---
id: pyap
version: "0.3.1"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# pyap — Pyap is an MIT Licensed text processing library, written in Python, for detecting and parsing addresses. Currently it supports USA, Canadian and British addresses.

License: permissive · Maintenance: dormant · Downloads: 460.8K/mo

## What it is and what it does

Pyap is a lightweight text-processing library that finds and extracts postal addresses from unstructured text using regular expressions. It targets US, Canadian, and British address formats, breaking them into structured components (street, city, state, postal code) that you can access programmatically.

The library trades accuracy for speed: it uses pattern matching rather than gazetteers or machine learning, so it can process large volumes of text quickly in real time. This makes it useful for initial address detection in web scraping or bulk text processing, though the description notes that false positives can occur when text happens to match the address pattern structure. The project is dormant but has no runtime dependencies, making it lightweight to deploy.

Use it for:

- Extract addresses from web-scraped content or document text during initial data collection before validation
- Batch-process large volumes of unstructured text to identify and isolate address-like patterns quickly
- Pre-filter text for geocoding services to reduce the volume of strings sent to paid APIs
- Parse addresses embedded in email bodies, PDFs, or other documents for contact database population
- Detect mailing addresses in user-submitted forms or comments for location-based features

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Pyap detects and parses postal addresses from unstructured text, extracting structured address components for US, Canadian, and British addresses using pattern matching.

Yes, if you need fast regex-based address detection for US, Canadian, or British addresses in bulk text processing and can tolerate occasional false positives. No, if you require high accuracy, active maintenance, or support for additional countries—consider a geocoding service or maintained alternative instead. The lack of dependencies and dormant status make it a low-risk, lightweight choice for specific use cases.

## Install

pip install pyap
uv add pyap
poetry add pyap

## Installing pyap

Before you install: Installation is straightforward with no runtime dependencies. The project is dormant (last commit 2023-12-13, latest release 2020-09-30), so expect no active maintenance or bug fixes, though the codebase remains available and archived.

License in practice: MIT license permits commercial and private use with minimal restrictions; you must include the license notice in distributions.

Quickstart:

pip install pyap

import pyap
text = "225 E. John Carpenter Freeway, Suite 1500 Irving, Texas 75062"
addresses = pyap.parse(text, country='US')
for addr in addresses:
    print(addr.as_dict())

Verify before relying:

- Whether false-positive rate remains acceptable for production use cases beyond the 'quite rare' claim in the description
- Current compatibility with modern Python versions beyond the declared support range
- Whether regex patterns have been updated to handle modern address formats or postal code standards

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 460.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

address extraction from text, postal address parser, address detection regex, parse US Canadian British addresses, extract addresses from documents, address recognition library, text address parsing, address-extraction, regex-based, web-scraping

[View on SkillFed](https://skillfed.io/packages/pyap) · [View on PyPI](https://pypi.org/project/pyap/)