name-variants 0.1.1

Multilingual name romanization lookup tables: Chinese, Japanese, Korean, Arabic, Vietnamese, Indian, Persian, Hebrew, Thai, Greek, Turkish, Russian, Indonesian/Malay

name-variants

"Chan" is simultaneously 陈 and 찬 and จัน — lookup() returns all of them.

1,558 name entries across 18 language tables. 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.

pip install name-variants

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The core idea

A NameCluster is a frozenset of co-equal representations. 陈, 陳, chen, chan, tan, ong 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 "陈" in clusters[0]   # Simplified
assert "陳" in clusters[0]   # Traditional

# 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-other

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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 input

lookup_dialect() — Chinese romanization system tag

from name_variants import lookup_dialect

lookup_dialect("chen")   # "mandarin_pinyin"
lookup_dialect("chan")   # "cantonese"
lookup_dialect("tan")    # "hokkien"
lookup_dialect("chou")   # "wade_giles"
lookup_dialect("hsu")    # "wade_giles"
lookup_dialect("陳")     # "traditional"
lookup_dialect("Smith")  # None

normalize() — text preprocessing

from name_variants import normalize

normalize("  NGUYỄN  ")                    # "nguyễn"
normalize("Nguyễn", strip_diacritics=True) # "nguyen"
normalize("chan​")                          # strips zero-width spaces

---

CLI

nv lookup Chan
# [chinese] (~90M bearers)
#   陈  陳  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 variants

---

Pandas accessor

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]

Install the extra: pip install "name-variants[pandas]"

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Language tables

Language	Entries	Coverage
chinese	140	Pinyin + Wade-Giles + Cantonese + Hokkien + Hakka + Teochew + Traditional
japanese	143	Hepburn + macron variants
korean	100	Revised Romanization + McCune-Reischauer
arabic	92	Multiple transliteration systems
vietnamese	84	Diacritics + stripped forms
russian	79	Multiple transliteration systems
indonesian_malay	77	—
persian	80	—
indian_hindi	80	—
hebrew	75	—
turkish	74	Dotted-İ variants
greek	60	—
thai	68	—
indian_bengali	56	—
indian_tamil	53	—
chinese_given	120	Common given-name characters with Pinyin
korean_given	70	Common given-name syllables
japanese_given	107	Common given-name kanji

from name_variants import ALL_TABLES
list(ALL_TABLES.keys())   # all 18 table names

---

Chinese romanization 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

---

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)

---

Optional extras

pip install "name-variants[pandas]"   # pandas Series .nv accessor

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Why equivalence classes instead of a canonical key?

Early versions returned one "canonical" form per romanization string. This forced a false choice: "Chan" had to map to either 陈 or 찬, not both. Table ordering became load-bearing — whichever table was imported last won. Romanizations had to 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.

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Contributing

git clone https://github.com/SecurityRonin/name-variants
cd name-variants
pip install -e ".[dev]"
pytest

Data 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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