Swiss Business Identifiers

Introduction

The function clean_ch_uid() cleans a column containing Swiss business identifier (UID) strings, and standardizes them in a given format. The function validate_ch_uid() validates either a single UID strings, a column of UID strings or a DataFrame of UID strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “CHE100155212”

• standard: UID strings with proper whitespace in the proper places, like “CHE-100.155.212”

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_ch_uid() and validate_ch_uid().

An example dataset containing UID strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "uid": [
            "CHE100155212",
            "CHE-100.155.213",
            "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

	uid	address
0	CHE100155212	123 Pine Ave.
1	CHE-100.155.213	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_ch_uid

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

from dataprep.clean import clean_ch_uid
clean_ch_uid(df, column = "uid")

	uid	address	uid_clean
0	CHE100155212	123 Pine Ave.	CHE-100.155.212
1	CHE-100.155.213	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_ch_uid(df, column = "uid", output_format="standard")

	uid	address	uid_clean
0	CHE100155212	123 Pine Ave.	CHE-100.155.212
1	CHE-100.155.213	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_ch_uid(df, column = "uid", output_format="compact")

	uid	address	uid_clean
0	CHE100155212	123 Pine Ave.	CHE100155212
1	CHE-100.155.213	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 UID strings is added with a title in the format "{original title}_clean".

clean_ch_uid(df, column="uid", inplace=True)

	uid_clean	address
0	CHE-100.155.212	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_ch_uid(df, "uid", errors="coerce")

	uid	address	uid_clean
0	CHE100155212	123 Pine Ave.	CHE-100.155.212
1	CHE-100.155.213	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_ch_uid(df, "uid", errors="ignore")

	uid	address	uid_clean
0	CHE100155212	123 Pine Ave.	CHE-100.155.212
1	CHE-100.155.213	main st	CHE-100.155.213
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_ch_uid()

validate_ch_uid() returns True when the input is a valid UID. Otherwise it returns False.

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

from dataprep.clean import validate_ch_uid
print(validate_ch_uid("CHE100155212"))
print(validate_ch_uid("CHE-100.155.213"))
print(validate_ch_uid("51824753556"))
print(validate_ch_uid("51 824 753 556"))
print(validate_ch_uid("hello"))
print(validate_ch_uid(np.nan))
print(validate_ch_uid("NULL"))

True
False
False
False
False
False
False

Series

validate_ch_uid(df["uid"])

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

DataFrame + Specify Column

validate_ch_uid(df, column="uid")

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

Only DataFrame

validate_ch_uid(df)

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