barangay 2026.4.13.0

Philippines Standard Geographic Code (PSGC) 2026 Python package, Fuzzy Search, JSON, and YAML.

🇵🇭 barangay

Philippine geographic data with fuzzy search. Based on the April 2026 PSGC masterlist.

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Quick Start

pip install barangay

from barangay import search

# Find barangays with fuzzy matching
results = search("Tongmageng, Tawi-Tawi")
print(results[0]['barangay'])  # Tongmageng

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Features

Feature	Description
🔍 Fuzzy Search	Fast, customizable matching for unstandardized addresses
📅 Historical Data	Access previous PSGC releases by date
👩‍💻 Command Line Interface	Easy-to-use command line interface
📚 Multiple Data Models	Choose the structure that fits your use case
💾 Smart Caching	Automatic caching for faster subsequent loads
📦 Ready-to-Use	JSON, YAML, and Python dictionary formats included

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Installation

pip install barangay

Requirements: Python 3.13+

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CLI

# Search for barangays
barangay search "Tongmageng, Tawi-Tawi"

# Export data
barangay export --model flat --format json --output data.json

# Show information
barangay info version
barangay info stats

# Work with historical data
barangay history list-dates
barangay history search-history "Tongmageng" --as-of "2025-07-08"

# Manage cache
barangay cache info
barangay cache clear

# Batch operations
barangay batch batch-search queries.txt --limit 5 --output results.json
barangay batch validate barangay_names.txt

📖 Full CLI Reference: docs/cli.md

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Python API

Fuzzy Search

from barangay import search

# Simple search
results = search("Tongmageng, Tawi-Tawi")

# Custom search
search(
    "Tongmagen, Tawi-Tawi",
    n=4,                    # Number of results
    match_hooks=["municipality", "barangay"],  # Match levels
    threshold=70.0,         # Minimum similarity (0-100)
)

# Historical data
search("Tongmageng", as_of="2025-07-08")

Data Access

from barangay import barangay, barangay_flat, barangay_extended

# Basic nested model (simple lookups)
ncr_cities = list(barangay["National Capital Region (NCR)"].keys())

# Extended model (recursive with metadata)
for region in barangay_extended.components:
    print(f"{region.name} ({region.type})")

# Flat model (search & filtering)
brgy = [loc for loc in barangay_flat if loc.name == "Marayos"][0]

Utilities

from barangay import sanitize_input, resolve_date, get_available_dates

# Sanitize strings
cleaned = sanitize_input("City of San Jose", exclude=["city of "])
# Result: "san jose"

# Resolve to closest available date
resolved_date, status = resolve_date("2025-07-01", get_available_dates(), "2026-04-13")
# Result: '2025-07-08'

# Get all available dates
dates = get_available_dates()
# ['2026-04-13', '2026-01-13', '2025-08-29', '2025-10-13', '2025-07-08']

📖 Full API Reference: docs/api.md

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Configuration

Configure via environment variables:

export BARANGAY_AS_OF="2025-07-08"      # Default dataset date
export BARANGAY_VERBOSE="true"          # Enable verbose logging
export BARANGAY_CACHE_DIR="/custom/path" # Custom cache directory

Or set programmatically:

import barangay
barangay.as_of = "2025-07-08"

Priority: Function parameter → Module attribute → Environment variable → Default (latest)

📖 Full Configuration Guide: docs/configuration.md

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Data Models

Three data structures are available. Choose based on your use case:

Model	Use Case	Structure
barangay	Simple lookups	Nested dictionary (region → city → barangay)
barangay_extended	Complex hierarchies	Recursive with rich metadata
barangay_flat	Search & filtering	Flat list with parent references

Note: Pydantic models (barangay, barangay_extended, barangay_flat) are recommended. Dict versions (BARANGAY, BARANGAY_EXTENDED, BARANGAY_FLAT) are available for backward compatibility.

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Historical Data

Access previous PSGC releases by date. Data is automatically cached after first download.

Current Data Version: 2026-04-13 (April 13 2026 PSGC masterlist)

Available Dates:

• Current: 2026-04-13 (bundled)
• Historical: 2026-01-13, 2025-07-08, 2025-08-29, 2025-10-13

import barangay
print(barangay.current)           # '2026-04-13'
print(barangay.available_dates)   # ['2026-04-13', '2026-01-13', '2025-08-29', '2025-10-13', '2025-07-08']

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Performance

Fuzzy search is optimized for speed:

Configuration	Performance
Default (3 hooks)	~80ms per search
Optimized (2 hooks)	~25ms per search

Use fewer match_hooks for better performance when appropriate.

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Resources

• 📚 Full Documentation - Comprehensive guides and API reference
• 📩 PSGC Source - April 2026 masterlist
• 📦 PyPI Package
• 💻 GitHub Repository
• 📊 Data Repository - Raw PSGC datasets (JSON, YAML)
• 🐳 Barangay-API Docker - REST API companion

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Contributing

Contributions are welcome! See our Contributing Guide and Code of Conduct.

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License

MIT © bendlikeabamboo