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

[1]:
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
[1]:
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.

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

[3]:
clean_es_iban(df, column = "iban", output_format="standard")
[3]:
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

[4]:
clean_es_iban(df, column = "iban", output_format="compact")
[4]:
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

[5]:
clean_es_iban(df, column = "iban", output_format="ccc")
[5]:
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".

[6]:
clean_es_iban(df, column="iban", inplace=True)
[6]:
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)

[7]:
clean_es_iban(df, "iban", errors="coerce")
[7]:
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

[8]:
clean_es_iban(df, "iban", errors="ignore")
[8]:
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.

[9]:
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

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

DataFrame + Specify Column

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

Only DataFrame

[12]:
validate_es_iban(df)
[12]:
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
[ ]: