indicate 0.0.1rc1

Transliterations to/from Indian languages

indicate: transliterate indic languages to english

Transliterations to/from Indian languages are still generally low quality. One problem is access to data. Another is that there is no standard transliteration.

For Hindi–English, we build novel dataset for names using the ESPNcricinfo. For instance, see here for Hindi version of the English scorecard.

We also create a dataset from election affidavits

We also exploit the Google Dakshina dataset.

To overcome the fact that there isn’t one standard way of transliteration, we provide k-best transliterations.

Install

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

pip install indicate

General API

Examples

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, 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 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

Data

Evaluation

Authors

Rajashekar Chintalapati 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.