Peruvian Fiscal Numbers

Introduction

The function clean_pe_ruc() cleans a column containing Peruvian fiscal number (RUC) strings, and standardizes them in a given format. The function validate_pe_ruc() validates either a single RUC strings, a column of RUC strings or a DataFrame of RUC strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “20512333797”.

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

• dni: return the DNI (CUI) part of the number for natural persons. Note if the RUC is not for natural persons, return NaN.

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_pe_ruc() and validate_pe_ruc().

An example dataset containing RUC strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "ruc": [
            "20512333797",
            "20512333798",
            '7542011030',
            '7552A10004',
            '8019010008',
            "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

	ruc	address
0	20512333797	123 Pine Ave.
1	20512333798	main st
2	7542011030	1234 west main heights 57033
3	7552A10004	apt 1 789 s maple rd manhattan
4	8019010008	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_pe_ruc

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

from dataprep.clean import clean_pe_ruc
clean_pe_ruc(df, column = "ruc")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	20512333797
1	20512333798	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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_pe_ruc(df, column = "ruc", output_format="standard")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	20512333797
1	20512333798	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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_pe_ruc(df, column = "ruc", output_format="compact")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	20512333797
1	20512333798	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

dni

clean_pe_ruc(df, column = "ruc", output_format="dni")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	NaN
1	20512333798	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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 RUC strings is added with a title in the format "{original title}_clean".

clean_pe_ruc(df, column="ruc", inplace=True)

	ruc_clean	address
0	20512333797	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_pe_ruc(df, "ruc", errors="coerce")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	20512333797
1	20512333798	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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_pe_ruc(df, "ruc", errors="ignore")

	ruc	address	ruc_clean
0	20512333797	123 Pine Ave.	20512333797
1	20512333798	main st	20512333798
2	7542011030	1234 west main heights 57033	7542011030
3	7552A10004	apt 1 789 s maple rd manhattan	7552A10004
4	8019010008	robie house, 789 north main street	8019010008
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_pe_ruc()

validate_pe_ruc() returns True when the input is a valid RUC. Otherwise it returns False.

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

from dataprep.clean import validate_pe_ruc
print(validate_pe_ruc('20512333797'))
print(validate_pe_ruc('20512333798'))
print(validate_pe_ruc('7542011030'))
print(validate_pe_ruc('7552A10004'))
print(validate_pe_ruc('8019010008'))
print(validate_pe_ruc("hello"))
print(validate_pe_ruc(np.nan))
print(validate_pe_ruc("NULL"))

True
False
False
False
False
False
False
False

Series

validate_pe_ruc(df["ruc"])

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

DataFrame + Specify Column

validate_pe_ruc(df, column="ruc")

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

Only DataFrame

validate_pe_ruc(df)

	ruc	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