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2026-05-18 14:15:59 +07:00
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.venv/lib/python3.12/site-packages/faker/providers/person/zh_TW/__init__.py Normal file
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from collections import OrderedDict
from .. import Provider as PersonProvider
class Provider(PersonProvider):
# update: 2025 04 30
# source:
# 中華民國(ROC)人口 2025 3月: 23,374,742
# (As of March 2025, the total population of the Republic of China (Taiwan) is 23,374,742.)
# https://www.ris.gov.tw/app/portal/346
# 臺灣原住民人口 2024 12月 612,000
# (As of December 2024, the indigenous population in Taiwan is approximately 612,000, accounting for 2.7% of the
# total population.)
# https://www.moi.gov.tw/News_Content.aspx?n=2905&sms=10305&s=325345
# Although most Taiwanese people are ethnically Han, their culture has diverged significantly from mainland China
# over centuries.
# Taiwan’s Han communities—like Hoklo and Hakka—have developed unique languages, customs, and identities distinct
# from Chinese people today.
# *Taiwanese Indigenous peoples traditionally have their own naming systems*,
# which are different from Han Chinese names—they often reflect tribal identity, family lineage, or personal traits.
formats_female = OrderedDict(
(
("{{last_name}}{{first_name_female}}", 1), # 漢人 Han
# ("{{first_indigenous_name_female}} {{last_indigenous_name}}", 0.027), # 原住民 Taiwanese Indigenous Peoples
)
)
formats_male = OrderedDict(
(
("{{last_name}}{{first_name_male}}", 1), # 漢人 Han
# ("{{first_indigenous_name_male}} {{last_indigenous_name}}", 0.027), # 原住民 Taiwanese Indigenous Peoples
)
)
formats = formats_male.copy()
formats.update(formats_female)
# =============================================================================
# source:
# 中華民國(ROC)全國姓名統計 2023/6月
# (National Name Statistics of the Republic of China (Taiwan), June 2023)
# https://www.ris.gov.tw/documents/data/5/2/112namestat.pdf
# page 267: TOP 100 female first name
# page 281: The top 10 most common female names by year of birth
first_names_female = OrderedDict(
(
# top 100 names in all ages
("淑芬", 0.14),
("淑惠", 0.13),
("美玲", 0.12),
("麗華", 0.11),
("美惠", 0.11),
("淑貞", 0.1),
("雅婷", 0.1),
("秀英", 0.1),
("淑娟", 0.1),
("秀琴", 0.1),
("秀美", 0.09),
("美華", 0.09),
("怡君", 0.09),
("淑華", 0.09),
("美玉", 0.09),
("雅惠", 0.08),
("秀蘭", 0.08),
("淑美", 0.08),
("秀鳳", 0.08),
("美珠", 0.07),
("麗珠", 0.07),
("麗娟", 0.07),
("淑玲", 0.07),
("美雲", 0.07),
("雅雯", 0.07),
("雅玲", 0.07),
("美麗", 0.06),
("玉蘭", 0.06),
("月娥", 0.06),
("麗卿", 0.06),
("惠美", 0.06),
("麗美", 0.06),
("秀珠", 0.06),
("淑珍", 0.05),
("欣怡", 0.05),
("素貞", 0.05),
("秀珍", 0.05),
("素珍", 0.05),
("惠玲", 0.05),
("玉梅", 0.05),
("玉英", 0.05),
("淑慧", 0.05),
("秀玲", 0.05),
("明珠", 0.05),
("秋香", 0.05),
("秀玉", 0.05),
("麗雲", 0.05),
("秀梅", 0.05),
("麗玉", 0.05),
("寶珠", 0.05),
("怡婷", 0.05),
("麗玲", 0.05),
("宜蓁", 0.04),
("月英", 0.04),
("淑芳", 0.04),
("玉玲", 0.04),
("秀雲", 0.04),
("慧玲", 0.04),
("春美", 0.04),
("碧霞", 0.04),
("麗香", 0.04),
("美鳳", 0.04),
("美珍", 0.04),
("美英", 0.04),
("碧珠", 0.04),
("碧雲", 0.04),
("佳蓉", 0.04),
("美蘭", 0.04),
("秀娟", 0.04),
("美娟", 0.04),
("淑敏", 0.04),
("玉珍", 0.04),
("淑卿", 0.04),
("美慧", 0.04),
("靜宜", 0.04),
("素珠", 0.04),
("雅慧", 0.04),
("靜怡", 0.04),
("玉美", 0.04),
("雅萍", 0.04),
("素卿", 0.04),
("素琴", 0.04),
("秀枝", 0.04),
("金蓮", 0.04),
("秋月", 0.04),
("麗雪", 0.04),
("惠珍", 0.04),
("心怡", 0.04),
("佳玲", 0.04),
("鈺婷", 0.04),
("詩涵", 0.04),
("秀霞", 0.04),
("秀華", 0.03),
("麗琴", 0.03),
("金鳳", 0.03),
("麗珍", 0.03),
("玉鳳", 0.03),
("玉琴", 0.03),
("秀蓮", 0.03),
("素蘭", 0.03),
# top n names in younger generation
("婉婷", 0.01),
("佩珊", 0.01),
("怡萱", 0.01),
("雅筑", 0.01),
("郁婷", 0.01),
("宜庭", 0.01),
("欣妤", 0.01),
("思妤", 0.01),
("佳穎", 0.01),
("品妤", 0.01),
("子涵", 0.01),
("品妍", 0.01),
("子晴", 0.01),
("詠晴", 0.01),
("禹彤", 0.01),
("羽彤", 0.01),
("芯語", 0.01),
("宥蓁", 0.01),
("語彤", 0.01),
("苡晴", 0.01),
("苡菲", 0.01),
("雨霏", 0.01),
("芸菲", 0.01),
("苡安", 0.01),
("玥彤", 0.01),
)
)
# source:
# 中華民國(ROC)全國姓名統計 2023/6月
# (National Name Statistics of the Republic of China (Taiwan), June 2023)
# https://www.ris.gov.tw/documents/data/5/2/112namestat.pdf
# page 266: TOP 100 male first name
# page 280: The top 10 most common male names by year of birth
first_names_male = OrderedDict(
(
# top 100 names in all ages
("家豪", 0.06),
("志明", 0.05),
("建宏", 0.05),
("俊傑", 0.05),
("俊宏", 0.05),
("志豪", 0.05),
("志偉", 0.05),
("承翰", 0.04),
("冠宇", 0.04),
("志強", 0.04),
("宗翰", 0.04),
("志宏", 0.04),
("冠廷", 0.04),
("志成", 0.04),
("文雄", 0.04),
("承恩", 0.04),
("金龍", 0.04),
("文彬", 0.03),
("正雄", 0.03),
("明輝", 0.03),
("柏翰", 0.03),
("彥廷", 0.03),
("明德", 0.03),
("文龍", 0.03),
("俊賢", 0.03),
("志忠", 0.03),
("國華", 0.03),
("信宏", 0.03),
("家銘", 0.03),
("俊雄", 0.03),
("宇翔", 0.03),
("建成", 0.03),
("冠霖", 0.03),
("志銘", 0.02),
("志雄", 0.02),
("進財", 0.02),
("明哲", 0.02),
("榮華", 0.02),
("柏宇", 0.02),
("志鴻", 0.02),
("志賢", 0.02),
("俊良", 0.02),
("建華", 0.02),
("家瑋", 0.02),
("家榮", 0.02),
("文祥", 0.02),
("建志", 0.02),
("文正", 0.02),
("文忠", 0.02),
("凱翔", 0.02),
("家宏", 0.02),
("國雄", 0.02),
("明宏", 0.02),
("文賢", 0.02),
("世昌", 0.02),
("哲瑋", 0.02),
("文傑", 0.02),
("正義", 0.02),
("武雄", 0.02),
("建興", 0.02),
("志文", 0.02),
("嘉宏", 0.02),
("文章", 0.02),
("明宗", 0.02),
("宇軒", 0.02),
("進興", 0.02),
("俊豪", 0.02),
("俊廷", 0.02),
("冠宏", 0.02),
("仁傑", 0.02),
("威廷", 0.02),
("哲維", 0.02),
("宗霖", 0.02),
("文欽", 0.02),
("博文", 0.02),
("俊男", 0.02),
("宗憲", 0.02),
("子豪", 0.02),
("俊宇", 0.02),
("勝雄", 0.02),
("柏諺", 0.02),
("建良", 0.02),
("俊明", 0.02),
("俊銘", 0.02),
("世明", 0.02),
("義雄", 0.02),
("建銘", 0.02),
("永昌", 0.02),
("文華", 0.02),
("子翔", 0.02),
("柏宏", 0.02),
("政宏", 0.02),
("進發", 0.02),
("柏霖", 0.02),
("建中", 0.02),
("國榮", 0.02),
("志誠", 0.02),
("聰明", 0.02),
("俊佑", 0.02),
("志遠", 0.02),
# top n names in younger generation
("宥廷", 0.01),
("品睿", 0.01),
("宸睿", 0.01),
("宇恩", 0.01),
("宥辰", 0.01),
("柏睿", 0.01),
("睿恩", 0.01),
("恩碩", 0.01),
("子睿", 0.01),
("子宸", 0.01),
("子恩", 0.01),
)
)
# source:
# 中華民國(ROC)全國姓名統計 2023/6月
# (National Name Statistics of the Republic of China (Taiwan), June 2023)
# https://www.ris.gov.tw/documents/data/5/2/112namestat.pdf
# page 282, 283, 284: TOP 200 last name
last_names = OrderedDict(
(
("陳", 11.2),
("林", 8.33),
("黃", 6),
("張", 5.3),
("李", 5.13),
("王", 4.09),
("吳", 4),
("劉", 3.16),
("蔡", 2.93),
("楊", 2.64),
("許", 2.31),
("鄭", 1.89),
("謝", 1.77),
("洪", 1.51),
("郭", 1.5),
("邱", 1.47),
("曾", 1.45),
("廖", 1.35),
("賴", 1.33),
("徐", 1.26),
("周", 1.21),
("葉", 1.18),
("蘇", 1.14),
("莊", 0.95),
("江", 0.92),
("呂", 0.91),
("何", 0.85),
("蕭", 0.83),
("羅", 0.83),
("高", 0.77),
("潘", 0.69),
("簡", 0.68),
("朱", 0.66),
("鍾", 0.65),
("游", 0.59),
("彭", 0.59),
("詹", 0.58),
("施", 0.54),
("胡", 0.54),
("沈", 0.51),
("余", 0.51),
("盧", 0.48),
("梁", 0.46),
("趙", 0.44),
("顏", 0.44),
("柯", 0.44),
("翁", 0.4),
("魏", 0.38),
("孫", 0.36),
("戴", 0.35),
("范", 0.34),
("方", 0.33),
("宋", 0.32),
("鄧", 0.27),
("杜", 0.23),
("侯", 0.23),
("傅", 0.22),
("曹", 0.22),
("薛", 0.21),
("阮", 0.21),
("丁", 0.21),
("卓", 0.19),
("馬", 0.18),
("温", 0.18),
("董", 0.18),
("藍", 0.18),
("古", 0.18),
("石", 0.18),
("紀", 0.17),
("唐", 0.17),
("蔣", 0.17),
("姚", 0.17),
("連", 0.17),
("歐", 0.16),
("馮", 0.16),
("程", 0.16),
("湯", 0.15),
("田", 0.15),
("康", 0.15),
("黄", 0.15),
("姜", 0.15),
("白", 0.14),
("汪", 0.14),
("尤", 0.14),
("鄒", 0.14),
("黎", 0.13),
("巫", 0.12),
("鐘", 0.12),
("涂", 0.12),
("龔", 0.11),
("嚴", 0.09),
("韓", 0.09),
("袁", 0.09),
("金", 0.08),
("童", 0.08),
("陸", 0.07),
("柳", 0.07),
("凃", 0.07),
("夏", 0.07),
("邵", 0.07),
("錢", 0.06),
("伍", 0.06),
("倪", 0.06),
("溫", 0.06),
("駱", 0.05),
("譚", 0.05),
("于", 0.05),
("甘", 0.05),
("熊", 0.05),
("任", 0.05),
("秦", 0.05),
("章", 0.05),
("毛", 0.05),
("官", 0.05),
("顧", 0.05),
("史", 0.05),
("萬", 0.05),
("俞", 0.05),
("粘", 0.04),
("雷", 0.04),
("饒", 0.04),
("張簡", 0.04),
("闕", 0.04),
("凌", 0.04),
("武", 0.03),
("孔", 0.03),
("尹", 0.03),
("崔", 0.03),
("辛", 0.03),
("歐陽", 0.03),
("辜", 0.03),
("陶", 0.03),
("段", 0.03),
("易", 0.03),
("龍", 0.03),
("韋", 0.03),
("池", 0.03),
("葛", 0.03),
("褚", 0.03),
("孟", 0.02),
("麥", 0.02),
("殷", 0.02),
("莫", 0.02),
("文", 0.02),
("賀", 0.02),
("賈", 0.02),
("管", 0.02),
("關", 0.02),
("包", 0.02),
("向", 0.02),
("丘", 0.02),
("范姜", 0.02),
("梅", 0.02),
("華", 0.02),
("裴", 0.02),
("利", 0.02),
("全", 0.02),
("樊", 0.02),
("房", 0.02),
("佘", 0.02),
("花", 0.01),
("安", 0.01),
("左", 0.01),
("魯", 0.01),
("塗", 0.01),
("穆", 0.01),
("鮑", 0.01),
("蒲", 0.01),
("郝", 0.01),
("谷", 0.01),
("成", 0.01),
("邢", 0.01),
("練", 0.01),
("閻", 0.01),
("鄔", 0.01),
("陽", 0.01),
("盛", 0.01),
("常", 0.01),
("符", 0.01),
("耿", 0.01),
("解", 0.01),
("繆", 0.01),
("申", 0.01),
("聶", 0.01),
("祝", 0.01),
("岳", 0.01),
("曲", 0.01),
("籃", 0.01),
("齊", 0.01),
("應", 0.01),
("舒", 0.01),
("單", 0.01),
("喬", 0.01),
("畢", 0.01),
("留", 0.01),
("鄞", 0.01),
("翟", 0.01),
("牛", 0.01),
("龎", 0.01),
("覃", 0.01),
)
)
first_names = first_names_male.copy()
first_names.update(first_names_female)
# =============================================================================
# From https://en.wikipedia.org/wiki/Chinese_given_name#Common_Chinese_names
# The above information is slightly incorrect.
# 使用 pypinyin 進行姓名翻譯：https://github.com/mozillazg/python-pinyin
# Using pypinyin for name translation: https://github.com/mozillazg/python-pinyin
# print(lazy_pinyin("許", style=Style.WADEGILES, v_to_u=True)[0].replace("'","").upper().replace("Ü","U"))
# 轉換過程有部分姓氏拼音剛好是重複的或是複姓的
# 因為重建過程字典的特性無法重複所以就被忽略了 目前懶得修ouo
# Some surnames result in duplicate transliterations during the conversion process.
# Due to the nature of dictionaries (no duplicate keys), duplicates are ignored during reconstruction.
# 使用威妥瑪拼音，而不是漢語拼音
# Using Wade–Giles romanization instead of Hanyu Pinyin
last_romanized_names = OrderedDict(
(
("CHEN", 11.2),
("LIN", 8.33),
("HUANG", 0.15),
("CHANG", 0.01),
("LI", 0.02),
("WANG", 0.14),
("WU", 0.01),
("LIU", 0.01),
("TSAI", 2.93),
("YANG", 0.01),
("HSU", 1.26),
("CHENG", 0.01),
("HSIEH", 1.77),
("HUNG", 1.51),
("KUO", 1.5),
("CHIU", 0.02),
("TSENG", 1.45),
("LIAO", 1.35),
("LEI", 0.04),
("CHOU", 1.21),
("YEH", 1.18),
("SU", 1.14),
("CHUANG", 0.95),
("CHIANG", 0.15),
("LU", 0.01),
("HO", 0.02),
("HSIAO", 0.83),
("LO", 0.05),
("KAO", 0.77),
("PAN", 0.69),
("CHIEN", 0.06),
("CHU", 0.01),
("CHUNG", 0.12),
("YU", 0.05),
("PENG", 0.59),
("CHAN", 0.04),
("SHIH", 0.05),
("HU", 0.54),
("SHEN", 0.01),
("LIANG", 0.46),
("CHAO", 0.44),
("YEN", 0.01),
("KO", 0.03),
("WENG", 0.4),
("WEI", 0.03),
("SUN", 0.36),
("TAI", 0.35),
("FAN", 0.02),
("FANG", 0.02),
("SUNG", 0.32),
("TENG", 0.27),
("TU", 0.01),
("HOU", 0.23),
("FU", 0.01),
("TSAO", 0.22),
("HSUEH", 0.21),
("JUAN", 0.21),
("TING", 0.21),
("CHO", 0.19),
("MA", 0.18),
("WEN", 0.02),
("TUNG", 0.08),
("LAN", 0.01),
("KU", 0.01),
("CHI", 0.01),
("TANG", 0.15),
("YAO", 0.17),
("LIEN", 0.01),
("OU", 0.03),
("FENG", 0.16),
("TIEN", 0.15),
("KANG", 0.15),
("PAI", 0.14),
("TSOU", 0.14),
("KUNG", 0.03),
("HAN", 0.09),
("YUAN", 0.09),
("CHIN", 0.05),
("HSIA", 0.07),
("SHAO", 0.07),
("NI", 0.06),
("TAN", 0.01),
("KAN", 0.05),
("HSIUNG", 0.05),
("JEN", 0.05),
("MAO", 0.05),
("KUAN", 0.02),
("WAN", 0.05),
("JAO", 0.04),
("CHUEH", 0.04),
("LING", 0.04),
("YIN", 0.01),
("TSUI", 0.03),
("HSIN", 0.03),
("TAO", 0.03),
("TUAN", 0.03),
("I", 0.03),
("LUNG", 0.03),
("CHIH", 0.03),
("MENG", 0.02),
("MEI", 0.02),
("MO", 0.02),
("CHIA", 0.02),
("PAO", 0.01),
("HSIANG", 0.02),
("HUA", 0.01),
("PEI", 0.02),
("CHUAN", 0.02),
("SHE", 0.02),
("AN", 0.01),
("TSO", 0.01),
("MU", 0.01),
("PU", 0.01),
("HAO", 0.01),
("HSING", 0.01),
("SHENG", 0.01),
("KENG", 0.01),
("CHIEH", 0.01),
("MOU", 0.01),
("NIEH", 0.01),
("YUEH", 0.01),
("YING", 0.01),
("SHU", 0.01),
("CHIAO", 0.01),
("PI", 0.01),
("TI", 0.01),
("NIU", 0.01),
("PANG", 0.01),
)
)
first_romanized_names_male = OrderedDict(
(
("CHIA-HAO", 0.06),
("CHIH-MING", 0.02),
("CHIEN-HUNG", 0.05),
("CHUN-CHIEH", 0.05),
("CHUN-HUNG", 0.05),
("CHIH-HAO", 0.05),
("CHIH-WEI", 0.05),
("CHENG-HAN", 0.04),
("KUAN-YU", 0.04),
("CHIH-CHIANG", 0.04),
("TSUNG-HAN", 0.04),
("CHIH-HUNG", 0.02),
("KUAN-TING", 0.04),
("CHIH-CHENG", 0.02),
("WEN-HSIUNG", 0.04),
("CHENG-EN", 0.04),
("CHIN-LUNG", 0.04),
("WEN-PIN", 0.03),
("CHENG-HSIUNG", 0.03),
("MING-HUI", 0.03),
("PAI-HAN", 0.03),
("YEN-TING", 0.03),
("MING-TE", 0.03),
("WEN-LUNG", 0.03),
("CHUN-HSIEN", 0.03),
("CHIH-CHUNG", 0.03),
("KUO-HUA", 0.03),
("HSIN-HUNG", 0.03),
("CHIA-MING", 0.03),
("CHUN-HSIUNG", 0.03),
("YU-HSIANG", 0.03),
("CHIEN-CHENG", 0.03),
("KUAN-LIN", 0.03),
("CHIH-HSIUNG", 0.02),
("CHIN-TSAI", 0.02),
("MING-CHE", 0.02),
("JUNG-HUA", 0.02),
("PAI-YU", 0.02),
("CHIH-HSIEN", 0.02),
("CHUN-LIANG", 0.02),
("CHIEN-HUA", 0.02),
("CHIA-WEI", 0.02),
("CHIA-JUNG", 0.02),
("WEN-HSIANG", 0.02),
("CHIEN-CHIH", 0.02),
("WEN-CHENG", 0.02),
("WEN-CHUNG", 0.02),
("KAI-HSIANG", 0.02),
("CHIA-HUNG", 0.02),
("KUO-HSIUNG", 0.02),
("MING-HUNG", 0.02),
("WEN-HSIEN", 0.02),
("SHIH-CHANG", 0.02),
("CHE-WEI", 0.02),
("WEN-CHIEH", 0.02),
("CHENG-I", 0.02),
("WU-HSIUNG", 0.02),
("CHIEN-HSING", 0.02),
("CHIH-WEN", 0.02),
("WEN-CHANG", 0.02),
("MING-TSUNG", 0.02),
("YU-HSUAN", 0.02),
("CHIN-HSING", 0.02),
("CHUN-HAO", 0.02),
("CHUN-TING", 0.02),
("KUAN-HUNG", 0.02),
("JEN-CHIEH", 0.02),
("WEI-TING", 0.02),
("TSUNG-LIN", 0.02),
("WEN-CHIN", 0.02),
("PO-WEN", 0.02),
("CHUN-NAN", 0.02),
("TSUNG-HSIEN", 0.02),
("TZU-HAO", 0.02),
("CHUN-YU", 0.02),
("SHENG-HSIUNG", 0.02),
("PAI-YEN", 0.02),
("CHIEN-LIANG", 0.02),
("CHUN-MING", 0.02),
("SHIH-MING", 0.02),
("I-HSIUNG", 0.02),
("CHIEN-MING", 0.02),
("YUNG-CHANG", 0.02),
("WEN-HUA", 0.02),
("TZU-HSIANG", 0.02),
("PAI-HUNG", 0.02),
("CHENG-HUNG", 0.02),
("CHIN-FA", 0.02),
("PAI-LIN", 0.02),
("CHIEN-CHUNG", 0.02),
("KUO-JUNG", 0.02),
("TSUNG-MING", 0.02),
("CHIH-YUAN", 0.02),
("YU-TING", 0.01),
("PIN-JUI", 0.01),
("CHEN-JUI", 0.01),
("YU-EN", 0.01),
("YU-CHEN", 0.01),
("PAI-JUI", 0.01),
("JUI-EN", 0.01),
("EN-SHO", 0.01),
("TZU-JUI", 0.01),
("TZU-CHEN", 0.01),
("TZU-EN", 0.01),
)
)
first_romanized_names_female = OrderedDict(
(
("SHU-FEN", 0.14),
("SHU-HUI", 0.05),
("MEI-LING", 0.12),
("LI-HUA", 0.11),
("MEI-HUI", 0.04),
("SHU-CHEN", 0.05),
("YA-TING", 0.1),
("HSIU-YING", 0.1),
("SHU-CHUAN", 0.1),
("HSIU-CHIN", 0.1),
("HSIU-MEI", 0.05),
("MEI-HUA", 0.09),
("I-CHUN", 0.09),
("SHU-HUA", 0.09),
("MEI-YU", 0.09),
("YA-HUI", 0.04),
("HSIU-LAN", 0.08),
("SHU-MEI", 0.08),
("HSIU-FENG", 0.08),
("MEI-CHU", 0.07),
("LI-CHU", 0.07),
("LI-CHUAN", 0.07),
("SHU-LING", 0.07),
("MEI-YUN", 0.07),
("YA-WEN", 0.07),
("YA-LING", 0.07),
("MEI-LI", 0.06),
("YU-LAN", 0.06),
("YUEH-O", 0.06),
("LI-CHING", 0.06),
("HUI-MEI", 0.06),
("LI-MEI", 0.06),
("HSIU-CHU", 0.06),
("HSIN-I", 0.04),
("SU-CHEN", 0.05),
("HSIU-CHEN", 0.05),
("HUI-LING", 0.04),
("YU-MEI", 0.04),
("YU-YING", 0.05),
("HSIU-LING", 0.05),
("MING-CHU", 0.05),
("CHIU-HSIANG", 0.05),
("HSIU-YU", 0.05),
("LI-YUN", 0.05),
("LI-YU", 0.05),
("PAO-CHU", 0.05),
("I-TING", 0.01),
("LI-LING", 0.05),
("I-CHEN", 0.04),
("YUEH-YING", 0.04),
("SHU-FANG", 0.04),
("YU-LING", 0.04),
("HSIU-YUN", 0.04),
("CHUN-MEI", 0.04),
("PI-HSIA", 0.04),
("LI-HSIANG", 0.04),
("MEI-FENG", 0.04),
("MEI-CHEN", 0.04),
("MEI-YING", 0.04),
("PI-CHU", 0.04),
("PI-YUN", 0.04),
("CHIA-JUNG", 0.04),
("MEI-LAN", 0.04),
("HSIU-CHUAN", 0.04),
("MEI-CHUAN", 0.04),
("SHU-MIN", 0.04),
("YU-CHEN", 0.01),
("SHU-CHING", 0.04),
("CHING-I", 0.04),
("SU-CHU", 0.04),
("YA-PING", 0.04),
("SU-CHING", 0.04),
("SU-CHIN", 0.04),
("HSIU-CHIH", 0.04),
("CHIN-LIEN", 0.04),
("CHIU-YUEH", 0.04),
("LI-HSUEH", 0.04),
("HUI-CHEN", 0.04),
("CHIA-LING", 0.04),
("YU-TING", 0.01),
("SHIH-HAN", 0.04),
("HSIU-HSIA", 0.04),
("HSIU-HUA", 0.03),
("LI-CHIN", 0.03),
("CHIN-FENG", 0.03),
("LI-CHEN", 0.03),
("YU-FENG", 0.03),
("YU-CHIN", 0.03),
("HSIU-LIEN", 0.03),
("SU-LAN", 0.03),
("WAN-TING", 0.01),
("PEI-SHAN", 0.01),
("I-HSUAN", 0.01),
("YA-CHU", 0.01),
("HSIN-YU", 0.01),
("SSU-YU", 0.01),
("CHIA-YING", 0.01),
("PIN-YU", 0.01),
("TZU-HAN", 0.01),
("PIN-YEN", 0.01),
("TZU-CHING", 0.01),
("YUNG-CHING", 0.01),
("YU-TUNG", 0.01),
("I-CHING", 0.01),
("I-FEI", 0.01),
("YU-FEI", 0.01),
("YUN-FEI", 0.01),
("I-AN", 0.01),
("YUEH-TUNG", 0.01),
)
)
first_romanized_names = first_romanized_names_male.copy()
first_romanized_names.update(first_romanized_names_female)
romanized_formats_female = OrderedDict(
(("{{last_romanized_name}} {{first_romanized_name_female}}", 1),) # 漢人 Han
)
romanized_formats_male = OrderedDict((("{{last_romanized_name}} {{first_romanized_name_male}}", 1),)) # 漢人 Han
romanized_formats = romanized_formats_male.copy()
romanized_formats.update(romanized_formats_female)
def first_romanized_name_male(self) -> str: # 只有jp有實作
"""
:example: 'CHIA-HAO'
"""
return self.random_element(self.first_romanized_names_male)
def first_romanized_name_female(self) -> str: # 只有jp有實作
"""
:example: 'SHU-FEN'
"""
return self.random_element(self.first_romanized_names_female)
def romanized_name(self) -> str: # 姓名
"""
:example: 'WANG SHU-FEN'
"""
pattern: str = self.random_element(self.romanized_formats)
return self.generator.parse(pattern)
def first_romanized_name(self) -> str: # 只有姓
"""
:example: 'WANG'
"""
return self.random_element(self.first_romanized_names)
def last_romanized_name(self) -> str: # 只有名
"""
:example: 'SHU-FEN'
"""
return self.random_element(self.last_romanized_names)
def romanized_name_male(self) -> str: # 男生姓名
"""
:example: 'WANG CHIH-MING'
"""
pattern: str = self.random_element(self.romanized_formats_male)
return self.generator.parse(pattern)
def romanized_name_female(self) -> str: # 女生姓名
"""
:example: 'WANG SHU-FEN'
"""
pattern: str = self.random_element(self.romanized_formats_female)
return self.generator.parse(pattern)

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