Lithuanian Personal Numbers¶

Introduction¶

The function clean_lt_asmens() cleans a column containing Lithuanian personal number (Asmens koda) strings, and standardizes them in a given format. The function validate_lt_asmens() validates either a single Asmens koda strings, a column of Asmens koda strings or a DataFrame of Asmens koda strings, returning True if the value is valid, and False otherwise.

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

compact: only number strings without any seperators or whitespace, like “33309240064”
standard: Asmens koda strings with proper whitespace in the proper places. Note that in the case of Asmens koda, the compact format is the same as the standard one.
birthdate: split the date parts from the number and return the birth date, like “1933-09-24”.

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_lt_asmens() and validate_lt_asmens().

An example dataset containing Asmens koda strings¶

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "asmens": [
            '33309240064',
            '33309240164',
            '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]:

	asmens	address
0	33309240064	123 Pine Ave.
1	33309240164	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_lt_asmens`¶

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

[2]:

from dataprep.clean import clean_lt_asmens
clean_lt_asmens(df, column = "asmens")

[2]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	33309240064
1	33309240164	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_lt_asmens(df, column = "asmens", output_format="standard")

[3]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	33309240064
1	33309240164	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_lt_asmens(df, column = "asmens", output_format="compact")

[4]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	33309240064
1	33309240164	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_lt_asmens(df, column = "asmens", output_format="birthdate")

[5]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	1933-09-24
1	33309240164	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 Asmens koda strings is added with a title in the format "{original title}_clean".

[6]:

clean_lt_asmens(df, column="asmens", inplace=True)

[6]:

	asmens_clean	address
0	33309240064	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_lt_asmens(df, "asmens", errors="coerce")

[7]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	33309240064
1	33309240164	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_lt_asmens(df, "asmens", errors="ignore")

[8]:

	asmens	address	asmens_clean
0	33309240064	123 Pine Ave.	33309240064
1	33309240164	main st	33309240164
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_lt_asmens()`¶

validate_lt_asmens() returns True when the input is a valid Asmens koda. Otherwise it returns False.

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

[9]:

from dataprep.clean import validate_lt_asmens
print(validate_lt_asmens('33309240064'))
print(validate_lt_asmens('33309240164'))
print(validate_lt_asmens('7542011030'))
print(validate_lt_asmens('7552A10004'))
print(validate_lt_asmens('8019010008'))
print(validate_lt_asmens("hello"))
print(validate_lt_asmens(np.nan))
print(validate_lt_asmens("NULL"))

True
False
False
False
False
False
False
False

Series¶

[10]:

validate_lt_asmens(df["asmens"])

[10]:

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

DataFrame + Specify Column¶

[11]:

validate_lt_asmens(df, column="asmens")

[11]:

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

Only DataFrame¶

[12]:

validate_lt_asmens(df)

[12]:

	asmens	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

[ ]:

Lithuanian Personal Numbers¶

Introduction¶

An example dataset containing Asmens koda strings¶

1. Default clean_lt_asmens¶

2. Output formats¶

standard (default)¶

compact¶

birthdate¶

3. inplace parameter¶

4. errors parameter¶

coerce (default)¶

ignore¶

4. validate_lt_asmens()¶