Costa Rica Tax Numbers

Introduction

The function clean_cr_cpj() cleans a column containing Costa Rica tax number (CPJ) strings, and standardizes them in a given format. The function validate_cr_cpj() validates either a single CPJ strings, a column of CPJ strings or a DataFrame of CPJ strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “4000042138”

• standard: CPJ strings with proper whitespace in the proper places, like “4-000-042138”

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_cr_cpj() and validate_cr_cpj().

An example dataset containing CPJ strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cpj": [
            "4 000 042138",
            "3-534-123559",
            "51824753556",
            "51 824 753 556",
            "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

	cpj	address
0	4 000 042138	123 Pine Ave.
1	3-534-123559	main st
2	51824753556	1234 west main heights 57033
3	51 824 753 556	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_cr_cpj

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

from dataprep.clean import clean_cr_cpj
clean_cr_cpj(df, column = "cpj")

	cpj	address	cpj_clean
0	4 000 042138	123 Pine Ave.	4-000-042138
1	3-534-123559	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_cr_cpj(df, column = "cpj", output_format="standard")

	cpj	address	cpj_clean
0	4 000 042138	123 Pine Ave.	4-000-042138
1	3-534-123559	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_cr_cpj(df, column = "cpj", output_format="compact")

	cpj	address	cpj_clean
0	4 000 042138	123 Pine Ave.	4000042138
1	3-534-123559	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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 CPJ strings is added with a title in the format "{original title}_clean".

clean_cr_cpj(df, column="cpj", inplace=True)

	cpj_clean	address
0	4-000-042138	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_cr_cpj(df, "cpj", errors="coerce")

	cpj	address	cpj_clean
0	4 000 042138	123 Pine Ave.	4-000-042138
1	3-534-123559	main st	NaN
2	51824753556	1234 west main heights 57033	NaN
3	51 824 753 556	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_cr_cpj(df, "cpj", errors="ignore")

	cpj	address	cpj_clean
0	4 000 042138	123 Pine Ave.	4-000-042138
1	3-534-123559	main st	3-534-123559
2	51824753556	1234 west main heights 57033	51824753556
3	51 824 753 556	apt 1 789 s maple rd manhattan	51 824 753 556
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_cr_cpj()

validate_cr_cpj() returns True when the input is a valid CPJ. Otherwise it returns False.

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

from dataprep.clean import validate_cr_cpj
print(validate_cr_cpj("4 000 042138"))
print(validate_cr_cpj("3-534-123559"))
print(validate_cr_cpj("51824753556"))
print(validate_cr_cpj("51 824 753 556"))
print(validate_cr_cpj("hello"))
print(validate_cr_cpj(np.nan))
print(validate_cr_cpj("NULL"))

True
False
False
False
False
False
False

Series

validate_cr_cpj(df["cpj"])

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

DataFrame + Specify Column

validate_cr_cpj(df, column="cpj")

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

Only DataFrame

validate_cr_cpj(df)

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