ethnicolr
pypi i ethnicolr

ethnicolr

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

by appeler

0.9.1 (see all)License:MIT
pypi i ethnicolr
Readme

ethnicolr: Predict Race and Ethnicity From Name

.. image:: https://github.com/appeler/ethnicolr/workflows/test/badge.svg :target: https://github.com/appeler/ethnicolr/actions?query=workflow%3Atest .. 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 2.x using Python 3.7 and 3.8 and lower will work. TensorFlow 1.x 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(df, namecol, year=2000)

    • What it does:

      • Removes extra space
      • For names in the census file <ethnicolr/data/census>__, it appends relevant data of what probability the name provided is of a certain race/ethnicity

+------------+--------------------------------------------------------------------------------------------------------------------------+ | Parameters | | +============+==========================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +------------+--------------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list, int} string or list of the name or location of the column containing the last name | +------------+--------------------------------------------------------------------------------------------------------------------------+ | | Year : {2000, 2010}, default=2000 year of census to use | +------------+--------------------------------------------------------------------------------------------------------------------------+

  • 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.

    ::

    import pandas as pd

    from ethnicolr import census_ln, pred_census_ln

    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

  • pred_census_ln(df, namecol, year=2000, num_iter=100, conf_int=0.9)

    • 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.

+--------------+---------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+=====================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list, int} string or list of the name or location of the column containing the last name | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | year : {2000, 2010}, default=2000 year of census to use | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+---------------------------------------------------------------------------------------------------------------------+

  • Output: Appends the following columns to the pandas DataFrame or CSV: race (white, black, asian, or hispanic), api (percentage chance asian), black, hispanic, white. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above) ::

      >>> 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
    
  • pred_wiki_ln( df, namecol, num_iter=100, conf_int=0.9)

    • 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.

+--------------+---------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+=====================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list, int} string or list of the name or location of the column containing the last name | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+---------------------------------------------------------------------------------------------------------------------+

  • 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". For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    ::

    import pandas as pd

    names = [ ... {"last": "smith", "first": "john", "true_race": "GreaterEuropean,British"}, ... { ... "last": "zhang", ... "first": "simon", ... "true_race": "Asian,GreaterEastAsian,EastAsian", ... }, ... ] df = pd.DataFrame(names)

    from ethnicolr import pred_wiki_ln, pred_wiki_name

    odf = pred_wiki_ln(df,'last') ['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']

    odf last first ... GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john ... 0.004559 GreaterEuropean,British 1 Zhang simon ... 0.004076 Asian,GreaterEastAsian,EastAsian

    [2 rows x 57 columns]

    odf.iloc[0,:8] last Smith first john true_race GreaterEuropean,British Asian,GreaterEastAsian,EastAsian_mean 0.004554 Asian,GreaterEastAsian,EastAsian_std 0.003358 Asian,GreaterEastAsian,EastAsian_lb 0.000535 Asian,GreaterEastAsian,EastAsian_ub 0.000705 Name: 0, dtype: object

  • pred_wiki_name(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+============================================================================================================================================================================================================================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list} string or list of 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. | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

  • 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". For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above) ::

    odf = pred_wiki_name(df, 'last', 'first') ['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']

    odf last first ... GreaterEuropean,WestEuropean,Nordic_ub race 0 Smith john ... 0.000236 GreaterEuropean,British 1 Zhang simon ... 0.000021 Asian,GreaterEastAsian,EastAsian

    [2 rows x 58 columns]

    odf.iloc[1,:8] last Zhang first simon true_race Asian,GreaterEastAsian,EastAsian Asian,GreaterEastAsian,EastAsian_mean 0.890619 Asian,GreaterEastAsian,EastAsian_std 0.119097 Asian,GreaterEastAsian,EastAsian_lb 0.391496 Name: 1, dtype: object

  • pred_fl_reg_ln(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+---------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+=====================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list, int} string or list of the name or location of the column containing the last name | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+---------------------------------------------------------------------------------------------------------------------+

  • 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. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    ::

    import pandas as pd

    names = [ ... {"last": "sawyer", "first": "john", "true_race": "nh_white"}, ... {"last": "torres", "first": "raul", "true_race": "hispanic"}, ... ]

    df = pd.DataFrame(names)

    from ethnicolr import pred_fl_reg_ln, pred_fl_reg_name, pred_fl_reg_ln_five_cat, pred_fl_reg_name_five_cat

    odf = pred_fl_reg_ln(df, 'last') ['asian', 'hispanic', 'nh_black', 'nh_white']

    odf last first true_race asian_mean asian_std ... nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0.004004 0.004483 ... 0.015442 0.908452 0.035121 0.722879 0.804443 nh_white 1 Torres raul hispanic 0.005882 0.002249 ... 0.005305 0.182575 0.072142 0.074511 0.090856 hispanic

    [2 rows x 21 columns]

    odf.iloc[0] last Sawyer first john true_race nh_white asian_mean 0.004004 asian_std 0.004483 asian_lb 0.000899 asian_ub 0.00103 hispanic_mean 0.034227 hispanic_std 0.01294 hispanic_lb 0.017406 hispanic_ub 0.017625 nh_black_mean 0.053317 nh_black_std 0.028634 nh_black_lb 0.010537 nh_black_ub 0.015442 nh_white_mean 0.908452 nh_white_std 0.035121 nh_white_lb 0.722879 nh_white_ub 0.804443 race nh_white Name: 0, dtype: object

  • pred_fl_reg_name(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+============================================================================================================================================================================================================================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list} string or list of 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. | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

  • 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. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above) ::

    odf = pred_fl_reg_name(df, 'last', 'first') ['asian', 'hispanic', 'nh_black', 'nh_white']

    odf last first true_race asian_mean ... nh_black_ub nh_white_mean nh_white_std nh_white_lb nh_white_ub race 0 Sawyer john nh_white 0.001196 ... 0.005450 0.971152 0.015757 0.915592 0.918630 nh_white 1 Torres raul hispanic 0.004770 ... 0.000885 0.066303 0.028486 0.022593 0.024143 hispanic

    [2 rows x 22 columns]

    odf.iloc[1] last Torres first raul true_race hispanic asian_mean 0.00477 asian_std 0.002943 asian_lb 0.000904 asian_ub 0.001056 hispanic_mean 0.9251 hispanic_std 0.032224 hispanic_lb 0.829494 hispanic_ub 0.8385 nh_black_mean 0.003826 nh_black_std 0.002735 nh_black_lb 0.000838 nh_black_ub 0.000885 nh_white_mean 0.066303 nh_white_std 0.028486 nh_white_lb 0.022593 nh_white_ub 0.024143 race hispanic Name: 1, dtype: object

  • pred_fl_reg_ln_five_cat(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+---------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+=====================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list, int} string or list of the name or location of the column containing the last name | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+---------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+---------------------------------------------------------------------------------------------------------------------+

  • 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. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above) ::

    odf = pred_fl_reg_ln_five_cat(df,'last') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']

    odf last first true_race asian_mean asian_std ... nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0.142867 0.046145 ... 0.203204 0.221313 0.235889 0.023794 0.192840 0.193671 nh_white 1 Torres raul hispanic 0.101397 0.028399 ... 0.090068 0.100212 0.238645 0.034070 0.136617 0.145928 hispanic

    [2 rows x 26 columns]

    odf.iloc[0] last Sawyer first john true_race nh_white asian_mean 0.142867 asian_std 0.046145 asian_lb 0.067382 asian_ub 0.073285 hispanic_mean 0.068199 hispanic_std 0.020641 hispanic_lb 0.02565 hispanic_ub 0.030017 nh_black_mean 0.239793 nh_black_std 0.076287 nh_black_lb 0.084239 nh_black_ub 0.085626 nh_white_mean 0.313252 nh_white_std 0.046173 nh_white_lb 0.203204 nh_white_ub 0.221313 other_mean 0.235889 other_std 0.023794 other_lb 0.19284 other_ub 0.193671 race nh_white Name: 0, dtype: object

  • pred_fl_reg_name_five_cat(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+============================================================================================================================================================================================================================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list} string or list of 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. | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

  • 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. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    (Using the same dataframe from example above) ::

    odf = pred_fl_reg_name_five_cat(df, 'last','first') ['asian', 'hispanic', 'nh_black', 'nh_white', 'other']

    odf last first true_race asian_mean asian_std ... nh_white_lb nh_white_ub other_mean other_std other_lb other_ub race 0 Sawyer john nh_white 0.194250 0.120314 ... 0.126987 0.167742 0.259069 0.030386 0.142455 0.177375 nh_white 1 Torres raul hispanic 0.081465 0.038318 ... 0.019312 0.020782 0.158614 0.039180 0.081994 0.083105 hispanic

    [2 rows x 26 columns]

    odf.iloc[1] last Torres first raul true_race hispanic asian_mean 0.081465 asian_std 0.038318 asian_lb 0.032789 asian_ub 0.034667 hispanic_mean 0.646059 hispanic_std 0.144663 hispanic_lb 0.188246 hispanic_ub 0.219772 nh_black_mean 0.037737 nh_black_std 0.045439 nh_black_lb 0.006477 nh_black_ub 0.006603 nh_white_mean 0.076125 nh_white_std 0.059213 nh_white_lb 0.019312 nh_white_ub 0.020782 other_mean 0.158614 other_std 0.03918 other_lb 0.081994 other_ub 0.083105 race hispanic Name: 1, dtype: object

  • pred_nc_reg_name(df, namecol, num_iter=100, conf_int=0.9)

    • 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.

    +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parameters | | +==============+============================================================================================================================================================================================================================================================================================================================+ | | df : {DataFrame, csv} Pandas dataframe of CSV file contains the names of the individual to be inferred | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | namecol : {string, list} string or list of 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. | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | num_iter : int, default=100 number of iterations to calculate uncertainty in model | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | | conf_int : float, default=1.0 confidence interval in predicted class | +--------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

  • Output: Appends the following columns to the pandas DataFrame or CSV: race + ethnicity. The codebook is here <https://github.com/appeler/nc_race_ethnicity>__. For each race it will provide the mean, standard error, lower & upper bound of confidence interval

    ::

    import pandas as pd

    names = [ ... {"last": "hernandez", "first": "hector", "true_race": "HL+O"}, ... {"last": "zhang", "first": "simon", "true_race": "NL+A"}, ... ]

    df = pd.DataFrame(names)

    from ethnicolr import pred_nc_reg_name

    odf = pred_nc_reg_name(df, 'last','first') ['HL+A', 'HL+B', 'HL+I', 'HL+M', 'HL+O', 'HL+W', 'NL+A', 'NL+B', 'NL+I', 'NL+M', 'NL+O', 'NL+W']

    odf last first true_race HL+A_mean HL+A_std HL+A_lb HL+A_ub HL+B_mean ... NL+M_ub NL+O_mean NL+O_std NL+O_lb NL+O_ub NL+W_mean NL+W_std NL+W_lb NL+W_ub race 0 hernandez hector HL+O 0.000054 0.000354 5.833132e-10 4.291366e-09 0.009606 ... 0.000416 0.090123 0.036310 0.000705 0.003757 0.021228 0.021222 0.000368 0.001230 HL+O 1 zhang simon NL+A 0.000603 0.002808 1.988648e-07 2.766486e-07 0.000026 ... 0.000086 0.125159 0.042818 0.050547 0.057208 0.003149 0.005437 0.000210 0.000225 NL+A

    [2 rows x 54 columns]

    odf.iloc[0] last hernandez first hector true_race HL+O HL+A_mean 0.000054 HL+A_std 0.000354 HL+A_lb 0.0 HL+A_ub 0.0 HL+B_mean 0.009606 HL+B_std 0.040739 HL+B_lb 0.0 HL+B_ub 0.000003 HL+I_mean 0.001605 HL+I_std 0.004569 HL+I_lb 0.0 HL+I_ub 0.0 HL+M_mean 0.147628 HL+M_std 0.215733 HL+M_lb 0.001253 HL+M_ub 0.001297 HL+O_mean 0.36902 HL+O_std 0.132249 HL+O_lb 0.002289 HL+O_ub 0.019187 HL+W_mean 0.264246 HL+W_std 0.090536 HL+W_lb 0.001782 HL+W_ub 0.015628 NL+A_mean 0.012004 NL+A_std 0.010873 NL+A_lb 0.000121 NL+A_ub 0.000281 NL+B_mean 0.010891 NL+B_std 0.01404 NL+B_lb 0.000094 NL+B_ub 0.000383 NL+I_mean 0.005182 NL+I_std 0.008259 NL+I_lb 0.000009 NL+I_ub 0.000068 NL+M_mean 0.068412 NL+M_std 0.08564 NL+M_lb 0.000172 NL+M_ub 0.000416 NL+O_mean 0.090123 NL+O_std 0.03631 NL+O_lb 0.000705 NL+O_ub 0.003757 NL+W_mean 0.021228 NL+W_std 0.021222 NL+W_lb 0.000368 NL+W_ub 0.00123 race HL+O Name: 0, dtype: object

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, Gaurav Sood and Bashar Naji

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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