Japanese Corporate Numbers

Introduction

The function clean_jp_cn() cleans a column containing Japanese Corporate Number (CN) strings, and standardizes them in a given format. The function validate_jp_cn() validates either a single CN strings, a column of CN strings or a DataFrame of CN strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “5835678256246”

• standard: CN strings with proper whitespace in the proper places, like “5-8356-7825-6246”

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_jp_cn() and validate_jp_cn().

An example dataset containing CN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cn": [
            "5835678256246",
            "2-8356-7825-6246",
            "51824753556",
            "51 824 753 556",
            "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

	cn	address
0	5835678256246	123 Pine Ave.
1	2-8356-7825-6246	main st
2	51824753556	1234 west main heights 57033
3	51 824 753 556	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_jp_cn

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

from dataprep.clean import clean_jp_cn
clean_jp_cn(df, column = "cn")

	cn	address	cn_clean
0	5835678256246	123 Pine Ave.	5-8356-7825-6246
1	2-8356-7825-6246	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_jp_cn(df, column = "cn", output_format="standard")

	cn	address	cn_clean
0	5835678256246	123 Pine Ave.	5-8356-7825-6246
1	2-8356-7825-6246	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_jp_cn(df, column = "cn", output_format="compact")

	cn	address	cn_clean
0	5835678256246	123 Pine Ave.	5835678256246
1	2-8356-7825-6246	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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 CN strings is added with a title in the format "{original title}_clean".

clean_jp_cn(df, column="cn", inplace=True)

	cn_clean	address
0	5-8356-7825-6246	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	(staples center) 1111 S Figueroa St, Los Angeles
6	NaN	hello

4. errors parameter

coerce (default)

clean_jp_cn(df, "cn", errors="coerce")

	cn	address	cn_clean
0	5835678256246	123 Pine Ave.	5-8356-7825-6246
1	2-8356-7825-6246	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_jp_cn(df, "cn", errors="ignore")

	cn	address	cn_clean
0	5835678256246	123 Pine Ave.	5-8356-7825-6246
1	2-8356-7825-6246	main st	2-8356-7825-6246
2	51824753556	1234 west main heights 57033	51824753556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51 824 753 556
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_jp_cn()

validate_jp_cn() returns True when the input is a valid CN. Otherwise it returns False.

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

from dataprep.clean import validate_jp_cn
print(validate_jp_cn("5835678256246"))
print(validate_jp_cn("2-8356-7825-6246"))
print(validate_jp_cn("51824753556"))
print(validate_jp_cn("51 824 753 556"))
print(validate_jp_cn("hello"))
print(validate_jp_cn(np.nan))
print(validate_jp_cn("NULL"))

True
False
False
False
False
False
False

Series

validate_jp_cn(df["cn"])

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

DataFrame + Specify Column

validate_jp_cn(df, column="cn")

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

Only DataFrame

validate_jp_cn(df)

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