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

[1]:
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
[1]:
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.

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

[3]:
clean_au_acn(df, column = "acn", output_format="standard")
[3]:
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

[4]:
clean_au_acn(df, column = "acn", output_format="compact")
[4]:
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

[5]:
clean_au_acn(df, column = "acn", output_format="abn")
[5]:
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".

[6]:
clean_au_acn(df, column="acn", inplace=True)
[6]:
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)

[7]:
clean_au_acn(df, "acn", errors="coerce")
[7]:
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

[8]:
clean_au_acn(df, "acn", errors="ignore")
[8]:
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.

[9]:
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

[10]:
validate_au_acn(df["acn"])
[10]:
0     True
1     True
2    False
3     True
4     True
5    False
6    False
7    False
Name: acn, dtype: bool

DataFrame + Specify Column

[11]:
validate_au_acn(df, column="acn")
[11]:
0     True
1     True
2    False
3     True
4     True
5    False
6    False
7    False
Name: acn, dtype: bool

Only DataFrame

[12]:
validate_au_acn(df)
[12]:
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
[ ]: