ethnicolr2: Predict Race and Ethnicity From Name

A pytorch implementation of ethnicolr with new models that make different assumptions (for instance, this package has models trained on unique names) than ethnicolr. The package uses the US census data and the Florida voting registration data to build models to predict the race and ethnicity (Non-Hispanic Whites, Non-Hispanic Blacks, Asians, Hispanics, and Other) based on first and last name or just the last name. For notebooks underlying the package, see here.

Caveats and Notes

If you picked a random person with the last name 'Smith' in the US in 2010 and asked us to guess this person's race (as measured by the census), the best guess is the modal race of the person named Smith (which you can get from the census popular last name data file). It is the Bayes Optimal Solution. So what good are predictive models? A few things---if you want to impute race and ethnicity for last names that are not in the census file, which can be because of errors, infer the race and ethnicity in different years than when the census was conducted (if some assumptions hold), infer the race of people in different countries (if some assumptions hold), etc. The biggest benefit comes in cases where both the first and last name are known.

Install

We strongly recommend installing ethnicolor2 inside a Python virtual environment (see venv documentation)

pip install ethnicolr2

Example

To predict race/ethnicity using the Florida Last Name Model to a file with first and last names

import pandas as pd
df = pd.read_csv("ethnicolr2/data/input-with-header.csv")
pred_fl_last_name(df, lname_col = "last_name")


names = [
 {"last": "sawyer", "first": "john", "true_race": "nh_white"},
 {"last": "torres", "first": "raul", "true_race": "hispanic"},
]
df = pd.DataFrame(names) 
df = pred_fl_full_name(df, lname_col = "last", fname_col = "first")

      last  first true_race   preds
0  sawyer   john  nh_white nh_white
1  torres   raul  hispanic hispanic

Authors

Rajashekar Chintalapati, 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.