Belgian IBAN Numbers

Introduction

The function clean_be_iban() cleans a column containing Belgian IBAN (International Bank Account Number) strings, and standardizes them in a given format. The function validate_be_iban() validates either a single Belgian IBAN strings, a column of Belgian IBAN strings or a DataFrame of Belgian IBAN strings, returning True if the value is valid, and False otherwise.

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

  • compact: only number strings without any seperators or whitespace, like “BE32123456789002”

  • standard: Belgian IBAN strings with proper whitespace in the proper places, like “BE32 1234 5678 9002”

  • bic: return the BIC for the bank that this number refers to, like “CTBKBEBX”.

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_be_iban() and validate_be_iban().

An example dataset containing Belgian IBAN strings

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "be_iban": [
            "BE32 123-4567890-02",
            "BE41091811735141",             # incorrect national check digits
            "BE83138811735115",             # unknown bank code
            "GR1601101050000010547023795",  # not a Belgian IBAN
            "BE 48 3200 7018 4927",
            "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]:
be_iban address
0 BE32 123-4567890-02 123 Pine Ave.
1 BE41091811735141 main st
2 BE83138811735115 1234 west main heights 57033
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan
4 BE 48 3200 7018 4927 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_be_iban

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

[2]:

from dataprep.clean import clean_be_iban
clean_be_iban(df, column = "be_iban")

[2]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. BE32 1234 5678 9002
1 BE41091811735141 main st NaN
2 BE83138811735115 1234 west main heights 57033 NaN
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan NaN
4 BE 48 3200 7018 4927 robie house, 789 north main street BE48 3200 7018 4927
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_be_iban(df, column = "be_iban", output_format="standard")

[3]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. BE32 1234 5678 9002
1 BE41091811735141 main st NaN
2 BE83138811735115 1234 west main heights 57033 NaN
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan NaN
4 BE 48 3200 7018 4927 robie house, 789 north main street BE48 3200 7018 4927
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_be_iban(df, column = "be_iban", output_format="compact")

[4]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. BE32123456789002
1 BE41091811735141 main st NaN
2 BE83138811735115 1234 west main heights 57033 NaN
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan NaN
4 BE 48 3200 7018 4927 robie house, 789 north main street BE48320070184927
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

bic

[5]:

clean_be_iban(df, column = "be_iban", output_format="bic")

[5]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. CTBKBEBX
1 BE41091811735141 main st NaN
2 BE83138811735115 1234 west main heights 57033 NaN
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan NaN
4 BE 48 3200 7018 4927 robie house, 789 north main street BBRUBEBB
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 Belgian IBAN strings is added with a title in the format "{original title}_clean".

[6]:

clean_be_iban(df, column="be_iban", inplace=True)

[6]:
be_iban_clean address
0 BE32 1234 5678 9002 123 Pine Ave.
1 NaN main st
2 NaN 1234 west main heights 57033
3 NaN apt 1 789 s maple rd manhattan
4 BE48 3200 7018 4927 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_be_iban(df, "be_iban", errors="coerce")

[7]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. BE32 1234 5678 9002
1 BE41091811735141 main st NaN
2 BE83138811735115 1234 west main heights 57033 NaN
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan NaN
4 BE 48 3200 7018 4927 robie house, 789 north main street BE48 3200 7018 4927
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_be_iban(df, "be_iban", errors="ignore")

[8]:
be_iban address be_iban_clean
0 BE32 123-4567890-02 123 Pine Ave. BE32 1234 5678 9002
1 BE41091811735141 main st BE41091811735141
2 BE83138811735115 1234 west main heights 57033 BE83138811735115
3 GR1601101050000010547023795 apt 1 789 s maple rd manhattan GR1601101050000010547023795
4 BE 48 3200 7018 4927 robie house, 789 north main street BE48 3200 7018 4927
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_be_iban()

validate_be_iban() returns True when the input is a valid Belgian IBAN. Otherwise it returns False.

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

[9]:

from dataprep.clean import validate_be_iban
print(validate_be_iban("BE32 123-4567890-02"))
print(validate_be_iban("BE41091811735141"))
print(validate_be_iban("BE83138811735115"))
print(validate_be_iban("GR1601101050000010547023795"))
print(validate_be_iban("004085616"))
print(validate_be_iban("hello"))
print(validate_be_iban(np.nan))
print(validate_be_iban("NULL"))

True
False
False
False
False
False
False
False

Series

[10]:

validate_be_iban(df["be_iban"])

[10]:

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

DataFrame + Specify Column

[11]:

validate_be_iban(df, column="be_iban")

[11]:

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

Only DataFrame

[12]:

validate_be_iban(df)

[12]:
be_iban address
0 True False
1 False False
2 False False
3 False False
4 True False
5 False False
6 False False
7 False False 
[ ]: