RyanData Address Utils

Parse and validate US addresses with Pydantic models, ZIP/state validation, pandas integration, and semantic-release powered CI.

Highlights

• Structured parsing of US addresses into 26+ components with Pydantic models
• ZIP and state validation backed by authoritative datasets
• Pandas-friendly parsing for batch workloads
• Custom errors (RyanDataAddressError, RyanDataValidationError) with package context
• Builder API for programmatic address construction
• ProcessLog system for transformation auditing (via abstract-validation-base)
• Semantic-release CI for automated tagging and releases

Install

uv (recommended)

uv add git+https://github.com/Abstract-Data/RyanData-Address-Utils.git
# with pandas extras
uv add "ryandata-address-utils[pandas] @ git+https://github.com/Abstract-Data/RyanData-Address-Utils.git"

pip

pip install git+https://github.com/Abstract-Data/RyanData-Address-Utils.git
pip install "ryandata-address-utils[pandas] @ git+https://github.com/Abstract-Data/RyanData-Address-Utils.git"

Setup cheat sheet (pick what you need)

• Local parsing only: install base package (no extras) and call parse(...) or AddressService.
• Pandas workflows: add the [pandas] extra so parse_dataframe works without optional import errors.
• Libpostal setup (local, no Docker): run uv run ryandata-address-utils-setup and follow the prompts. The default data directory is system-wide (e.g., /usr/local/share/libpostal or C:\\libpostal); override it if you prefer and set LIBPOSTAL_DATA_DIR accordingly.
• The setup command detects your OS, attempts installation via Homebrew/apt/dnf/yum where available, and downloads the official libpostal data archives into the chosen directory.

International parsing (libpostal)

• parse_auto (service) tries US first, then libpostal if US validation fails.
• Strict rules: international results must include a road plus at least one location element (city/state/postal/country) or parsing fails.
• Returned structure includes InternationalAddress fields (HouseNumber, Road, City, State, PostalCode, Country, CountryCode) and raw libpostal Components.
• Requires libpostal installed; use the setup helper (uv run ryandata-address-utils-setup) to install locally and download data.
• Heuristics: if the input clearly names a non-US country or contains non-ASCII, it skips US parsing and goes straight to libpostal; otherwise, US is attempted first and any US validation failure triggers libpostal fallback.

Quick start

from ryandata_address_utils import AddressService, parse

result = parse("123 Main St, Austin TX 78749")
if result.is_valid:
    print(result.address.ZipCode)   # "78749"
    print(result.to_dict())         # full address dict
else:
    print(result.validation.errors) # custom errors with context

service = AddressService()
service.parse("456 Oak Ave, Dallas TX 75201")

Pandas integration

import pandas as pd
from ryandata_address_utils import AddressService

df = pd.DataFrame({"address": ["123 Main St, Austin TX 78749", "456 Oak Ave, Dallas TX 75201"]})
service = AddressService()

parsed = service.parse_dataframe(df, "address", prefix="addr_")
print(parsed[["addr_AddressNumber", "addr_StreetName", "addr_ZipCode"]])

Programmatic build

from ryandata_address_utils import AddressBuilder

address = (
    AddressBuilder()
    .with_street_number("123")
    .with_street_name("Main")
    .with_street_type("St")
    .with_city("Austin")
    .with_state("TX")
    .with_zip("78749")
    .build()
)

Transformation tracking

Track what normalizations and cleanings were applied during parsing:

from ryandata_address_utils import AddressService

service = AddressService()
result = service.parse("123 main st, austin texas 78749")

# Aggregate logs from parsing and model transformations
for entry in result.aggregate_logs():
    print(f"{entry['field']}: {entry['message']}")
# Example output:
# StateName: Normalized state name from full name to abbreviation
# ZipCode: ZIP code parsed and validated

Workflow at a glance

flowchart LR
    parseStep[Parse] --> validateStep[Validate ZIP/State]
    validateStep --> testsStep[Tests & Lint]
    testsStep --> releaseStep[uv version bump + tag]
    releaseStep --> githubRelease[GitHub Release + Artifacts]

Loading

APIs you get

• AddressService: parse single, batch, DataFrame; look up ZIP/state; validate
• parse(...): convenience wrapper returning ParseResult
• ZIP utilities: get_city_state_from_zip, get_zip_info, is_valid_zip, is_valid_state, normalize_state
• Builder: AddressBuilder for programmatic address construction
• Audit trail: ProcessLog, ProcessEntry for tracking transformations
• Validation base: ValidationBase, RyanDataValidationBase for model mixins

Documentation

• Architecture Overview - Package structure, data flow diagrams, design patterns, and SOLID/DRY principles applied
• Diagrams - Visual references for the codebase
• Changelog - Version history and release notes
• AI Agent Guide - Guidance for AI coding assistants

Development (uv)

git clone https://github.com/Abstract-Data/RyanData-Address-Utils.git
cd RyanData-Address-Utils
uv sync
uv run pytest
uv run ruff check src/
uv run mypy src/
uv run ruff format src/

Agent Workflow (Cursor)

This project uses a structured parallel agent workflow for AI-assisted development. See .cursor/agents.md for:

• STEP/AGENT naming convention for organizing parallel tasks
• Specialized agents: CodeAgent, TestAgent, DocsAgent, RefactorAgent, ConfigAgent
• Execution rules for coordinating multi-agent work
• Task templates for features, bug fixes, and refactoring

Example todo format:

STEP1 - CodeAgent: Implement core data models
STEP1 - TestAgent: Create test scaffolding
STEP2 - CodeAgent: Wire up service layer (runs after STEP1)

Contributing and support

• Issues: https://github.com/Abstract-Data/RyanData-Address-Utils/issues
• Releases/notes: https://github.com/Abstract-Data/RyanData-Address-Utils/releases
• Architecture: docs/ARCHITECTURE.md
• License: MIT