Infer Gender from Indian Names

gender, names, electoral-rolls, gender-classification, india
pip install naampy==0.6.0


naampy: Infer Sociodemographic Characteristics from Indian Names

The ability to programmatically reliably infer the social attributes of a person from their name can be useful for a broad set of tasks, from estimating bias in coverage of women in the media to estimating bias in lending against certain social groups. But unlike the American Census Bureau, which produces a list of last names and first names, which can (and are) used to infer the gender, race, ethnicity, etc., from names, the Indian government produces no such commensurate datasets. And hence inferring the relationship between gender, ethnicity, language group, etc., and names have generally been done with small datasets constructed in an ad-hoc manner.

We fill this yawning gap. Using data from the Indian Electoral Rolls (parsed data here), we estimate the proportion female, male, and third sex (see here) for a particular first name, year, and state.

Please also check out pranaam that uses land record data from Bihar to infer religion based on the name. The package uses indicate to transliterate Hindi to English.


In all, we capitalize on information in the parsed electoral rolls from the following 31 states and union territories:

Andaman Delhi Kerala Puducherry
Andhra Pradesh Goa Madhya Pradesh Punjab
Arunachal Pradesh Gujarat Maharashtra Rajasthan
Assam Haryana Manipur Sikkim
Bihar Himachal Pradesh Meghalaya Tripura
Chandigarh Jammu and Kashmir Mizoram Uttar Pradesh
Dadra Jharkhand Nagaland Uttarakhand
Daman Karnataka Odisha  

How is the underlying data produced?

We split the name into first name and last name (see the python notebook for how we do this) and then aggregate per state and first_name, and tabulate prop_male, prop_female, prop_third_gender, n_female, n_male, n_third_gender. We produce native language rolls and english transliterations. (We use indicate to produce transliterations for hindi rolls.)

This is used to provide the base prediction.

Given the association between prop_female and first_name may change over time, we exploited the age. Given the data were collected in 2017, we calculated the year each person was born and then did a group by year to create prop_male, prop_female, prop_third_gender, n_female, n_male, n_third_gender

Issues with underlying data


  • Voting registration lists may not be accurate, systematically underrepresenting poor people, minorities, and similar such groups.
  • Voting registration lists are, at best, a census of adult citizens. But to the extent there is prejudice against women, etc., that prevents them from reaching adulthood, the data bakes those biases in.
  • Indian names are complicated. We do not have good parsers for them yet. We have gone for the default arrangement. Please go through the notebook to look at the judgments we make. We plan to improve the underlying data over time.
  • For state electoral rolls that are neither in English and Hindi, we use libindic. The quality of transliterations is consistently bad.

Gender Classifier

We start by providing a base model for first_name that gives the Bayes optimal solution---the proportion of people with that name who are women. We also provide a series of base models where the state of residence and year of birth is known.

If the name does not exist in the database, we use ML model that uses the relationship between sequences of characters in the first name and gender to predict gender from the name.

The model was trained as a regression problem instead of a classification problem because men and women share names. (See the histogram below for the female proportion for the dataset.) The model predicts the female proportion of the name. If it is less than 0.5, we classify it as male; otherwise, we classify it as female.

Female proportion

Test data

MSE no weights - loss: .05, metric: 0.05

RMSE no weights - loss: 0.22, metric: 0.22

Test data with weights

MSE with weights - loss: 0.05, metric: 0.04

RMSE with weights - loss: 0.22, metric: 0.22

Below are the inference results using different models.

Inference on different models


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

pip install naampy


usage: in_rolls_fn_gender [-h] -f FIRST_NAME
                        [-s {andaman,andhra,arunachal,assam,bihar,chandigarh,dadra,daman,delhi,goa,gujarat,haryana,himachal,jharkhand,jk,karnataka,kerala,maharashtra,manipur,meghalaya,mizoram,mp,nagaland,odisha,puducherry,punjab,rajasthan,sikkim,tripura,up,uttarakhand}]
                        [-y YEAR] [-o OUTPUT]

Appends Electoral roll columns for prop_female, n_female, n_male
n_third_gender by first name

positional arguments:
input                 Input file

optional arguments:
-h, --help            show this help message and exit
-f FIRST_NAME, --first-name FIRST_NAME
                        Name or index location of column contains the first
-s {andaman,andhra,arunachal,assam,bihar,chandigarh,dadra,daman,delhi,goa,gujarat,haryana,himachal,jharkhand,jk,karnataka,kerala,maharashtra,manipur,meghalaya,mizoram,mp,nagaland,odisha,puducherry,punjab,rajasthan,sikkim,tripura,up,uttarakhand},
--state {andaman,andhra,arunachal,assam,bihar,chandigarh,dadra,daman,delhi,goa,gujarat,haryana,himachal,jharkhand,jk,karnataka,kerala,maharashtra,manipur,meghalaya,mizoram,mp,nagaland,odisha,puducherry,punjab,rajasthan,sikkim,tripura,up,uttarakhand}
                        State name of Indian electoral rolls data
-y YEAR, --year YEAR  Birth year in Indian electoral rolls data
-o OUTPUT, --output OUTPUT
                        Output file with Indian electoral rolls data columns

    choices=["v1", "v2", "v2_1k", "v2_native", "v2_en"],

Using naampy

>>> import pandas as pd
>>> from naampy import in_rolls_fn_gender

>>> names = [{'name': 'gaurav'},
             {'name': 'nabha'},
             {'name': 'yasmin'},
             {'name': 'deepti'},
             {'name': 'hrithik'},
             {'name': 'vivek'}]

>>> df = pd.DataFrame(names)

>>> in_rolls_fn_gender(df, 'name')
            name    n_male  n_female  n_third_gender  prop_female  prop_male  prop_third_gender pred_gender  pred_prob
    0   gaurav   25625.0      47.0             0.0     0.001831   0.998169                0.0         NaN        NaN
    1    nabha       NaN       NaN             NaN          NaN        NaN                NaN      female   0.755028
    2   yasmin      58.0    6079.0             0.0     0.990549   0.009451                0.0         NaN        NaN
    3   deepti      35.0    5784.0             0.0     0.993985   0.006015                0.0         NaN        NaN
    4  hrithik       NaN       NaN             NaN          NaN        NaN                NaN        male   0.922181
    5    vivek  233622.0    1655.0             0.0     0.007034   0.992966                0.0         NaN        NaN

>>> help(in_rolls_fn_gender)
Help on method in_rolls_fn_gender in module naampy.in_rolls_fn:

in_rolls_fn_gender(df, namecol, state=None, year=None) method of builtins.type instance
    Appends additional columns from Female ratio data to the input DataFrame
    based on the first name.

    Removes extra space. Checks if the name is the Indian electoral rolls data.
    If it is, outputs data from that row.

        df (:obj:`DataFrame`): Pandas DataFrame containing the first name
        namecol (str or int): Column's name or location of the name in
        state (str): The state name of Indian electoral rolls data to be used.
            (default is None for all states)
        year (int): The year of Indian electoral rolls to be used.
            (default is None for all years)

        DataFrame: Pandas DataFrame with additional columns:-
            'n_female', 'n_male', 'n_third_gender',
            'prop_female', 'prop_male', 'prop_third_gender' by first name

# If you want to use model prediction use `predict_fn_gender` like below
from naampy import predict_fn_gender
input = [
     "tamannaah bhatia",

                    name pred_gender  pred_prob
0        rajinikanth        male   0.994747
1             harvin        male   0.840713
2        shyamsingha        male   0.956903
3             srihan        male   0.825542
4            thammam      female   0.564286
5           bahubali        male   0.901159
6     rajarajeshwari      female   0.942478
7             shobby        male   0.788314
8   tamannaah bhatia      female   0.971478
9            mehreen      female   0.659633
10             kiara      female   0.614125
11       shivathmika      female   0.743240
12           komalee      female   0.901051
13           nazriya      female   0.854167
14             nabha      female   0.755028
15           taapsee      female   0.665176
16         parineeti      female   0.813237
17           katrina      female   0.630126
18            ileana      female   0.640331
19        vishwaksen        male   0.992237
20       sampoornesh        male   0.940307
21           hrithik        male   0.922181
22            emraan        male   0.795963
23         rajkummar        male   0.845139
24           sharman        male   0.858538
25         ayushmann        male   0.964895
26            irrfan        male   0.837053
27           riteish        male   0.950755


When you first run in_rolls_fn_gender, it downloads data from Harvard Dataverse to the local folder. Next time you run the function, it searches for local data and if it finds it, it uses it. Use predict_fn_gender to get gender predictions based on first name.


Suriyan Laohaprapanon, Gaurav Sood, and Rajashekar Chintalapati


The package is released under the MIT License.