Australian Business Numbers

Introduction

The function clean_au_abn() cleans a column containing Australian Business Number (ABN) strings, and standardizes them in a given format. The function validate_au_abn() validates either a single ABN strings, a column of ABN strings or a DataFrame of ABN strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “51824753556”

• standard: ABN strings with proper whitespace in the proper places, like “51 824 753 556”

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_au_abn() and validate_au_abn().

An example dataset containing ABN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "abn": [
            "83 914 571 673",
            "99 999 999 999",
            "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

	abn	address
0	83 914 571 673	123 Pine Ave.
1	99 999 999 999	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_au_abn

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

from dataprep.clean import clean_au_abn
clean_au_abn(df, column = "abn")

	abn	address	abn_clean
0	83 914 571 673	123 Pine Ave.	83 914 571 673
1	99 999 999 999	main st	NaN
2	51824753556	1234 west main heights 57033	51 824 753 556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51 824 753 556
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_au_abn(df, column = "abn", output_format="standard")

	abn	address	abn_clean
0	83 914 571 673	123 Pine Ave.	83 914 571 673
1	99 999 999 999	main st	NaN
2	51824753556	1234 west main heights 57033	51 824 753 556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51 824 753 556
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_au_abn(df, column = "abn", output_format="compact")

	abn	address	abn_clean
0	83 914 571 673	123 Pine Ave.	83914571673
1	99 999 999 999	main st	NaN
2	51824753556	1234 west main heights 57033	51824753556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51824753556
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 ABN strings is added with a title in the format "{original title}_clean".

clean_au_abn(df, column="abn", inplace=True)

	abn_clean	address
0	83 914 571 673	123 Pine Ave.
1	NaN	main st
2	51 824 753 556	1234 west main heights 57033
3	51 824 753 556	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_au_abn(df, "abn", errors="coerce")

	abn	address	abn_clean
0	83 914 571 673	123 Pine Ave.	83 914 571 673
1	99 999 999 999	main st	NaN
2	51824753556	1234 west main heights 57033	51 824 753 556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51 824 753 556
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_au_abn(df, "abn", errors="ignore")

	abn	address	abn_clean
0	83 914 571 673	123 Pine Ave.	83 914 571 673
1	99 999 999 999	main st	99 999 999 999
2	51824753556	1234 west main heights 57033	51 824 753 556
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_au_abn()

validate_au_abn() returns True when the input is a valid ABN. Otherwise it returns False.

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

from dataprep.clean import validate_au_abn
print(validate_au_abn("83 914 571 673"))
print(validate_au_abn("99 999 999 999"))
print(validate_au_abn("51824753556"))
print(validate_au_abn("51 824 753 556"))
print(validate_au_abn("hello"))
print(validate_au_abn(np.nan))
print(validate_au_abn("NULL"))

True
False
True
True
False
False
False

Series

validate_au_abn(df["abn"])

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

DataFrame + Specify Column

validate_au_abn(df, column="abn")

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

Only DataFrame

validate_au_abn(df)

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