# DQinYuan/chinese_province_city_area_mapper

一个用于提取简体中文字符串中省，市和区并能够进行映射，检验和简单绘图的python模块

Repository: https://github.com/DQinYuan/chinese_province_city_area_mapper
Canonical: https://ross.abutalabs.com/products/chinese_province_city_area_mapper
Language: Python
License: MIT
License Family: permissive
Last push: 2024-03-19T00:14:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3104, "days_push": 898, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1796, forks 402 (observed 2026-08-28T04:05:37.526841+00:00)

## What it is
A Python library (pip package 'cpca') that extracts province, city, and district names from simplified Chinese address strings and maps them to their full administrative hierarchy. It outputs results as a pandas DataFrame with adcodes and supports simple mapping/plotting.

## Use cases
- extract province city and district from chinese addresses
- parse chinese address strings into structured fields
- normalize chinese addresses to administrative divisions
- map a chinese district to its city and province
- clean messy shipping addresses in python
- get adcode for chinese location names

## When to choose
- you need to parse simplified Chinese addresses into province/city/district columns
- you want a lightweight pandas-based address extraction tool
- you need administrative division codes (adcode) for Chinese locations

## When to avoid
- you need full geocoding to latitude/longitude coordinates
- you need to parse traditional Chinese or non-Chinese addresses
- you need high-throughput production NLP with deep learning models

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, parser, data-science
- domain: data-science
- platform: python, cross-platform
- tags: chinese-address-parsing, administrative-divisions, pandas, geocoding, cpca, natural-language-processing, china

## Member repositories
- DQinYuan/chinese_province_city_area_mapper (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:37.526841+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:22:47.662317+00:00, confidence not recorded.
  - readme: https://github.com/DQinYuan/chinese_province_city_area_mapper (fetched 2026-08-28T04:05:37.526841+00:00, sha 48b5dace43fc)
  - registry_pypi: https://pypi.org/pypi/chinese_province_city_area_mapper/json (fetched 2026-08-29T11:01:27.314550+00:00, sha 6c7f817adb24)
- Data as of 2026-08-30T08:39:29.467469+00:00.