Indian Digital Resident Personal Identity Numbers

Introduction

The function clean_in_aadhaar() cleans a column containing Indian digital resident personal identity number (Aadhaar) strings, and standardizes them in a given format. The function validate_in_aadhaar() validates either a single Aadhaar strings, a column of Aadhaar strings or a DataFrame of Aadhaar strings, returning True if the value is valid, and False otherwise.

Aadhaar strings can be converted to the following formats via the output_format parameter:

  • compact: only number strings without any seperators or whitespace, like “234123412346”

  • standard: Aadhaar strings with proper whitespace in the proper places, like “2341 2341 2346”

  • mask: mask the first 8 digits as per MeitY guidelines for securing identity information and Sensitive personal data, like “XXXX XXXX 2346”.

Invalid parsing is handled with the errors parameter:

  • coerce (default): invalid parsing will be set to NaN

  • ignore: invalid parsing will return the input

  • raise: invalid parsing will raise an exception

The following sections demonstrate the functionality of clean_in_aadhaar() and validate_in_aadhaar().

An example dataset containing Aadhaar strings

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "aadhaar": [
            "234123412346",
            "643343121",
            "999 999 999",
            "004085616",
            "002 724 334",
            "hello",
            np.nan,
            "NULL",
        ],
        "address": [
            "123 Pine Ave.",
            "main st",
            "1234 west main heights 57033",
            "apt 1 789 s maple rd manhattan",
            "robie house, 789 north main street",
            "1111 S Figueroa St, Los Angeles, CA 90015",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df

[1]:
aadhaar address
0 234123412346 123 Pine Ave.
1 643343121 main st
2 999 999 999 1234 west main heights 57033
3 004085616 apt 1 789 s maple rd manhattan
4 002 724 334 robie house, 789 north main street
5 hello 1111 S Figueroa St, Los Angeles, CA 90015
6 NaN (staples center) 1111 S Figueroa St, Los Angeles
7 NULL hello

1. Default clean_in_aadhaar

By default, clean_in_aadhaar will clean aadhaar strings and output them in the standard format with proper separators.

[2]:

from dataprep.clean import clean_in_aadhaar
clean_in_aadhaar(df, column = "aadhaar")

[2]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. 2341 2341 2346
1 643343121 main st NaN
2 999 999 999 1234 west main heights 57033 NaN
3 004085616 apt 1 789 s maple rd manhattan NaN
4 002 724 334 robie house, 789 north main street NaN
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 NaN
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

2. Output formats

This section demonstrates the output parameter.

standard (default)

[3]:

clean_in_aadhaar(df, column = "aadhaar", output_format="standard")

[3]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. 2341 2341 2346
1 643343121 main st NaN
2 999 999 999 1234 west main heights 57033 NaN
3 004085616 apt 1 789 s maple rd manhattan NaN
4 002 724 334 robie house, 789 north main street NaN
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 NaN
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

compact

[4]:

clean_in_aadhaar(df, column = "aadhaar", output_format="compact")

[4]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. 234123412346
1 643343121 main st NaN
2 999 999 999 1234 west main heights 57033 NaN
3 004085616 apt 1 789 s maple rd manhattan NaN
4 002 724 334 robie house, 789 north main street NaN
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 NaN
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

mask

[5]:

clean_in_aadhaar(df, column = "aadhaar", output_format="mask")

[5]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. XXXX XXXX 2346
1 643343121 main st NaN
2 999 999 999 1234 west main heights 57033 NaN
3 004085616 apt 1 789 s maple rd manhattan NaN
4 002 724 334 robie house, 789 north main street NaN
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 NaN
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

3. inplace parameter

This deletes the given column from the returned DataFrame. A new column containing cleaned Aadhaar strings is added with a title in the format "{original title}_clean".

[6]:

clean_in_aadhaar(df, column="aadhaar", inplace=True)

[6]:
aadhaar_clean address
0 2341 2341 2346 123 Pine Ave.
1 NaN main st
2 NaN 1234 west main heights 57033
3 NaN apt 1 789 s maple rd manhattan
4 NaN robie house, 789 north main street
5 NaN 1111 S Figueroa St, Los Angeles, CA 90015
6 NaN (staples center) 1111 S Figueroa St, Los Angeles
7 NaN hello

4. errors parameter

coerce (default)

[7]:

clean_in_aadhaar(df, "aadhaar", errors="coerce")

[7]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. 2341 2341 2346
1 643343121 main st NaN
2 999 999 999 1234 west main heights 57033 NaN
3 004085616 apt 1 789 s maple rd manhattan NaN
4 002 724 334 robie house, 789 north main street NaN
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 NaN
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

ignore

[8]:

clean_in_aadhaar(df, "aadhaar", errors="ignore")

[8]:
aadhaar address aadhaar_clean
0 234123412346 123 Pine Ave. 2341 2341 2346
1 643343121 main st 643343121
2 999 999 999 1234 west main heights 57033 999 999 999
3 004085616 apt 1 789 s maple rd manhattan 004085616
4 002 724 334 robie house, 789 north main street 002 724 334
5 hello 1111 S Figueroa St, Los Angeles, CA 90015 hello
6 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
7 NULL hello NaN

4. validate_in_aadhaar()

validate_in_aadhaar() returns True when the input is a valid Aadhaar. Otherwise it returns False.

The input of validate_in_aadhaar() can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.

When the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn’t need to specify a column name to be validated.

When the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, validate_in_aadhaar() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_in_aadhaar() returns the validation result for the whole DataFrame.

[9]:

from dataprep.clean import validate_in_aadhaar
print(validate_in_aadhaar("234123412346"))
print(validate_in_aadhaar("643343121"))
print(validate_in_aadhaar("999 999 999"))
print(validate_in_aadhaar("51824753556"))
print(validate_in_aadhaar("004085616"))
print(validate_in_aadhaar("hello"))
print(validate_in_aadhaar(np.nan))
print(validate_in_aadhaar("NULL"))

True
False
False
False
False
False
False
False

Series

[10]:

validate_in_aadhaar(df["aadhaar"])

[10]:

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: aadhaar, dtype: bool

DataFrame + Specify Column

[11]:

validate_in_aadhaar(df, column="aadhaar")

[11]:

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: aadhaar, dtype: bool

Only DataFrame

[12]:

validate_in_aadhaar(df)

[12]:
aadhaar address
0 True False
1 False False
2 False False
3 False False
4 False False
5 False False
6 False False
7 False False 
[ ]: