Spanish IBANs

Introduction

The function clean_es_iban() cleans a column containing Spanish IBAN (IBAN) strings, and standardizes them in a given format. The function validate_es_iban() validates either a single IBAN strings, a column of IBAN strings or a DataFrame of IBAN strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “ES7712341234161234567890”

• standard: IBAN strings with proper whitespace in the proper places, like “ES77 1234 1234 1612 3456 7890”

• ccc: return the CCC (Código Cuenta Corriente) part of the number, like “12341234161234567890”.

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_es_iban() and validate_es_iban().

An example dataset containing IBAN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "iban": [
            "ES771234-1234-16 1234567890",
            "R1601101050000010547023795",
            "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

	iban	address
0	ES771234-1234-16 1234567890	123 Pine Ave.
1	R1601101050000010547023795	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_es_iban

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

from dataprep.clean import clean_es_iban
clean_es_iban(df, column = "iban")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	ES77 1234 1234 1612 3456 7890
1	R1601101050000010547023795	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_es_iban(df, column = "iban", output_format="standard")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	ES77 1234 1234 1612 3456 7890
1	R1601101050000010547023795	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_es_iban(df, column = "iban", output_format="compact")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	ES7712341234161234567890
1	R1601101050000010547023795	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

ccc

clean_es_iban(df, column = "iban", output_format="ccc")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	12341234161234567890
1	R1601101050000010547023795	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 IBAN strings is added with a title in the format "{original title}_clean".

clean_es_iban(df, column="iban", inplace=True)

	iban_clean	address
0	ES77 1234 1234 1612 3456 7890	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_es_iban(df, "iban", errors="coerce")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	ES77 1234 1234 1612 3456 7890
1	R1601101050000010547023795	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_es_iban(df, "iban", errors="ignore")

	iban	address	iban_clean
0	ES771234-1234-16 1234567890	123 Pine Ave.	ES77 1234 1234 1612 3456 7890
1	R1601101050000010547023795	main st	R1601101050000010547023795
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_es_iban()

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

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

from dataprep.clean import validate_es_iban
print(validate_es_iban("ES771234-1234-16 1234567890"))
print(validate_es_iban("R1601101050000010547023795"))
print(validate_es_iban("999 999 999"))
print(validate_es_iban("51824753556"))
print(validate_es_iban("004085616"))
print(validate_es_iban("hello"))
print(validate_es_iban(np.nan))
print(validate_es_iban("NULL"))

True
False
False
False
False
False
False
False

Series

validate_es_iban(df["iban"])

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

DataFrame + Specify Column

validate_es_iban(df, column="iban")

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

Only DataFrame

validate_es_iban(df)

	iban	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