Australian Company Numbers

Introduction

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

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

• compact: only number strings without any seperators or whitespace, like “004085616”

• standard: ACN strings with proper whitespace in the proper places, like “004 085 616”

• abn: convert the number to an Australian Business Number (ABN) in compact format, like “53004085616”.

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_acn() and validate_au_acn().

An example dataset containing ACN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "acn": [
            "004 085 616",
            "010 499 966",
            "999 999 999",
            "004085616",
            "002 724 334",
            "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

	acn	address
0	004 085 616	123 Pine Ave.
1	010 499 966	main st
2	999 999 999	1234 west main heights 57033
3	004085616	apt 1 789 s maple rd manhattan
4	002 724 334	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_au_acn

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

from dataprep.clean import clean_au_acn
clean_au_acn(df, column = "acn")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	004 085 616
1	010 499 966	main st	010 499 966
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	004 085 616
4	002 724 334	robie house, 789 north main street	002 724 334
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_au_acn(df, column = "acn", output_format="standard")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	004 085 616
1	010 499 966	main st	010 499 966
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	004 085 616
4	002 724 334	robie house, 789 north main street	002 724 334
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_au_acn(df, column = "acn", output_format="compact")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	004085616
1	010 499 966	main st	010499966
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	004085616
4	002 724 334	robie house, 789 north main street	002724334
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

abn

clean_au_acn(df, column = "acn", output_format="abn")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	53004085616
1	010 499 966	main st	25010499966
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	53004085616
4	002 724 334	robie house, 789 north main street	43002724334
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 ACN strings is added with a title in the format "{original title}_clean".

clean_au_acn(df, column="acn", inplace=True)

	acn_clean	address
0	004 085 616	123 Pine Ave.
1	010 499 966	main st
2	NaN	1234 west main heights 57033
3	004 085 616	apt 1 789 s maple rd manhattan
4	002 724 334	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_au_acn(df, "acn", errors="coerce")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	004 085 616
1	010 499 966	main st	010 499 966
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	004 085 616
4	002 724 334	robie house, 789 north main street	002 724 334
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_au_acn(df, "acn", errors="ignore")

	acn	address	acn_clean
0	004 085 616	123 Pine Ave.	004 085 616
1	010 499 966	main st	010 499 966
2	999 999 999	1234 west main heights 57033	999 999 999
3	004085616	apt 1 789 s maple rd manhattan	004 085 616
4	002 724 334	robie house, 789 north main street	002 724 334
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_au_acn()

validate_au_acn() returns True when the input is a valid ACN. Otherwise it returns False.

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

from dataprep.clean import validate_au_acn
print(validate_au_acn("004 085 616"))
print(validate_au_acn("010 499 966"))
print(validate_au_acn("999 999 999"))
print(validate_au_acn("51824753556"))
print(validate_au_acn("004085616"))
print(validate_au_acn("hello"))
print(validate_au_acn(np.nan))
print(validate_au_acn("NULL"))

True
True
False
False
True
False
False
False

Series

validate_au_acn(df["acn"])

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

DataFrame + Specify Column

validate_au_acn(df, column="acn")

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

Only DataFrame

validate_au_acn(df)

	acn	address
0	True	False
1	True	False
2	False	False
3	True	False
4	True	False
5	False	False
6	False	False
7	False	False