Infer Caste from Indian Names
Project description
Using data on more than 420M Indians from the Socio-Economic Caste Census (parsed data here), we estimate the proportion lower-caste for a particular last name, year, and state.
Why?
We provide this package so that people can find ways to assess, highlight, and fight unfairness.
How is the underlying data produced?
We split name into first name and last name and then aggregated per state, year.
This is used to provide the base prediction.
Base Classifier
We start by providing a base model for last_name that gives the Bayes optimal solution providing the proportion of SC, ST, and Other with that last name. We also provide a series of base models where the state of residence is known.
Installation
We strongly recommend installing outkast inside a Python virtual environment (see venv documentation)
pip install outkast
Usage
usage: secc_caste [-h] -l LAST_NAME [-s {arunachal pradesh,assam,bihar,chhattisgarh,gujarat,haryana,kerala,madhya pradesh,maharashtra,mizoram,odisha,nagaland,punjab,rajasthan,sikkim,tamilnadu,uttar pradesh,uttarakhand,west bengal}] [-y YEAR] [-o OUTPUT] input Appends SECC 2011 data columns for sc, st, and other by last name positional arguments: input Input file optional arguments: -h, --help show this help message and exit -l LAST_NAME, --last-name LAST_NAME Name or index location of column contains the last name -s {arunachal pradesh,assam,bihar,chhattisgarh,gujarat,haryana,kerala,madhya pradesh,maharashtra,mizoram,odisha,nagaland,punjab,rajasthan,sikkim,tamilnadu,uttar pradesh,uttarakhand,west bengal}, --state {arunachal pradesh,assam,bihar,chhattisgarh,gujarat,haryana,kerala,madhya pradesh,maharashtra,mizoram,odisha,nagaland,punjab,rajasthan,sikkim,tamilnadu,uttar pradesh,uttarakhand,west bengal} State name of SECC data (default=all) -y YEAR, --year YEAR Birth year in SECC data (default=all) -o OUTPUT, --output OUTPUT Output file with SECC data columns
Using outkast
>>> import pandas as pd >>> from outkast import secc_caste >>> >>> names = [{'name': 'patel'}, ... {'name': 'kohli'}, ... {'name': 'lal'}, ... {'name': 'agarwal'}] >>> >>> df = pd.DataFrame(names) >>> >>> secc_caste(df, 'name') name n_sc n_st n_other prop_sc prop_st prop_other 0 patel 17043 336909 1894248 0.007581 0.149857 0.842562 1 kohli 468 57 552 0.434540 0.052925 0.512535 2 lal 2111632 725713 3943494 0.311412 0.107024 0.581564 3 agarwal 117 36 13125 0.008812 0.002711 0.988477 >>> >>> help(secc_caste) Help on method secc_caste in module outkast.secc_caste_ln: secc_caste(df, namecol, state=None, year=None) method of builtins.type instance Appends additional columns from SECC data to the input DataFrame based on the last name. Removes extra space. Checks if the name is the SECC data. If it is, outputs data from that row. 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. state (str): The state name of SECC data to be used. (default is None for all states) year (int): The year of SECC data to be used. (default is None for all years) Returns: DataFrame: Pandas DataFrame with additional columns:- 'n_sc', 'n_st', 'n_other', 'prop_sc', 'prop_st', 'prop_other' by last name
License
The package is released under the MIT License.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for outkast-0.1.0-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c2aa4e2d51e0b64875cbae0546efd18eb09dcc922a6da4dd015ba1a63d55bbca |
|
MD5 | 5f892965d232a5549406451b4d850ac7 |
|
BLAKE2b-256 | 3da79980bbb70a2fb5c343655065394e42f0a77e0f4851432c935184d4fdbb62 |