name-variants
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name-variants
"Chan" is simultaneously 陳, 陈 and 찬 and จัน — lookup() returns all of them.
Every romanization system produces a member of an equivalence class: no canonical form, no ordering dependency, no silent data loss. share_cluster("Hsu", "Xu") is True. lookup("Chan") returns a Chinese surname cluster and a Korean given-name cluster, sorted by bearer count.
Available as a Python library, CLI, pandas accessor, and Model Context Protocol (MCP) server.
pip install name-variantsThe core idea
A NameCluster is a frozenset of co-equal representations. 陳, 陈, chen, chan, tan, chern are all members of the same Chinese surname cluster — none is more "real" than another. lookup() returns every cluster that contains your query, sorted by frequency:
from name_variants import lookup, share_cluster
clusters = lookup("Chan")
# [NameCluster(language='chinese', 8 forms),
# NameCluster(language='korean_given', 3 forms)]
# Both Chinese scripts are in the same cluster — co-equal
assert "陳" in clusters[0] # Traditional
assert "陈" in clusters[0] # Simplified
# Membership is case-insensitive
assert "CHAN" in clusters[0]
# Ambiguity is surfaced, not suppressed
assert len(clusters) == 2 # Chinese AND Korean, not one-or-the-otherRelated MCP server: @cyanheads/sanctions-screening-mcp-server
API
lookup() — all matching clusters
from name_variants import lookup
lookup("Chan")
# [NameCluster(language='chinese', 8 forms),
# NameCluster(language='korean_given', 3 forms)]
lookup("Nguyen")
# [NameCluster(language='vietnamese', 4 forms)]
lookup("Smith")
# []Results are sorted by frequency descending — most statistically likely interpretation first.
share_cluster() — equivalence check
from name_variants import share_cluster
share_cluster("Chan", "Chen") # True — same Chinese cluster
share_cluster("Chou", "Zhou") # True — Wade-Giles = Pinyin
share_cluster("Chiang", "Jiang") # True — Chiang Kai-shek / 蒋介石
share_cluster("Hsu", "Xu") # True — Taiwan diaspora romanization
share_cluster("Tsao", "Cao") # True — Ts'ao Ts'ao / 曹操
share_cluster("Chan", "Kim") # False — different names
share_cluster("", "Chan") # False — empty inputdialect() — Chinese romanization system tag
from name_variants import dialect
dialect("chen") # "mandarin_pinyin"
dialect("chan") # "cantonese"
dialect("tan") # "hokkien"
dialect("陳") # "traditional"
dialect("chou") # "wade_giles"
dialect("hsu") # "wade_giles"
dialect("Smith") # Nonenormalize() — text preprocessing
from name_variants import normalize
normalize(" NGUYỄN ") # "nguyễn"
normalize("Nguyễn", strip_diacritics=True) # "nguyen"
normalize("chan") # strips zero-width spacesCLI
nv lookup Chan
# [chinese] (~90M bearers)
# 陳 陈 chan chen tan ...
# [korean_given]
# 찬 chan chahn
nv match Chan Chen # true
nv match Chan Kim # false
nv match --exit-code Chan Chen && echo same # shell-scripting friendly
nv canonicalize-csv names.csv --col name --out out.csv
# adds {name}_canonical column
nv dedupe names.csv --col name --out out.csv
# adds cluster_id column grouping romanization variantsPandas
pip install "name-variants[pandas]"import pandas as pd
import name_variants # registers .nv accessor
s = pd.Series(["Chan", "Chen", "Smith", "Park"])
s.nv.lookup()
# 0 [NameCluster(chinese, ...), NameCluster(korean_given, ...)]
# 1 [NameCluster(chinese, ...)]
# 2 []
# 3 [NameCluster(korean, ...)]
s.nv.cluster_id()
# 0 a3f2b1c4d5e6 ← same as row 1 (Chan and Chen share chinese cluster)
# 1 a3f2b1c4d5e6
# 2 ← empty string for unknown
# 3 9b8c7d6e5f4a
a = pd.Series(["Chan", "Park"])
b = pd.Series(["Chen", "Bak"])
a.nv.share_cluster_with(b) # [True, True]MCP server (Model Context Protocol)
name-variants ships a built-in Model Context Protocol server, exposing name lookup as MCP tools that any MCP-compatible AI client (Claude Desktop, Claude Code, Cursor, etc.) can call directly.
Claude Code:
claude mcp add name-variants -- uvx --from "name-variants[mcp]" nv-mcpClaude Desktop — add to claude_desktop_config.json:
{
"mcpServers": {
"name-variants": {
"command": "uvx",
"args": ["--from", "name-variants[mcp]", "nv-mcp"]
}
}
}Three MCP tools are exposed:
Tool | Arguments | Returns |
|
| list of |
|
|
|
|
| romanization system string or |
Language tables
Language | Entries | Coverage |
| 140 | Pinyin + Wade-Giles + Cantonese + Hokkien + Hakka + Teochew + Traditional |
| 143 | Hepburn + macron variants |
| 100 | Revised Romanization + McCune-Reischauer |
| 92 | Multiple transliteration systems |
| 84 | Diacritics + stripped forms |
| 79 | Multiple transliteration systems |
| 77 | — |
| 80 | — |
| 80 | — |
| 75 | — |
| 74 | Dotted-İ variants |
| 60 | — |
| 68 | — |
| 56 | — |
| 53 | — |
| 120 | Common given-name characters with Pinyin |
| 70 | Common given-name syllables |
| 107 | Common given-name kanji |
from name_variants import ALL_TABLES
list(ALL_TABLES.keys()) # all 18 table namesChinese writing systems
System | Examples |
Mandarin Pinyin | Zhou, Zhang, Wang, Xu |
Wade-Giles | Chou, Chang, Wang, Hsu, Tsao, Kuo, Hsieh |
Cantonese (Jyutping/Yale) | Chan, Wong, Ng, Lam, Tsui |
Hokkien/Min Nan | Tan, Ng, Lim, Goh |
Hakka | Fong, Thong |
Teochew | Teo, Ng |
Postal romanization | Peking, Nanking, Chungking |
Traditional characters | 陳, 劉, 張, 楊, 趙 |
Simplified characters | 陈, 刘, 张, 杨, 赵 |
NameCluster reference
@dataclass(frozen=True)
class NameCluster:
forms: frozenset[str] # all representations — co-equal
language: str # "chinese", "korean", "vietnamese", etc.
frequency: int | None # approximate global bearer count
def __contains__(self, text: str) -> bool # case-insensitive
def __iter__(self) # iterate all forms
def __len__(self)Why equivalence classes instead of a canonical key?
A canonical-key model forces a false choice: "Chan" must map to either 陳, 陈 or 찬, not both. Table ordering becomes load-bearing — whichever table is consulted last wins. Romanizations must be stripped from given-name tables to prevent collisions.
The NameCluster model eliminates this: every romanization system's output is just another member of a frozenset. lookup() returns all matching clusters. Ambiguity is surfaced, not suppressed. The most likely interpretation comes first by frequency.
Contributing
git clone https://github.com/SecurityRonin/name-variants
cd name-variants
pip install -e ".[dev]"
pytestData files are in name_variants/*_names.py and name_variants/*_surnames.py. Each entry is a plain Python dict — easy to read and edit:
"陈": {
"forms": ["陳", "chen", "chan", "tan", ...],
"frequency": 90_000_000,
"dialects": {
"chen": "mandarin_pinyin",
"chan": "cantonese",
"tan": "hokkien",
"陳": "traditional",
},
},Adding a new variant is one edit to one entry — forms, frequency, and dialect tag colocated.
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