Norwegian Birth Numbers¶

Introduction¶

The function clean_no_fodselsnummer() cleans a column containing Norwegian birth number (fodselsnummer) strings, and standardizes them in a given format. The function validate_no_fodselsnummer() validates either a single fodselsnummer strings, a column of fodselsnummer strings or a DataFrame of fodselsnummer strings, returning True if the value is valid, and False otherwise.

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

compact: only number strings without any seperators or whitespace, like “15108695088”
standard: fodselsnummer strings with proper whitespace in the proper places, like “151086 95088”
birthdate: get the person’s birthdate, like “1986-10-15”.
gender: get the person’s birth gender (‘M’ or ‘F’).

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_no_fodselsnummer() and validate_no_fodselsnummer().

An example dataset containing fodselsnummer strings¶

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "fodselsnummer": [
            '15108695088',
            '15108695077',
            "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]:

	fodselsnummer	address
0	15108695088	123 Pine Ave.
1	15108695077	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_no_fodselsnummer`¶

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

[2]:

from dataprep.clean import clean_no_fodselsnummer
clean_no_fodselsnummer(df, column = "fodselsnummer")

[2]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	151086 95088
1	15108695077	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_no_fodselsnummer(df, column = "fodselsnummer", output_format="standard")

[3]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	151086 95088
1	15108695077	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_no_fodselsnummer(df, column = "fodselsnummer", output_format="compact")

[4]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	15108695088
1	15108695077	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_no_fodselsnummer(df, column = "fodselsnummer", output_format="birthdate")

[5]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	1986-10-15
1	15108695077	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

`gender`¶

[6]:

clean_no_fodselsnummer(df, column = "fodselsnummer", output_format="gender")

[6]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	F
1	15108695077	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 fodselsnummer strings is added with a title in the format "{original title}_clean".

[7]:

clean_no_fodselsnummer(df, column="fodselsnummer", inplace=True)

[7]:

	fodselsnummer_clean	address
0	151086 95088	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_no_fodselsnummer(df, "fodselsnummer", errors="coerce")

[8]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	151086 95088
1	15108695077	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_no_fodselsnummer(df, "fodselsnummer", errors="ignore")

[9]:

	fodselsnummer	address	fodselsnummer_clean
0	15108695088	123 Pine Ave.	151086 95088
1	15108695077	main st	15108695077
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_no_fodselsnummer()`¶

validate_no_fodselsnummer() returns True when the input is a valid fodselsnummer. Otherwise it returns False.

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

[10]:

from dataprep.clean import validate_no_fodselsnummer
print(validate_no_fodselsnummer("15108695088"))
print(validate_no_fodselsnummer("15108695077"))
print(validate_no_fodselsnummer("999 999 999"))
print(validate_no_fodselsnummer("51824753556"))
print(validate_no_fodselsnummer("004085616"))
print(validate_no_fodselsnummer("hello"))
print(validate_no_fodselsnummer(np.nan))
print(validate_no_fodselsnummer("NULL"))

True
False
False
False
False
False
False
False

Series¶

[11]:

validate_no_fodselsnummer(df["fodselsnummer"])

[11]:

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

DataFrame + Specify Column¶

[12]:

validate_no_fodselsnummer(df, column="fodselsnummer")

[12]:

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

Only DataFrame¶

[13]:

validate_no_fodselsnummer(df)

[13]:

	fodselsnummer	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

[ ]:

Norwegian Birth Numbers¶

Introduction¶

An example dataset containing fodselsnummer strings¶

1. Default clean_no_fodselsnummer¶

2. Output formats¶

standard (default)¶

compact¶

birthdate¶

gender¶

3. inplace parameter¶

4. errors parameter¶

coerce (default)¶

ignore¶

4. validate_no_fodselsnummer()¶