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Predict Race/Ethnicity Based on Name

Project description

ethnicolr: Predict Race and Ethnicity From Name

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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.

Downloads

As of February 3rd, 2018, the package had been downloaded over 1,900 times (see saved BigQuery).

DIME Race

Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here

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

pip install ethnicolr

Or

conda install ethnicolr

Note: 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 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, 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, 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 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, 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
  • 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:
    • 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:
    • 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:
    • 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?:
    • 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:
    • 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

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.

Data on race of all the people in the DIME data is posted here The underlying python scripts are posted here

Data

In particular, we utilize the last-name–race data from the 2000 census and 2010 census, the Wikipedia data collected by Skiena and colleagues, and the Florida voter registration data from early 2017.

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.

License

The package is released under the MIT License.

Project details


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Filename, size & hash SHA256 hash help File type Python version Upload date
ethnicolr-0.1.7.tar.gz (16.9 MB) Copy SHA256 hash SHA256 Source None May 7, 2018

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