German Tax Numbers

Introduction

The function clean_de_stnr() cleans a column containing German tax numbers (STNR) strings, and standardizes them in a given format. The function validate_de_stnr() validates either a single STNR strings, a column of STNR strings or a DataFrame of STNR strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “18181508155”

• standard: STNR strings with proper whitespace in the proper places, like “181/815/0815 5”

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_de_stnr() and validate_de_stnr().

An example dataset containing STNR strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "stnr": [
            ' 181/815/0815 5',
            "136695978",
            "201/123/12340",
            "4151081508156",
            "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

	stnr	address
0	181/815/0815 5	123 Pine Ave.
1	136695978	main st
2	201/123/12340	1234 west main heights 57033
3	4151081508156	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_de_stnr

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

from dataprep.clean import clean_de_stnr
clean_de_stnr(df, column = "stnr")

	stnr	address	stnr_clean
0	181/815/0815 5	123 Pine Ave.	181/815/08155
1	136695978	main st	NaN
2	201/123/12340	1234 west main heights 57033	201/123/12340
3	4151081508156	apt 1 789 s maple rd manhattan	4151081508156
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_de_stnr(df, column = "stnr", output_format="standard")

	stnr	address	stnr_clean
0	181/815/0815 5	123 Pine Ave.	181/815/08155
1	136695978	main st	NaN
2	201/123/12340	1234 west main heights 57033	201/123/12340
3	4151081508156	apt 1 789 s maple rd manhattan	4151081508156
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_de_stnr(df, column = "stnr", output_format="compact")

	stnr	address	stnr_clean
0	181/815/0815 5	123 Pine Ave.	18181508155
1	136695978	main st	NaN
2	201/123/12340	1234 west main heights 57033	20112312340
3	4151081508156	apt 1 789 s maple rd manhattan	4151081508156
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 STNR strings is added with a title in the format "{original title}_clean".

clean_de_stnr(df, column="stnr", inplace=True)

	stnr_clean	address
0	181/815/08155	123 Pine Ave.
1	NaN	main st
2	201/123/12340	1234 west main heights 57033
3	4151081508156	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_de_stnr(df, "stnr", errors="coerce")

	stnr	address	stnr_clean
0	181/815/0815 5	123 Pine Ave.	181/815/08155
1	136695978	main st	NaN
2	201/123/12340	1234 west main heights 57033	201/123/12340
3	4151081508156	apt 1 789 s maple rd manhattan	4151081508156
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_de_stnr(df, "stnr", errors="ignore")

	stnr	address	stnr_clean
0	181/815/0815 5	123 Pine Ave.	181/815/08155
1	136695978	main st	136695978
2	201/123/12340	1234 west main heights 57033	201/123/12340
3	4151081508156	apt 1 789 s maple rd manhattan	4151081508156
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_de_stnr()

validate_de_stnr() returns True when the input is a valid STNR. Otherwise it returns False.

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

from dataprep.clean import validate_de_stnr
print(validate_de_stnr("181/815/0815 5"))
print(validate_de_stnr("136695978"))
print(validate_de_stnr("201/123/12340"))
print(validate_de_stnr("4151081508156"))
print(validate_de_stnr("hello"))
print(validate_de_stnr(np.nan))
print(validate_de_stnr("NULL"))

True
False
True
True
False
False
False

Series

validate_de_stnr(df["stnr"])

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

DataFrame + Specify Column

validate_de_stnr(df, column="stnr")

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

Only DataFrame

validate_de_stnr(df)

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

region parameter

Specifically, region can be supplied to validate_de_stnr to verify that the number is assigned in that region.

validate_de_stnr(df["stnr"], region='Sachsen')

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

validate_de_stnr(df["stnr"], region='Thuringen')

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