Romanian Numerical Personal Codes

Introduction

The function clean_ro_cnp() cleans a column containing Romanian Numerical Personal Code (CNP) strings, and standardizes them in a given format. The function validate_ro_cnp() validates either a single CNP strings, a column of CNP strings or a DataFrame of CNP strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “1630615123457”

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

• birthdate: split the date parts from the number and return the birth date, like “1875-03-16”.

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_ro_cnp() and validate_ro_cnp().

An example dataset containing CNP strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cnp": [
            "1630615123457",
            "0800101221142",
            '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

	cnp	address
0	1630615123457	123 Pine Ave.
1	0800101221142	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_ro_cnp

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

from dataprep.clean import clean_ro_cnp
clean_ro_cnp(df, column = "cnp")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1630615123457
1	0800101221142	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)

clean_ro_cnp(df, column = "cnp", output_format="standard")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1630615123457
1	0800101221142	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

clean_ro_cnp(df, column = "cnp", output_format="compact")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1630615123457
1	0800101221142	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

clean_ro_cnp(df, column = "cnp", output_format="birthdate")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1963-06-15
1	0800101221142	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 CNP strings is added with a title in the format "{original title}_clean".

clean_ro_cnp(df, column="cnp", inplace=True)

	cnp_clean	address
0	1630615123457	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_ro_cnp(df, "cnp", errors="coerce")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1630615123457
1	0800101221142	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

clean_ro_cnp(df, "cnp", errors="ignore")

	cnp	address	cnp_clean
0	1630615123457	123 Pine Ave.	1630615123457
1	0800101221142	main st	0800101221142
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_ro_cnp()

validate_ro_cnp() returns True when the input is a valid CNP. Otherwise it returns False.

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

from dataprep.clean import validate_ro_cnp
print(validate_ro_cnp('1630615123457'))
print(validate_ro_cnp('0800101221142'))
print(validate_ro_cnp('7542011030'))
print(validate_ro_cnp('7552A10004'))
print(validate_ro_cnp('8019010008'))
print(validate_ro_cnp("hello"))
print(validate_ro_cnp(np.nan))
print(validate_ro_cnp("NULL"))

True
False
False
False
False
False
False
False

Series

validate_ro_cnp(df["cnp"])

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

DataFrame + Specify Column

validate_ro_cnp(df, column="cnp")

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

Only DataFrame

validate_ro_cnp(df)

	cnp	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