Spanish Fiscal Numbers¶

Introduction¶

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

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

compact: only number strings without any seperators or whitespace, like “A13585625”
standard: CIF strings with proper whitespace in the proper places. Note that in the case of CIF, 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_cif() and validate_es_cif().

An example dataset containing CIF strings¶

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cif": [
            'A13 585 625',
            'M-1234567-L',
            '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

[1]:

	cif	address
0	A13 585 625	123 Pine Ave.
1	M-1234567-L	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_cif`¶

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

[2]:

from dataprep.clean import clean_es_cif
clean_es_cif(df, column = "cif")

[2]:

	cif	address	cif_clean
0	A13 585 625	123 Pine Ave.	A13585625
1	M-1234567-L	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)¶

[3]:

clean_es_cif(df, column = "cif", output_format="standard")

[3]:

	cif	address	cif_clean
0	A13 585 625	123 Pine Ave.	A13585625
1	M-1234567-L	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`¶

[4]:

clean_es_cif(df, column = "cif", output_format="compact")

[4]:

	cif	address	cif_clean
0	A13 585 625	123 Pine Ave.	A13585625
1	M-1234567-L	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. `split` parameter¶

The split parameter adds individual columns containing the cleaned 9-digits CIF values to the given DataFrame.

[5]:

clean_es_cif(df, column="cif", split=True)

[5]:

	cif	address	cif_clean	type_code	province_code	within_code	check_digit
0	A13 585 625	123 Pine Ave.	A13585625	A	13	58562	5
1	M-1234567-L	main st	NaN	NaN	NaN	NaN	NaN
2	BE 428759497	1234 west main heights 57033	NaN	NaN	NaN	NaN	NaN
3	BE431150351	apt 1 789 s maple rd manhattan	NaN	NaN	NaN	NaN	NaN
4	002 724 334	robie house, 789 north main street	NaN	NaN	NaN	NaN	NaN
5	hello	1111 S Figueroa St, Los Angeles, CA 90015	NaN	NaN	NaN	NaN	NaN
6	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN	NaN	NaN	NaN	NaN
7	NULL	hello	NaN	NaN	NaN	NaN	NaN

4. `inplace` parameter¶

This deletes the given column from the returned DataFrame. A new column containing cleaned CIF strings is added with a title in the format "{original title}_clean".

[6]:

clean_es_cif(df, column="cif", inplace=True)

[6]:

	cif_clean	address
0	A13585625	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

5. `errors` parameter¶

`coerce` (default)¶

[7]:

clean_es_cif(df, "cif", errors="coerce")

[7]:

	cif	address	cif_clean
0	A13 585 625	123 Pine Ave.	A13585625
1	M-1234567-L	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`¶

[8]:

clean_es_cif(df, "cif", errors="ignore")

[8]:

	cif	address	cif_clean
0	A13 585 625	123 Pine Ave.	A13585625
1	M-1234567-L	main st	M-1234567-L
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

6. `validate_es_cif()`¶

validate_es_cif() returns True when the input is a valid CIF. Otherwise it returns False.

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

[9]:

from dataprep.clean import validate_es_cif
print(validate_es_cif("A13 585 625"))
print(validate_es_cif("M-1234567-L"))
print(validate_es_cif('BE 428759497'))
print(validate_es_cif('BE431150351'))
print(validate_es_cif("004085616"))
print(validate_es_cif("hello"))
print(validate_es_cif(np.nan))
print(validate_es_cif("NULL"))

True
False
False
False
False
False
False
False

Series¶

[10]:

validate_es_cif(df["cif"])

[10]:

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

DataFrame + Specify Column¶

[11]:

validate_es_cif(df, column="cif")

[11]:

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

Only DataFrame¶

[12]:

validate_es_cif(df)

[12]:

	cif	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

[ ]:

Spanish Fiscal Numbers¶

Introduction¶

An example dataset containing CIF strings¶

1. Default clean_es_cif¶

2. Output formats¶

standard (default)¶

compact¶

3. split parameter¶

4. inplace parameter¶

5. errors parameter¶

coerce (default)¶

ignore¶

6. validate_es_cif()¶