Chinese Resident Identity Card Numbers

Introduction

The function clean_cn_ric() cleans a column containing Chinese Resident Identity Card Number (RIC) strings, and standardizes them in a given format. The function validate_cn_ric() validates either a single RIC strings, a column of RIC strings or a DataFrame of RIC strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “360426199101010071”

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

• birthdate: split the date parts from the number and return the birth date

• birthplace: use the number to look up the place of birth of the person

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_cn_ric() and validate_cn_ric().

An example dataset containing RIC strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "ric": [
            '360426199101010071',
            '230306196304054513',
            '230307196304054513', # invalid
            '110223790813697', # not a RIC
            "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",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df

	ric	address
0	360426199101010071	123 Pine Ave.
1	230306196304054513	main st
2	230307196304054513	1234 west main heights 57033
3	110223790813697	apt 1 789 s maple rd manhattan
4	hello	robie house, 789 north main street
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles
6	NULL	hello

1. Default clean_cn_ric

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

from dataprep.clean import clean_cn_ric
clean_cn_ric(df, column = "ric")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	360426199101010071
1	230306196304054513	main st	230306196304054513
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

2. Output formats

This section demonstrates the output parameter.

standard (default)

clean_cn_ric(df, column = "ric", output_format="standard")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	360426199101010071
1	230306196304054513	main st	230306196304054513
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

compact

clean_cn_ric(df, column = "ric", output_format="compact")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	360426199101010071
1	230306196304054513	main st	230306196304054513
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

birthdate

clean_cn_ric(df, column = "ric", output_format="birthdate")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	1991-01-01
1	230306196304054513	main st	1963-04-05
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

birthplace

clean_cn_ric(df, column = "ric", output_format="birthplace")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	{'county': '德安县', 'prefecture': '九江市', 'provin...
1	230306196304054513	main st	{'county': '城子河区', 'prefecture': '鸡西市', 'provi...
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

3. inplace parameter

This deletes the given column from the returned DataFrame. A new column containing cleaned RIC strings is added with a title in the format "{original title}_clean".

clean_cn_ric(df, column="ric", inplace=True)

	ric_clean	address
0	360426199101010071	123 Pine Ave.
1	230306196304054513	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	(staples center) 1111 S Figueroa St, Los Angeles
6	NaN	hello

4. errors parameter

coerce (default)

clean_cn_ric(df, "ric", errors="coerce")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	360426199101010071
1	230306196304054513	main st	230306196304054513
2	230307196304054513	1234 west main heights 57033	NaN
3	110223790813697	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

ignore

clean_cn_ric(df, "ric", errors="ignore")

	ric	address	ric_clean
0	360426199101010071	123 Pine Ave.	360426199101010071
1	230306196304054513	main st	230306196304054513
2	230307196304054513	1234 west main heights 57033	230307196304054513
3	110223790813697	apt 1 789 s maple rd manhattan	110223790813697
4	hello	robie house, 789 north main street	hello
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

4. validate_cn_ric()

validate_cn_ric() returns True when the input is a valid RIC. Otherwise it returns False.

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

from dataprep.clean import validate_cn_ric
print(validate_cn_ric("230306196304054513"))
print(validate_cn_ric("1234567"))
print(validate_cn_ric("360426199101010071"))
print(validate_cn_ric("360436199101010071")) # change a bit and become invalid
print(validate_cn_ric("hello"))
print(validate_cn_ric(np.nan))
print(validate_cn_ric("NULL"))

True
False
True
False
False
False
False

Series

validate_cn_ric(df["ric"])

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

DataFrame + Specify Column

validate_cn_ric(df, column="ric")

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

Only DataFrame

validate_cn_ric(df)

	ric	address
0	True	False
1	True	False
2	False	False
3	False	False
4	False	False
5	False	False
6	False	False