ethnicity

Get ethnicity from name. Currently, we cover the following ethnicities:

• Anglo-Saxon
• Arabic
• Chinese
• Fijian
• German
• Greek
• Hawaiian
• Hispanic (Spanish and Mexican)
• Indian (excluding islamic states)
• Iranian
• Italian
• Japanese
• Khmer (Cambodian)
• Korean
• Polish
• Portuguese (Portuguese and Brazilian)
• Russian
• Samoan
• South-Slavic (Serbian, Croatian, Bosnian, Slovenian)
• Thai
• Turkish
• Vietnamese

Data Sources

• Surnames by race (the US Census data)
• Most common Japanese surnames
• Most common Indian surnames
• Most common Greek surnames
• Most common Italian surnames
• Samoan surnames
• Hawaiian surnames
• German surnames
• Most popular Spanish names
• Most frequent Spanish surnames
• Most common Portuguese surnames
• Most common names I Brazil
• Most common surnames in Brazil
• Most common Russian surnames
• Popular baby names in NSW, Australia
• Most common surnames in Saudi Arabia
• Most common surnames in Egypt
• Most common surnames in Lebanon
• Most common surnames in Morocco
• Most common Palestinian Surnames
• Most common surnames in Kuwait
• Most common surnames in Europe

Installation

pip3 install ethnicity

Usage

# initialize and create dictionaries
e = Ethnicity().make_dicts()  

# apply to a list of names
e.get(['emele kuoi', 'andrew miller', 'peter', 'andrey',  'nima al hassan', 'tomasz bolowski', 'christiano ronaldo', 'parisa karimi,', 'lisa bowen', 'melissa chan'])

# which gives you a pandas dataframe as below
                 Name    Ethnicity
0          Emele Kuoi       fijian
1       Andrew Miller  anglo-saxon
2               Peter          ---
3              Andrey      russian
4      Nima Al Hassan       arabic
5     Tomasz Bolowski       polish
6  Christiano Ronaldo   portuguese
7       Parisa Karimi      iranian
8          Lisa Bowen  anglo-saxon
9        Melissa Chan      chinese