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Predict Race and Ethnicity Based on the Sequence of Characters in a Name
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ethnicolr: Predict Race and Ethnicity From Name

.. image:: https://travis-ci.com/appeler/ethnicolr.svg?branch=master :target: https://travis-ci.com/appeler/ethnicolr .. image:: https://ci.appveyor.com/api/projects/status/u9fe72hn8nnhmaxt?svg=true :target: https://ci.appveyor.com/project/soodoku/ethnicolr-m6u1p .. image:: https://img.shields.io/pypi/v/ethnicolr.svg :target: https://pypi.python.org/pypi/ethnicolr .. image:: https://anaconda.org/soodoku/ethnicolr/badges/version.svg :target: https://anaconda.org/soodoku/ethnicolr/ .. image:: https://pepy.tech/badge/ethnicolr :target: https://pepy.tech/project/ethnicolr

We exploit the US census data, the Florida voting registration data, and the Wikipedia data collected by Skiena and colleagues, to predict race and ethnicity based on first and last name or just the last name. The granularity at which we predict the race depends on the dataset. For instance, Skiena et al.' Wikipedia data is at the ethnic group level, while the census data we use in the model (the raw data has additional categories of Native Americans and Bi-racial) merely categorizes between Non-Hispanic Whites, Non-Hispanic Blacks, Asians, and Hispanics.

DIME Race

Data on race of all the people in the DIME data <https://data.stanford.edu/dime>__ is posted here <http://dx.doi.org/10.7910/DVN/M5K7VR>__ The underlying python scripts are posted here <https://github.com/appeler/dime_race>__

Caveats and Notes

If you picked a random individual with last name 'Smith' from the US in 2010
and asked us to guess this person's race (measured as crudely as by the census), the best guess would be based on what is available from the aggregated Census file. It is the Bayes Optimal Solution. So what good are last name only predictive models for? A few things---if you want to impute ethnicity at a more granular level, guess the race of people in different years (than when the census was conducted if some assumptions hold), guess the race of people in different countries (again if some assumptions hold), when names are slightly different (again with some assumptions), etc. The big benefit comes from when both the first name and last name is known.

Install

We strongly recommend installing ethnicolor inside a Python virtual environment (see venv documentation <https://docs.python.org/3/library/venv.html#creating-virtual-environments>__)

::

pip install ethnicolr

Or ::

conda install -c soodoku ethnicolr

Notes:

  • The models are run and verified on Tensorflow 2x using Python 3.7 and 3.8 and lower will work. tf1x has been deprecated.
  • If you are installing on Windows, Theano installation typically needs admin. privileges on the shell.

General API

To see the available command line options for any function, please type in <function-name> --help

::

census_ln --help

usage: census_ln [-h] [-y {2000,2010}] [-o OUTPUT] -l LAST input

Appends Census columns by last name

positional arguments: input Input file

optional arguments: -h, --help show this help message and exit -y {2000,2010}, --year {2000,2010} Year of Census data (default=2000) -o OUTPUT, --output OUTPUT Output file with Census data columns -l LAST, --last LAST Name or index location of column contains the last name

Examples

To append census data from 2010 to a file without column headers <ethnicolr/data/input-without-header.csv>__ and the first column carries the last name, use -l 0

::

census_ln -y 2010 -o output-census2010.csv -l 0 input-without-header.csv

To append census data from 2010 to a file with column header in the first row <ethnicolr/data/input-with-header.csv>__, specify the column name carrying last names using the -l option, keeping the rest the same:

::

census_ln -y 2010 -o output-census2010.csv -l last_name input-with-header.csv

To predict race/ethnicity using Wikipedia full name model <ethnicolr/models/ethnicolr_keras_lstm_wiki_name.ipynb>__, if the input file doesn't have any column headers, you must using -l and -f to specify the index of column carrying the last name and first name respectively (first column has index 0).

::

pred_wiki_name -o output-wiki-pred-race.csv -l 0 -f 1 input-without-header.csv

And to predict race/ethnicity using Wikipedia full name model <ethnicolr/models/ethnicolr_keras_lstm_wiki_name.ipynb>__ for a file with column headers, you can specify the column name of last name and first name by using -l and -f flags respectively.

::

pred_wiki_name -o output-wiki-pred-race.csv -l last_name -f first_name input-with-header.csv

Functions

We expose 6 functions, each of which either take a pandas DataFrame or a CSV. If the CSV doesn't have a header, we make some assumptions about where the data is

  • census_ln

    • Input: pandas DataFrame or CSV and a string or list of the name or location of the column containing the last name.

    • What it does:

      • Removes extra space.
      • For names in the census file <ethnicolr/data/census>__, it appends relevant data.

    • Options:

      • year: 2000 or 2010
      • if no year is given, data from the 2000 census is appended

    • Output: Appends the following columns to the pandas DataFrame or CSV: pctwhite, pctblack, pctapi, pctaian, pct2prace, pcthispanic. See here <https://github.com/appeler/ethnicolr/blob/master/ethnicolr/data/census/census_2000.pdf>__ for what the column names mean.

  • pred_census_ln

    • Input: pandas DataFrame or CSV and string or list of the name or location of the column containing the last name.

    • What it does:

      • Removes extra space.
      • Uses the last name census 2000 model <ethnicolr/models/ethnicolr_keras_lstm_census2000_ln.ipynb>__ or last name census 2010 model <ethnicolr/models/ethnicolr_keras_lstm_census2010_ln.ipynb>__ to predict the race and ethnicity.

    • Options:

      • year: 2000 or 2010

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), api (percentage chance asian), black, hispanic, white.

  • pred_wiki_ln

    • Input: pandas DataFrame or CSV and string or list of the name or location of the column containing the last name.

    • What it does:

      • Removes extra space.
      • Uses the last name wiki model <ethnicolr/models/ethnicolr_keras_lstm_wiki_ln.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable --- category with the highest probability), "Asian,GreaterEastAsian,EastAsian", "Asian,GreaterEastAsian,Japanese", "Asian,IndianSubContinent", "GreaterAfrican,Africans", "GreaterAfrican,Muslim", "GreaterEuropean,British","GreaterEuropean,EastEuropean", "GreaterEuropean,Jewish","GreaterEuropean,WestEuropean,French", "GreaterEuropean,WestEuropean,Germanic","GreaterEuropean,WestEuropean,Hispanic", "GreaterEuropean,WestEuropean,Italian","GreaterEuropean,WestEuropean,Nordic"

  • pred_wiki_name

    • Input: pandas DataFrame or CSV and string or list containing the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.

    • What it does:

      • Removes extra space.
      • Uses the full name wiki model <ethnicolr/models/ethnicolr_keras_lstm_wiki_name.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (categorical variable---category with the highest probability), "Asian,GreaterEastAsian,EastAsian", "Asian,GreaterEastAsian,Japanese", "Asian,IndianSubContinent", "GreaterAfrican,Africans", "GreaterAfrican,Muslim", "GreaterEuropean,British","GreaterEuropean,EastEuropean", "GreaterEuropean,Jewish","GreaterEuropean,WestEuropean,French", "GreaterEuropean,WestEuropean,Germanic","GreaterEuropean,WestEuropean,Hispanic", "GreaterEuropean,WestEuropean,Italian","GreaterEuropean,WestEuropean,Nordic"

  • pred_fl_reg_ln

    • Input: pandas DataFrame or CSV and string or list of the name or location of the column containing the last name.

    • What it does?:

      • Removes extra space, if there.
      • Uses the last name FL registration model <ethnicolr/models/ethnicolr_keras_lstm_fl_voter_ln.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white

  • pred_fl_reg_name

    • Input: pandas DataFrame or CSV and string or list containing the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.

    • What it does:

      • Removes extra space.
      • Uses the full name FL model <ethnicolr/models/ethnicolr_keras_lstm_fl_voter_name.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), asian (percentage chance Asian), hispanic, nh_black, nh_white

  • pred_fl_reg_ln_five_cat

    • Input: pandas DataFrame or CSV and string or list of the name or location of the column containing the last name.

    • What it does?:

      • Removes extra space, if there.
      • Uses the last name FL registration model <ethnicolr/models/ethnicolr_keras_lstm_fl_voter_ln_five_cat.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other

  • pred_fl_reg_name_five_cat

    • Input: pandas DataFrame or CSV and string or list containing the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.

    • What it does:

      • Removes extra space.
      • Uses the full name FL model <ethnicolr/models/ethnicolr_keras_lstm_fl_voter_ln_five_cat.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, hispanic, or other), asian (percentage chance Asian), hispanic, nh_black, nh_white, other

  • pred_nc_reg_name

    • Input: pandas DataFrame or CSV and string or list containing the name or location of the column containing the first name, last name, middle name, and suffix, if there. The first name and last name columns are required. If no middle name of suffix columns are there, it is assumed that there are no middle names or suffixes.

    • What it does:

      • Removes extra space.
      • Uses the full name NC model <ethnicolr/models/ethnicolr_keras_lstm_nc_12_cat_model.ipynb>__ to predict the race and ethnicity.

    • Output: Appends the following columns to the pandas DataFrame or CSV: race + ethnicity. The codebook is here <https://github.com/appeler/nc_race_ethnicity>__

Using ethnicolr

::

import pandas as pd

from ethnicolr import census_ln, pred_census_ln Using TensorFlow backend.

names = [{'name': 'smith'}, ... {'name': 'zhang'}, ... {'name': 'jackson'}]

df = pd.DataFrame(names)

df name 0 smith 1 zhang 2 jackson

census_ln(df, 'name') name pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith 73.35 22.22 0.40 0.85 1.63 1.56 1 zhang 0.61 0.09 98.16 0.02 0.96 0.16 2 jackson 41.93 53.02 0.31 1.04 2.18 1.53

census_ln(df, 'name', 2010) name race pctwhite pctblack pctapi pctaian pct2prace pcthispanic 0 smith white 70.9 23.11 0.5 0.89 2.19 2.4 1 zhang api 0.99 0.16 98.06 0.02 0.62 0.15 2 jackson black 39.89 53.04 0.39 1.06 3.12 2.5

pred_census_ln(df, 'name') name race api black hispanic white 0 smith white 0.002019 0.247235 0.014485 0.736260 1 zhang api 0.997807 0.000149 0.000470 0.001574 2 jackson black 0.002797 0.528193 0.014605 0.454405

help(pred_census_ln) Help on function pred_census_ln in module ethnicolr.pred_census_ln:

pred_census_ln(df, namecol, year=2000) Predict the race/ethnicity by the last name using Census model.

   Using the Census last name model to predict the race/ethnicity of the input
   DataFrame.

   Args:
       df (:obj:`DataFrame`): Pandas DataFrame containing the last name
           column.
       namecol (str or int): Column's name or location of the name in
           DataFrame.
       year (int): The year of Census model to be used. (2000 or 2010)
           (default is 2000)

   Returns:
       DataFrame: Pandas DataFrame with additional columns:
           - `race` the predict result
           - `black`, `api`, `white`, `hispanic` are the prediction
               probability.

Application

To illustrate how the package can be used, we impute the race of the campaign contributors recorded by FEC for the years 2000 and 2010 and tally campaign contributions by race.

  • Contrib 2000/2010 using census_ln <ethnicolr/examples/ethnicolr_app_contrib20xx-census_ln.ipynb>__
  • Contrib 2000/2010 using pred_census_ln <ethnicolr/examples/ethnicolr_app_contrib20xx.ipynb>__
  • Contrib 2000/2010 using pred_fl_reg_name <ethnicolr/examples/ethnicolr_app_contrib20xx-fl_reg.ipynb>__

Data on race of all the people in the DIME data <https://data.stanford.edu/dime>__ is posted here <http://dx.doi.org/10.7910/DVN/M5K7VR>__ The underlying python scripts are posted here <https://github.com/appeler/dime_race>__

Data

In particular, we utilize the last-name--race data from the 2000 census <http://www.census.gov/topics/population/genealogy/data/2000_surnames.html>__ and 2010 census <http://www.census.gov/topics/population/genealogy/data/2010_surnames.html>, the Wikipedia data <ethnicolr/data/wiki/> collected by Skiena and colleagues, and the Florida voter registration data from early 2017.

  • Census <ethnicolr/data/census/>__
  • The Wikipedia dataset <ethnicolr/data/wiki/>__
  • Florida voter registration database <http://dx.doi.org/10.7910/DVN/UBIG3F>__

Evaluation

  1. SCAN Health Plan, a Medicare Advantage plan that serves over 200,000 members throughout California used the software to better assess racial disparities of health among the people they serve. They only had racial data on about 47% of their members so used it to learn the race of the remaining 53%. On the data they had labels for, they found .9 AUC and 83% accuracy for the last name model.

  2. Evaluation on NC Data: https://github.com/appeler/nc_race_ethnicity

Authors

Suriyan Laohaprapanon and Gaurav Sood

Contributor Code of Conduct

The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct <http://contributor-covenant.org/version/1/0/0/>__.

License

The package is released under the MIT License <https://opensource.org/licenses/MIT>__.


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