Mauritian National ID Numbers

Introduction

The function clean_mu_nid() cleans a column containing Mauritian national ID number (NID) strings, and standardizes them in a given format. The function validate_mu_nid() validates either a single NID strings, a column of NID strings or a DataFrame of NID strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “J2906201304089”

• standard: NID strings with proper whitespace in the proper places. Note that in the case of NID, the compact format is the same as the standard one.

• birthdate: split the date parts from the number and return the birth date, like “2020-06-29”.

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_mu_nid() and validate_mu_nid().

An example dataset containing NID strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "nid": [
            'J2906201304089',
            'J2906201304088',
            '7542011030',
            '7552A10004',
            '8019010008',
            "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

	nid	address
0	J2906201304089	123 Pine Ave.
1	J2906201304088	main st
2	7542011030	1234 west main heights 57033
3	7552A10004	apt 1 789 s maple rd manhattan
4	8019010008	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_mu_nid

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

from dataprep.clean import clean_mu_nid
clean_mu_nid(df, column = "nid")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	J2906201304089
1	J2906201304088	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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)

clean_mu_nid(df, column = "nid", output_format="standard")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	J2906201304089
1	J2906201304088	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

clean_mu_nid(df, column = "nid", output_format="compact")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	J2906201304089
1	J2906201304088	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

clean_mu_nid(df, column = "nid", output_format="birthdate")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	2020-06-29
1	J2906201304088	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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 NID strings is added with a title in the format "{original title}_clean".

clean_mu_nid(df, column="nid", inplace=True)

	nid_clean	address
0	J2906201304089	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)

clean_mu_nid(df, "nid", errors="coerce")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	J2906201304089
1	J2906201304088	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

clean_mu_nid(df, "nid", errors="ignore")

	nid	address	nid_clean
0	J2906201304089	123 Pine Ave.	J2906201304089
1	J2906201304088	main st	J2906201304088
2	7542011030	1234 west main heights 57033	7542011030
3	7552A10004	apt 1 789 s maple rd manhattan	7552A10004
4	8019010008	robie house, 789 north main street	8019010008
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_mu_nid()

validate_mu_nid() returns True when the input is a valid NID. Otherwise it returns False.

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

from dataprep.clean import validate_mu_nid
print(validate_mu_nid('J2906201304089'))
print(validate_mu_nid('J2906201304088'))
print(validate_mu_nid('7542011030'))
print(validate_mu_nid('7552A10004'))
print(validate_mu_nid('8019010008'))
print(validate_mu_nid("hello"))
print(validate_mu_nid(np.nan))
print(validate_mu_nid("NULL"))

True
False
False
False
False
False
False
False

Series

validate_mu_nid(df["nid"])

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

DataFrame + Specify Column

validate_mu_nid(df, column="nid")

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

Only DataFrame

validate_mu_nid(df)

	nid	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