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

[1]:
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
[1]:
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.

[2]:
from dataprep.clean import clean_cn_ric
clean_cn_ric(df, column = "ric")
[2]:
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)

[3]:
clean_cn_ric(df, column = "ric", output_format="standard")
[3]:
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

[4]:
clean_cn_ric(df, column = "ric", output_format="compact")
[4]:
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

[5]:
clean_cn_ric(df, column = "ric", output_format="birthdate")
[5]:
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

[6]:
clean_cn_ric(df, column = "ric", output_format="birthplace")
[6]:
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".

[7]:
clean_cn_ric(df, column="ric", inplace=True)
[7]:
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)

[8]:
clean_cn_ric(df, "ric", errors="coerce")
[8]:
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

[9]:
clean_cn_ric(df, "ric", errors="ignore")
[9]:
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.

[10]:
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

[11]:
validate_cn_ric(df["ric"])
[11]:
0     True
1     True
2    False
3    False
4    False
5    False
6    False
Name: ric, dtype: bool

DataFrame + Specify Column

[12]:
validate_cn_ric(df, column="ric")
[12]:
0     True
1     True
2    False
3    False
4    False
5    False
6    False
Name: ric, dtype: bool

Only DataFrame

[13]:
validate_cn_ric(df)
[13]:
ric address
0 True False
1 True False
2 False False
3 False False
4 False False
5 False False
6 False False
[ ]: