Cuban Identity Card Numbers¶

Introduction¶

The function clean_cu_ni() cleans a column containing Cuban identity card numbers (NI) strings, and standardizes them in a given format. The function validate_cu_ni() validates either a single NI strings, a column of NI strings or a DataFrame of NI strings, returning True if the value is valid, and False otherwise.

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

compact: only number strings without any seperators or whitespace, like “91021027775”
standard: NI strings with proper whitespace in the proper places. Note that in the case of NI, the compact format is the same as the standard one.
birthdate: split the date parts from the number and return the birth date, like “1991-02-10”.
gender: return the gender (M/F) from the person’s NI.

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_cu_ni() and validate_cu_ni().

An example dataset containing NI strings¶

[1]:

import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "ni": [
            '91021027775',
            '9102102777A',
            '7542011030',
            '7552A10004',
            '8019010008',
            "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

[1]:

	ni	address
0	91021027775	123 Pine Ave.
1	9102102777A	main st
2	7542011030	1234 west main heights 57033
3	7552A10004	apt 1 789 s maple rd manhattan
4	8019010008	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_cu_ni`¶

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

[2]:

from dataprep.clean import clean_cu_ni
clean_cu_ni(df, column = "ni")

[2]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	91021027775
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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)¶

[3]:

clean_cu_ni(df, column = "ni", output_format="standard")

[3]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	91021027775
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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`¶

[4]:

clean_cu_ni(df, column = "ni", output_format="compact")

[4]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	91021027775
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

`birthdate`¶

[5]:

clean_cu_ni(df, column = "ni", output_format="birthdate")

[5]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	1991-02-10
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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

`gender`¶

[6]:

clean_cu_ni(df, column = "ni", output_format="gender")

[6]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	F
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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 NI strings is added with a title in the format "{original title}_clean".

[7]:

clean_cu_ni(df, column="ni", inplace=True)

[7]:

	ni_clean	address
0	91021027775	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)¶

[8]:

clean_cu_ni(df, "ni", errors="coerce")

[8]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	91021027775
1	9102102777A	main st	NaN
2	7542011030	1234 west main heights 57033	NaN
3	7552A10004	apt 1 789 s maple rd manhattan	NaN
4	8019010008	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`¶

[9]:

clean_cu_ni(df, "ni", errors="ignore")

[9]:

	ni	address	ni_clean
0	91021027775	123 Pine Ave.	91021027775
1	9102102777A	main st	9102102777A
2	7542011030	1234 west main heights 57033	7542011030
3	7552A10004	apt 1 789 s maple rd manhattan	7552A10004
4	8019010008	robie house, 789 north main street	8019010008
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_cu_ni()`¶

validate_cu_ni() returns True when the input is a valid NI. Otherwise it returns False.

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

[10]:

from dataprep.clean import validate_cu_ni
print(validate_cu_ni('91021027775'))
print(validate_cu_ni('9102102777A'))
print(validate_cu_ni('7542011030'))
print(validate_cu_ni('7552A10004'))
print(validate_cu_ni('8019010008'))
print(validate_cu_ni("hello"))
print(validate_cu_ni(np.nan))
print(validate_cu_ni("NULL"))

True
False
False
False
False
False
False
False

Series¶

[11]:

validate_cu_ni(df["ni"])

[11]:

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

DataFrame + Specify Column¶

[12]:

validate_cu_ni(df, column="ni")

[12]:

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

Only DataFrame¶

[13]:

validate_cu_ni(df)

[13]:

	ni	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

[ ]:

Cuban Identity Card Numbers¶

Introduction¶

An example dataset containing NI strings¶

1. Default clean_cu_ni¶

2. Output formats¶

standard (default)¶

compact¶

birthdate¶

gender¶

3. inplace parameter¶

4. errors parameter¶

coerce (default)¶

ignore¶

4. validate_cu_ni()¶