Malaysian National Registration Identity Card Numbers

Introduction

The function clean_my_nric() cleans a column containing Malaysian National Registration Identity Card Number (NRIC) strings, and standardizes them in a given format. The function validate_my_nric() validates either a single NRIC strings, a column of NRIC strings or a DataFrame of NRIC strings, returning True if the value is valid, and False otherwise.

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

  • compact: only number strings without any seperators or whitespace, like “770305021234”

  • standard: NRIC strings with proper whitespace in the proper places, like “770305-02-1234”

  • birthdate: return the registration date or the birth date, like “1977-03-05”.

  • birthplace: return a dict containing the birthplace of the person, like {‘state’: ‘Kedah’, ‘country’: ‘Malaysia’, ‘countries’: ‘Malaysia’}.

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_my_nric() and validate_my_nric().

An example dataset containing NRIC strings

[1]:
import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "nric": [
            "770305021234",
            "771305-02-1234",
            "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]:
nric address
0 770305021234 123 Pine Ave.
1 771305-02-1234 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_my_nric

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

[2]:
from dataprep.clean import clean_my_nric
clean_my_nric(df, column = "nric")
[2]:
nric address nric_clean
0 770305021234 123 Pine Ave. 770305-02-1234
1 771305-02-1234 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_my_nric(df, column = "nric", output_format="standard")
[3]:
nric address nric_clean
0 770305021234 123 Pine Ave. 770305-02-1234
1 771305-02-1234 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_my_nric(df, column = "nric", output_format="compact")
[4]:
nric address nric_clean
0 770305021234 123 Pine Ave. 770305021234
1 771305-02-1234 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

birthdate

[5]:
clean_my_nric(df, column = "nric", output_format="birthdate")
[5]:
nric address nric_clean
0 770305021234 123 Pine Ave. 1977-03-05
1 771305-02-1234 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

birthplace

[6]:
clean_my_nric(df, column = "nric", output_format="birthplace")
[6]:
nric address nric_clean
0 770305021234 123 Pine Ave. {'state': 'Kedah', 'country': 'Malaysia', 'cou...
1 771305-02-1234 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 NRIC strings is added with a title in the format "{original title}_clean".

[7]:
clean_my_nric(df, column="nric", inplace=True)
[7]:
nric_clean address
0 770305-02-1234 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)

[8]:
clean_my_nric(df, "nric", errors="coerce")
[8]:
nric address nric_clean
0 770305021234 123 Pine Ave. 770305-02-1234
1 771305-02-1234 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

[9]:
clean_my_nric(df, "nric", errors="ignore")
[9]:
nric address nric_clean
0 770305021234 123 Pine Ave. 770305-02-1234
1 771305-02-1234 main st 771305-02-1234
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_my_nric()

validate_my_nric() returns True when the input is a valid NRIC. Otherwise it returns False.

The input of validate_my_nric() 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_my_nric() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_my_nric() returns the validation result for the whole DataFrame.

[10]:
from dataprep.clean import validate_my_nric
print(validate_my_nric("770305021234"))
print(validate_my_nric("771305-02-1234"))
print(validate_my_nric("999 999 999"))
print(validate_my_nric("51824753556"))
print(validate_my_nric("004085616"))
print(validate_my_nric("hello"))
print(validate_my_nric(np.nan))
print(validate_my_nric("NULL"))
True
False
False
False
False
False
False
False

Series

[11]:
validate_my_nric(df["nric"])
[11]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: nric, dtype: bool

DataFrame + Specify Column

[12]:
validate_my_nric(df, column="nric")
[12]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: nric, dtype: bool

Only DataFrame

[13]:
validate_my_nric(df)
[13]:
nric 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
[ ]: