English Unique Pupil Numbers

Introduction

The function clean_gb_upn() cleans a column containing English Unique Pupil Number (UPN) strings, and standardizes them in a given format. The function validate_gb_upn() validates either a single UPN strings, a column of UPN strings or a DataFrame of UPN strings, returning True if the value is valid, and False otherwise.

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

• compact: only number strings without any seperators or whitespace, like “B801200005001”

• standard: UPN strings with proper whitespace in the proper places. Note that in the case of UPN, 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_gb_upn() and validate_gb_upn().

An example dataset containing UPN strings

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "upn": [
            'B801200005001',
            'A801200005001',
            '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

	upn	address
0	B801200005001	123 Pine Ave.
1	A801200005001	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_gb_upn

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

from dataprep.clean import clean_gb_upn
clean_gb_upn(df, column = "upn")

	upn	address	upn_clean
0	B801200005001	123 Pine Ave.	B801200005001
1	A801200005001	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	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_gb_upn(df, column = "upn", output_format="standard")

	upn	address	upn_clean
0	B801200005001	123 Pine Ave.	B801200005001
1	A801200005001	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	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_gb_upn(df, column = "upn", output_format="compact")

	upn	address	upn_clean
0	B801200005001	123 Pine Ave.	B801200005001
1	A801200005001	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	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 UPN strings is added with a title in the format "{original title}_clean".

clean_gb_upn(df, column="upn", inplace=True)

	upn_clean	address
0	B801200005001	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_gb_upn(df, "upn", errors="coerce")

	upn	address	upn_clean
0	B801200005001	123 Pine Ave.	B801200005001
1	A801200005001	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	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_gb_upn(df, "upn", errors="ignore")

	upn	address	upn_clean
0	B801200005001	123 Pine Ave.	B801200005001
1	A801200005001	main st	A801200005001
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	002 724 334
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_gb_upn()

validate_gb_upn() returns True when the input is a valid UPN. Otherwise it returns False.

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

from dataprep.clean import validate_gb_upn
print(validate_gb_upn("B801200005001"))
print(validate_gb_upn("A801200005001"))
print(validate_gb_upn('BE 428759497'))
print(validate_gb_upn('BE431150351'))
print(validate_gb_upn("004085616"))
print(validate_gb_upn("hello"))
print(validate_gb_upn(np.nan))
print(validate_gb_upn("NULL"))

True
False
False
False
False
False
False
False

Series

validate_gb_upn(df["upn"])

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

DataFrame + Specify Column

validate_gb_upn(df, column="upn")

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

Only DataFrame

validate_gb_upn(df)

	upn	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