Brazilian Company Identifiers

Introduction

The function clean_br_cnpj() cleans a column containing Brazilian company identifier (CNPJ) strings, and standardizes them in a given format. The function validate_br_cnpj() validates either a single cnpj strings, a column of CNPJ strings or a DataFrame of CNPJ strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “16727230000197”

• standard: cnpj strings with proper whitespace in the proper places, like “16.727.230/0001-97”

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_cnpj() and validate_br_cnpj().

An example dataset containing cnpj strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cnpj": [
            "16.727.230/0001-97",
            "16.727.230.0001-98",
            "16.727.230/0001=97",
            "16727230000197",
            "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

	cnpj	address
0	16.727.230/0001-97	123 Pine Ave.
1	16.727.230.0001-98	main st
2	16.727.230/0001=97	1234 west main heights 57033
3	16727230000197	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_cnpj

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

from dataprep.clean import clean_br_cnpj
clean_br_cnpj(df, column = "cnpj")

	cnpj	address	cnpj_clean
0	16.727.230/0001-97	123 Pine Ave.	16.727.230/0001-97
1	16.727.230.0001-98	main st	NaN
2	16.727.230/0001=97	1234 west main heights 57033	NaN
3	16727230000197	apt 1 789 s maple rd manhattan	16.727.230/0001-97
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_cnpj(df, column = "cnpj", output_format="standard")

	cnpj	address	cnpj_clean
0	16.727.230/0001-97	123 Pine Ave.	16.727.230/0001-97
1	16.727.230.0001-98	main st	NaN
2	16.727.230/0001=97	1234 west main heights 57033	NaN
3	16727230000197	apt 1 789 s maple rd manhattan	16.727.230/0001-97
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_cnpj(df, column = "cnpj", output_format="compact")

	cnpj	address	cnpj_clean
0	16.727.230/0001-97	123 Pine Ave.	16727230000197
1	16.727.230.0001-98	main st	NaN
2	16.727.230/0001=97	1234 west main heights 57033	NaN
3	16727230000197	apt 1 789 s maple rd manhattan	16727230000197
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 CNPJ strings is added with a title in the format "{original title}_clean".

clean_br_cnpj(df, column="cnpj", inplace=True)

	cnpj_clean	address
0	16.727.230/0001-97	123 Pine Ave.
1	NaN	main st
2	NaN	1234 west main heights 57033
3	16.727.230/0001-97	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_cnpj(df, "cnpj", errors="coerce")

	cnpj	address	cnpj_clean
0	16.727.230/0001-97	123 Pine Ave.	16.727.230/0001-97
1	16.727.230.0001-98	main st	NaN
2	16.727.230/0001=97	1234 west main heights 57033	NaN
3	16727230000197	apt 1 789 s maple rd manhattan	16.727.230/0001-97
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_cnpj(df, "cnpj", errors="ignore")

	cnpj	address	cnpj_clean
0	16.727.230/0001-97	123 Pine Ave.	16.727.230/0001-97
1	16.727.230.0001-98	main st	16.727.230.0001-98
2	16.727.230/0001=97	1234 west main heights 57033	16.727.230/0001=97
3	16727230000197	apt 1 789 s maple rd manhattan	16.727.230/0001-97
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_cnpj()

validate_br_cnpj() returns True when the input is a valid CNPJ. Otherwise it returns False.

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

from dataprep.clean import validate_br_cnpj
print(validate_br_cnpj('16.727.230/0001-97'))
print(validate_br_cnpj('16.727.230.0001-98'))
print(validate_br_cnpj('16.727.230/0001=97'))
print(validate_br_cnpj("51 824 753 556"))
print(validate_br_cnpj("hello"))
print(validate_br_cnpj(np.nan))
print(validate_br_cnpj("NULL"))

True
False
False
False
False
False
False

Series

validate_br_cnpj(df["cnpj"])

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

DataFrame + Specify Column

validate_br_cnpj(df, column="cnpj")

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

Only DataFrame

validate_br_cnpj(df)

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