Dutch Student Identification Numbers

Introduction

The function clean_nl_onderwijsnummer() cleans a column containing Onderwijsnummer (the Dutch student identification number) strings, and standardizes them in a given format. The function validate_nl_onderwijsnummer() validates either a single Onderwijsnummer strings, a column of Onderwijsnummer strings or a DataFrame of Onderwijsnummer strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “101222331”

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

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_nl_onderwijsnummer() and validate_nl_onderwijsnummer().

An example dataset containing Onderwijsnummer strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "vat": [
            '1012.22.331',
            '2112.22.337',
            'BE 428759497',
            'BE431150351',
            "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

	vat	address
0	1012.22.331	123 Pine Ave.
1	2112.22.337	main st
2	BE 428759497	1234 west main heights 57033
3	BE431150351	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_nl_onderwijsnummer

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

from dataprep.clean import clean_nl_onderwijsnummer
clean_nl_onderwijsnummer(df, column = "vat")

	vat	address	vat_clean
0	1012.22.331	123 Pine Ave.	101222331
1	2112.22.337	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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)

clean_nl_onderwijsnummer(df, column = "vat", output_format="standard")

	vat	address	vat_clean
0	1012.22.331	123 Pine Ave.	101222331
1	2112.22.337	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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

clean_nl_onderwijsnummer(df, column = "vat", output_format="compact")

	vat	address	vat_clean
0	1012.22.331	123 Pine Ave.	101222331
1	2112.22.337	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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 Onderwijsnummer strings is added with a title in the format "{original title}_clean".

clean_nl_onderwijsnummer(df, column="vat", inplace=True)

	vat_clean	address
0	101222331	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_nl_onderwijsnummer(df, "vat", errors="coerce")

	vat	address	vat_clean
0	1012.22.331	123 Pine Ave.	101222331
1	2112.22.337	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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

clean_nl_onderwijsnummer(df, "vat", errors="ignore")

	vat	address	vat_clean
0	1012.22.331	123 Pine Ave.	101222331
1	2112.22.337	main st	2112.22.337
2	BE 428759497	1234 west main heights 57033	BE 428759497
3	BE431150351	apt 1 789 s maple rd manhattan	BE431150351
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_nl_onderwijsnummer()

validate_nl_onderwijsnummer() returns True when the input is a valid Onderwijsnummer. Otherwise it returns False.

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

from dataprep.clean import validate_nl_onderwijsnummer
print(validate_nl_onderwijsnummer("1012.22.331"))
print(validate_nl_onderwijsnummer("2112.22.337"))
print(validate_nl_onderwijsnummer('BE 428759497'))
print(validate_nl_onderwijsnummer('BE431150351'))
print(validate_nl_onderwijsnummer("004085616"))
print(validate_nl_onderwijsnummer("hello"))
print(validate_nl_onderwijsnummer(np.nan))
print(validate_nl_onderwijsnummer("NULL"))

True
False
False
False
False
False
False
False

Series

validate_nl_onderwijsnummer(df["vat"])

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

DataFrame + Specify Column

validate_nl_onderwijsnummer(df, column="vat")

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

Only DataFrame

validate_nl_onderwijsnummer(df)

	vat	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