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

[1]:

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

[1]:
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.

[2]:

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

[2]:
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)

[3]:

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

[3]:
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

[4]:

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

[4]:
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".

[5]:

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

[5]:
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)

[6]:

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

[6]:
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

[7]:

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

[7]:
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.

[8]:

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

[9]:

validate_nl_onderwijsnummer(df["vat"])

[9]:

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

DataFrame + Specify Column

[10]:

validate_nl_onderwijsnummer(df, column="vat")

[10]:

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

Only DataFrame

[11]:

validate_nl_onderwijsnummer(df)

[11]:
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 
[ ]: