Spanish NIF Numbers

Introduction

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

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

• compact: only number strings without any seperators or whitespace, like “B58378431”

• standard: NIF strings with proper whitespace in the proper places. Note that in the case of NIF, the compact format is the same as the standard one.

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_nif() and validate_es_nif().

An example dataset containing NIF strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "nif": [
            'ES B-58378431',
            'B64717839',
            'BE 428759497',
            'BE431150351',
            "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

	nif	address
0	ES B-58378431	123 Pine Ave.
1	B64717839	main st
2	BE 428759497	1234 west main heights 57033
3	BE431150351	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_nif

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

from dataprep.clean import clean_es_nif
clean_es_nif(df, column = "nif")

	nif	address	nif_clean
0	ES B-58378431	123 Pine Ave.	B58378431
1	B64717839	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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_nif(df, column = "nif", output_format="standard")

	nif	address	nif_clean
0	ES B-58378431	123 Pine Ave.	B58378431
1	B64717839	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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_nif(df, column = "nif", output_format="compact")

	nif	address	nif_clean
0	ES B-58378431	123 Pine Ave.	B58378431
1	B64717839	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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 NIF strings is added with a title in the format "{original title}_clean".

clean_es_nif(df, column="nif", inplace=True)

	nif_clean	address
0	B58378431	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_nif(df, "nif", errors="coerce")

	nif	address	nif_clean
0	ES B-58378431	123 Pine Ave.	B58378431
1	B64717839	main st	NaN
2	BE 428759497	1234 west main heights 57033	NaN
3	BE431150351	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_nif(df, "nif", errors="ignore")

	nif	address	nif_clean
0	ES B-58378431	123 Pine Ave.	B58378431
1	B64717839	main st	B64717839
2	BE 428759497	1234 west main heights 57033	BE 428759497
3	BE431150351	apt 1 789 s maple rd manhattan	BE431150351
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_nif()

validate_es_nif() returns True when the input is a valid NIF. Otherwise it returns False.

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

from dataprep.clean import validate_es_nif
print(validate_es_nif('ES B-58378431'))
print(validate_es_nif('B64717839'))
print(validate_es_nif('BE 428759497'))
print(validate_es_nif('BE431150351'))
print(validate_es_nif("004085616"))
print(validate_es_nif("hello"))
print(validate_es_nif(np.nan))
print(validate_es_nif("NULL"))

True
False
False
False
False
False
False
False

Series

validate_es_nif(df["nif"])

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

DataFrame + Specify Column

validate_es_nif(df, column="nif")

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

Only DataFrame

validate_es_nif(df)

	nif	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