas-Like API

sunstone-py provides a drop-in replacement for pandas that adds lineage tracking:

from sunstone import pandas as pd

# Works like pandas, but tracks lineage
df = pd.read_csv('input.csv', project_path='/path/to/project')
df2 = pd.read_csv('input2.csv', project_path='/path/to/project')

# All pandas operations work
filtered = df[df['value'] > 100]
grouped = df.groupby('category').sum()

# Merge/join operations combine lineage from both sources
merged = pd.merge(df, df2, on='key')
concatenated = pd.concat([df, df2])

Strict vs Relaxed Mode

Relaxed Mode (default):

• Writing to new outputs auto-registers them in datasets.yaml
• More flexible for exploratory work

Strict Mode:

• All reads and writes must be pre-registered in datasets.yaml
• Ensures complete documentation of data operations
• Enable via strict=True parameter or SUNSTONE_DATAFRAME_STRICT=1 environment variable

# Enable strict mode
df = pd.read_csv('data.csv', project_path=PROJECT_PATH, strict=True)

# Or globally
import os
os.environ['SUNSTONE_DATAFRAME_STRICT'] = '1'

Validation Tools

Check notebooks for correct import usage:

import sunstone

# Check a single notebook
result = sunstone.check_notebook_imports('analysis.ipynb')
print(result.summary())

# Check all notebooks in project
results = sunstone.validate_project_notebooks('/path/to/project')
for path, result in results.items():
    if not result.is_valid:
        print(f"\n{path}:")
        print(result.summary())

Plugin System

sunstone-py uses a plugin architecture for reading, writing, and fetching data. Built-in handlers cover common formats (CSV, JSON, Excel, Parquet, TSV) and HTTP/HTTPS, local file, GCS, and S3/R2 URLs.

Plugin Protocols

Plugins implement one or more of these protocols:

• AuthProvider: Injects authentication headers into HTTP requests
• URLHandler: Opens URLs for reading/writing, returning file-like streams (BinaryIO/TextIO)
• FormatHandler: Reads and writes data formats not built into sunstone

Installation Extras

pip install sunstone-py          # Core + HTTP + local file handling
pip install sunstone-py[gcs]     # Adds GCS (gs://) support
pip install sunstone-py[s3]      # Adds S3 (s3://) and R2 (r2://) support
pip install sunstone-py[gcs,s3]  # Both

Registering Custom Plugins

Plugins are discovered via Python entry points:

[project.entry-points."sunstone.plugins"]
my-plugin = "my_package:MyPlugin"

Plugin Configuration

Plugin config uses cascading precedence (later sources override earlier):

1. datasets.yaml — plugins.<name> section
2. pyproject.toml — [tool.sunstone.plugins.<name>] table
3. Environment variables — SUNSTONE_PLUGIN_<NAME>_<KEY>

Advanced Usage

Direct DataFrame API

For more control, use the DataFrame class directly:

from sunstone import DataFrame

# Read with explicit parameters
df = DataFrame.read_csv(
    'data.csv',
    project_path='/path/to/project',
    strict=True
)

# Access underlying pandas DataFrame
pandas_df = df.data

DataFrame Metadata

Set metadata on DataFrames that flows through to datasets.yaml on write:

from sunstone import pandas as pd

df = pd.read_csv('input.csv', project_path=PROJECT_PATH)
result = df[df['value'] > 100]

# Set output identity and description
result.metadata.slug = "filtered-data"
result.metadata.name = "Filtered Data"
result.metadata.description = "Values above threshold"

# Set RDF metadata
result.metadata.rdf_prefixes = {"schema": "https://schema.org/"}
result.metadata.custom_properties = {"schema:about": "Analysis"}

# Annotate columns
result.set_field_metadata("value", description="Measured value", unit="kg")

# Write — slug/name come from metadata
result.to_csv('outputs/filtered.csv', index=False)

Available metadata:

• df.metadata.slug: Dataset slug (used at write time)
• df.metadata.name: Dataset name (used at write time)
• df.metadata.description: Dataset description
• df.metadata.rdf_prefixes: RDF namespace prefixes
• df.metadata.custom_properties: Custom properties (RDF-style)
• df.set_field_metadata(column, *, description, unit, source, type, constraints): Annotate a column

Managing datasets.yaml Programmatically

from sunstone import DatasetsManager, FieldSchema

manager = DatasetsManager('/path/to/project')

# Find datasets
dataset = manager.find_dataset_by_slug('school-data')
dataset = manager.find_dataset_by_location('data/schools.csv')

# Add new output dataset
manager.add_output_dataset(
    name='Analysis Results',
    slug='analysis-results',
    location='outputs/results.csv',
    fields=[
        FieldSchema(name='category', type='string'),
        FieldSchema(name='count', type='integer'),
        FieldSchema(name='avg_value', type='number')
    ],
    publish=True
)

Documentation

• Contributing Guide
• Changelog
• API Reference (below)

API Reference

pandas Module

Drop-in replacement for pandas with lineage tracking:

• read_csv(filepath, project_path, strict=False, **kwargs): Read CSV with lineage
• read_excel(filepath, project_path, strict=False, **kwargs): Read Excel (.xlsx/.xls) with lineage
• read_json(filepath, project_path, strict=False, **kwargs): Read JSON with lineage
• merge(left, right, **kwargs): Merge DataFrames with combined lineage
• concat(dfs, **kwargs): Concatenate DataFrames with combined lineage

DataFrame Class

Main class for working with data:

• read_csv(filepath, project_path, strict=False, **kwargs): Read CSV with lineage tracking
• read_excel(filepath, project_path, strict=False, **kwargs): Read Excel with lineage tracking
• to_csv(path, slug, name, publish=False, **kwargs): Write CSV and register
• merge(right, **kwargs): Merge with another DataFrame
• join(other, **kwargs): Join with another DataFrame
• concat(others, **kwargs): Concatenate DataFrames
• set_field_metadata(column, **kwargs): Annotate column metadata
• .data: Access underlying pandas DataFrame
• .metadata: Access unified metadata container
• .lineage: Access lineage metadata (deprecated — use .metadata.lineage)

DatasetsManager Class

Manage datasets.yaml files:

• find_dataset_by_location(location, dataset_type='input'): Find by file path
• find_dataset_by_slug(slug, dataset_type='input'): Find by slug
• get_all_inputs(): Get all input datasets
• get_all_outputs(): Get all output datasets
• add_output_dataset(...): Register new output
• update_output_dataset(...): Update existing output

Validation Functions

• check_notebook_imports(notebook_path): Validate a single notebook
• validate_project_notebooks(project_path): Validate all notebooks in project

Plugin Protocols

• AuthProvider: Implement authenticate(url, headers, dataset) -> headers to inject auth
• URLHandler: Implement can_handle(url) -> bool and open(url, mode) -> BinaryIO | TextIO
• FormatHandler: Implement can_read(path, format), read(stream, **kwargs), can_write(path, format), write(df, stream, **kwargs)

PluginRegistry Class

Singleton that discovers and manages plugins:

• PluginRegistry.get(): Get the singleton registry instance
• get_auth_providers(): Return all registered auth providers
• get_url_handlers(): Return all registered URL handlers
• get_format_handlers(): Return all registered format handlers
• find_url_handler(url): Find first handler that can handle a URL
• find_format_reader(path, format): Find first handler that can read a file
• find_format_writer(path, format): Find first handler that can write a file
• fetch(url, dest): Convenience — download URL to local file via open()

Exceptions

• SunstoneError: Base exception
• DatasetNotFoundError: Dataset not found in datasets.yaml
• StrictModeError: Operation blocked in strict mode
• DatasetValidationError: Validation failed
• LineageError: Lineage tracking error

Environment Variables

• SUNSTONE_DATAFRAME_STRICT: Set to "1" or "true" to enable strict mode globally
• SUNSTONE_PLUGIN_<NAME>_<KEY>: Override plugin configuration (highest precedence)

Development

See CONTRIBUTING.md for development setup and guidelines.

Running Tests

uv run pytest

Type Checking

uv run mypy

Linting and Formatting

uv run ruff check
uv run ruff format

About Sunstone Institute

Sunstone Institute is a philanthropy-funded organization using data and AI to show the world as it really is, and inspire action everywhere.

License

MIT License - see LICENSE file for details.

Support

• Issues: GitHub Issues

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