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

[1]:
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
[1]:
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.

[2]:
from dataprep.clean import clean_mu_nid
clean_mu_nid(df, column = "nid")
[2]:
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)

[3]:
clean_mu_nid(df, column = "nid", output_format="standard")
[3]:
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

[4]:
clean_mu_nid(df, column = "nid", output_format="compact")
[4]:
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

[5]:
clean_mu_nid(df, column = "nid", output_format="birthdate")
[5]:
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".

[6]:
clean_mu_nid(df, column="nid", inplace=True)
[6]:
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)

[7]:
clean_mu_nid(df, "nid", errors="coerce")
[7]:
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

[8]:
clean_mu_nid(df, "nid", errors="ignore")
[8]:
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.

[9]:
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

[10]:
validate_mu_nid(df["nid"])
[10]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: nid, dtype: bool

DataFrame + Specify Column

[11]:
validate_mu_nid(df, column="nid")
[11]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
Name: nid, dtype: bool

Only DataFrame

[12]:
validate_mu_nid(df)
[12]:
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
[ ]: