Polish Register Of Economic Units

Introduction

The function clean_pl_regon() cleans a column containing Polish register of economic units (REGON) strings, and standardizes them in a given format. The function validate_pl_regon() validates either a single REGON strings, a column of REGON strings or a DataFrame of REGON strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “192598184”

• standard: REGON strings with proper whitespace in the proper places. Note that in the case of REGON, 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_pl_regon() and validate_pl_regon().

An example dataset containing REGON strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "regon": [
            '192598184',
            '192598183',
            '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

	regon	address
0	192598184	123 Pine Ave.
1	192598183	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_pl_regon

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

from dataprep.clean import clean_pl_regon
clean_pl_regon(df, column = "regon")

	regon	address	regon_clean
0	192598184	123 Pine Ave.	192598184
1	192598183	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	002724334
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_pl_regon(df, column = "regon", output_format="standard")

	regon	address	regon_clean
0	192598184	123 Pine Ave.	192598184
1	192598183	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	002724334
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_pl_regon(df, column = "regon", output_format="compact")

	regon	address	regon_clean
0	192598184	123 Pine Ave.	192598184
1	192598183	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	002724334
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 REGON strings is added with a title in the format "{original title}_clean".

clean_pl_regon(df, column="regon", inplace=True)

	regon_clean	address
0	192598184	123 Pine Ave.
1	NaN	main st
2	NaN	1234 west main heights 57033
3	NaN	apt 1 789 s maple rd manhattan
4	002724334	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_pl_regon(df, "regon", errors="coerce")

	regon	address	regon_clean
0	192598184	123 Pine Ave.	192598184
1	192598183	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	002724334
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_pl_regon(df, "regon", errors="ignore")

	regon	address	regon_clean
0	192598184	123 Pine Ave.	192598184
1	192598183	main st	192598183
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	002724334
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_pl_regon()

validate_pl_regon() returns True when the input is a valid REGON. Otherwise it returns False.

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

from dataprep.clean import validate_pl_regon
print(validate_pl_regon("192598184"))
print(validate_pl_regon("192598183"))
print(validate_pl_regon('BE 428759497'))
print(validate_pl_regon('BE431150351'))
print(validate_pl_regon("004085616"))
print(validate_pl_regon("hello"))
print(validate_pl_regon(np.nan))
print(validate_pl_regon("NULL"))

True
False
False
False
False
False
False
False

Series

validate_pl_regon(df["regon"])

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

DataFrame + Specify Column

validate_pl_regon(df, column="regon")

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

Only DataFrame

validate_pl_regon(df)

	regon	address
0	True	False
1	False	False
2	False	False
3	False	False
4	True	False
5	False	False
6	False	False
7	False	False