Canadian Business Numbers

Introduction

The function clean_ca_bn() cleans a column containing Canadian Business Number (BN) strings, and standardizes them in a given format. The function validate_ca_bn() validates either a single BN strings, a column of BN strings or a DataFrame of BN strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “123026635”

• standard: BN strings with proper whitespace in the proper places. Note: in the case of BN, 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_ca_bn() and validate_ca_bn().

An example dataset containing BN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "bn": [
            "12302 6635",
            "12302 6635 RC 0001",
            "12345678Z",
            "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

	bn	address
0	12302 6635	123 Pine Ave.
1	12302 6635 RC 0001	main st
2	12345678Z	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_ca_bn

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

from dataprep.clean import clean_ca_bn
clean_ca_bn(df, column = "bn")

	bn	address	bn_clean
0	12302 6635	123 Pine Ave.	123026635
1	12302 6635 RC 0001	main st	123026635RC0001
2	12345678Z	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_ca_bn(df, column = "bn", output_format="standard")

	bn	address	bn_clean
0	12302 6635	123 Pine Ave.	123026635
1	12302 6635 RC 0001	main st	123026635RC0001
2	12345678Z	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_ca_bn(df, column = "bn", output_format="compact")

	bn	address	bn_clean
0	12302 6635	123 Pine Ave.	123026635
1	12302 6635 RC 0001	main st	123026635RC0001
2	12345678Z	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 BN strings is added with a title in the format "{original title}_clean".

clean_ca_bn(df, column="bn", inplace=True)

	bn_clean	address
0	123026635	123 Pine Ave.
1	123026635RC0001	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_ca_bn(df, "bn", errors="coerce")

	bn	address	bn_clean
0	12302 6635	123 Pine Ave.	123026635
1	12302 6635 RC 0001	main st	123026635RC0001
2	12345678Z	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_ca_bn(df, "bn", errors="ignore")

	bn	address	bn_clean
0	12302 6635	123 Pine Ave.	123026635
1	12302 6635 RC 0001	main st	123026635RC0001
2	12345678Z	1234 west main heights 57033	12345678Z
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_ca_bn()

validate_ca_bn() returns True when the input is a valid BN. Otherwise it returns False.

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

from dataprep.clean import validate_ca_bn
print(validate_ca_bn("12302 6635"))
print(validate_ca_bn("12302 6635 RC 0001"))
print(validate_ca_bn("12345678Z"))
print(validate_ca_bn("51 824 753 556"))
print(validate_ca_bn("hello"))
print(validate_ca_bn(np.nan))
print(validate_ca_bn("NULL"))

True
True
False
False
False
False
False

Series

validate_ca_bn(df["bn"])

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

DataFrame + Specify Column

validate_ca_bn(df, column="bn")

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

Only DataFrame

validate_ca_bn(df)

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