Brazilian National Identifiers

Introduction

The function clean_br_cpf() cleans a column containing Brazilian national identifier (CPF) strings, and standardizes them in a given format. The function validate_br_cpf() validates either a single cpf strings, a column of CPF strings or a DataFrame of CPF strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “39053344705”

• standard: cpf strings with proper whitespace in the proper places, like “390.533.447-05”

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_br_cpf() and validate_br_cpf().

An example dataset containing cpf strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cpf": [
            '390.533.447-05',
            '231.002.999-00',
            '390.533.447=0',
            '23100299900',
            "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",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df

	cpf	address
0	390.533.447-05	123 Pine Ave.
1	231.002.999-00	main st
2	390.533.447=0	1234 west main heights 57033
3	23100299900	apt 1 789 s maple rd manhattan
4	hello	robie house, 789 north main street
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles
6	NULL	hello

1. Default clean_br_cpf

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

from dataprep.clean import clean_br_cpf
clean_br_cpf(df, column = "cpf")

	cpf	address	cpf_clean
0	390.533.447-05	123 Pine Ave.	390.533.447-05
1	231.002.999-00	main st	NaN
2	390.533.447=0	1234 west main heights 57033	NaN
3	23100299900	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

2. Output formats

This section demonstrates the output parameter.

standard (default)

clean_br_cpf(df, column = "cpf", output_format="standard")

	cpf	address	cpf_clean
0	390.533.447-05	123 Pine Ave.	390.533.447-05
1	231.002.999-00	main st	NaN
2	390.533.447=0	1234 west main heights 57033	NaN
3	23100299900	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

compact

clean_br_cpf(df, column = "cpf", output_format="compact")

	cpf	address	cpf_clean
0	390.533.447-05	123 Pine Ave.	39053344705
1	231.002.999-00	main st	NaN
2	390.533.447=0	1234 west main heights 57033	NaN
3	23100299900	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

3. inplace parameter

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

clean_br_cpf(df, column="cpf", inplace=True)

	cpf_clean	address
0	390.533.447-05	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	(staples center) 1111 S Figueroa St, Los Angeles
6	NaN	hello

4. errors parameter

coerce (default)

clean_br_cpf(df, "cpf", errors="coerce")

	cpf	address	cpf_clean
0	390.533.447-05	123 Pine Ave.	390.533.447-05
1	231.002.999-00	main st	NaN
2	390.533.447=0	1234 west main heights 57033	NaN
3	23100299900	apt 1 789 s maple rd manhattan	NaN
4	hello	robie house, 789 north main street	NaN
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

ignore

clean_br_cpf(df, "cpf", errors="ignore")

	cpf	address	cpf_clean
0	390.533.447-05	123 Pine Ave.	390.533.447-05
1	231.002.999-00	main st	231.002.999-00
2	390.533.447=0	1234 west main heights 57033	390.533.447=0
3	23100299900	apt 1 789 s maple rd manhattan	23100299900
4	hello	robie house, 789 north main street	hello
5	NaN	(staples center) 1111 S Figueroa St, Los Angeles	NaN
6	NULL	hello	NaN

4. validate_br_cpf()

validate_br_cpf() returns True when the input is a valid CPF. Otherwise it returns False.

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

from dataprep.clean import validate_br_cpf
print(validate_br_cpf('390.533.447-05'))
print(validate_br_cpf('231.002.999-00'))
print(validate_br_cpf('390.533.447=0'))
print(validate_br_cpf('23100299900'))
print(validate_br_cpf("hello"))
print(validate_br_cpf(np.nan))
print(validate_br_cpf("NULL"))

True
False
False
False
False
False
False

Series

validate_br_cpf(df["cpf"])

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

DataFrame + Specify Column

validate_br_cpf(df, column="cpf")

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

Only DataFrame

validate_br_cpf(df)

	cpf	address
0	True	False
1	False	False
2	False	False
3	False	False
4	False	False
5	False	False
6	False	False