New Zealand Bank Account Numbers

Introduction

The function clean_nz_bankaccount() cleans a column containing New Zealand bank account number (bankaccount) strings, and standardizes them in a given format. The function validate_nz_bankaccount() validates either a single bankaccount strings, a column of bankaccount strings or a DataFrame of bankaccount strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “0102420100194000”

• standard: bankaccount strings with proper whitespace in the proper places, like “01-0242-0100194-000”

• info: return a dictionary of data about the supplied number, like {‘bank’: ‘ANZ Bank New Zealand’, ‘branch’: ‘ANZ Retail’}. This typically returns the name of the bank and branch and a BIC if it is valid.

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_nz_bankaccount() and validate_nz_bankaccount().

An example dataset containing bankaccount strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "bankaccount": [
            "0102420100194000",
            "01-0242-0100195-00",
            "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

	bankaccount	address
0	0102420100194000	123 Pine Ave.
1	01-0242-0100195-00	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_nz_bankaccount

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

from dataprep.clean import clean_nz_bankaccount
clean_nz_bankaccount(df, column = "bankaccount")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	01-0242-0100194-000
1	01-0242-0100195-00	main st	NaN
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
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_nz_bankaccount(df, column = "bankaccount", output_format="standard")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	01-0242-0100194-000
1	01-0242-0100195-00	main st	NaN
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
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_nz_bankaccount(df, column = "bankaccount", output_format="compact")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	0102420100194000
1	01-0242-0100195-00	main st	NaN
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
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

info

clean_nz_bankaccount(df, column = "bankaccount", output_format="info")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	{'bank': 'ANZ Bank New Zealand', 'branch': 'AN...
1	01-0242-0100195-00	main st	NaN
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
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 bankaccount strings is added with a title in the format "{original title}_clean".

clean_nz_bankaccount(df, column="bankaccount", inplace=True)

	bankaccount_clean	address
0	01-0242-0100194-000	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	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_nz_bankaccount(df, "bankaccount", errors="coerce")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	01-0242-0100194-000
1	01-0242-0100195-00	main st	NaN
2	999 999 999	1234 west main heights 57033	NaN
3	004085616	apt 1 789 s maple rd manhattan	NaN
4	002 724 334	robie house, 789 north main street	NaN
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_nz_bankaccount(df, "bankaccount", errors="ignore")

	bankaccount	address	bankaccount_clean
0	0102420100194000	123 Pine Ave.	01-0242-0100194-000
1	01-0242-0100195-00	main st	01-0242-0100195-00
2	999 999 999	1234 west main heights 57033	999 999 999
3	004085616	apt 1 789 s maple rd manhattan	004085616
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_nz_bankaccount()

validate_nz_bankaccount() returns True when the input is a valid bankaccount. Otherwise it returns False.

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

from dataprep.clean import validate_nz_bankaccount
print(validate_nz_bankaccount("0102420100194000"))
print(validate_nz_bankaccount("01-0242-0100195-00"))
print(validate_nz_bankaccount("999 999 999"))
print(validate_nz_bankaccount("51824753556"))
print(validate_nz_bankaccount("004085616"))
print(validate_nz_bankaccount("hello"))
print(validate_nz_bankaccount(np.nan))
print(validate_nz_bankaccount("NULL"))

True
False
False
False
False
False
False
False

Series

validate_nz_bankaccount(df["bankaccount"])

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

DataFrame + Specify Column

validate_nz_bankaccount(df, column="bankaccount")

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

Only DataFrame

validate_nz_bankaccount(df)

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