Portuguese NIF Numbers¶
Introduction¶
The function clean_pt_nif()
cleans a column containing Portuguese NIF number (NIF) strings, and standardizes them in a given format. The function validate_pt_nif()
validates either a single NIF strings, a column of NIF strings or a DataFrame of NIF strings, returning True
if the value is valid, and False
otherwise.
NIF strings can be converted to the following formats via the output_format
parameter:
compact
: only number strings without any seperators or whitespace, like “501964843”standard
: NIF strings with proper whitespace in the proper places. Note that in the case of NIF, 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 NaNignore
: invalid parsing will return the inputraise
: invalid parsing will raise an exception
The following sections demonstrate the functionality of clean_pt_nif()
and validate_pt_nif()
.
An example dataset containing NIF strings¶
[1]:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"nif": [
'PT 501 964 843',
'PT 501 964 842',
'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
[1]:
nif | address | |
---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. |
1 | PT 501 964 842 | 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_pt_nif
¶
By default, clean_pt_nif
will clean nif strings and output them in the standard format with proper separators.
[2]:
from dataprep.clean import clean_pt_nif
clean_pt_nif(df, column = "nif")
[2]:
nif | address | nif_clean | |
---|---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. | 501964843 |
1 | PT 501 964 842 | 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)¶
[3]:
clean_pt_nif(df, column = "nif", output_format="standard")
[3]:
nif | address | nif_clean | |
---|---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. | 501964843 |
1 | PT 501 964 842 | 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
¶
[4]:
clean_pt_nif(df, column = "nif", output_format="compact")
[4]:
nif | address | nif_clean | |
---|---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. | 501964843 |
1 | PT 501 964 842 | 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 NIF strings is added with a title in the format "{original title}_clean"
.
[5]:
clean_pt_nif(df, column="nif", inplace=True)
[5]:
nif_clean | address | |
---|---|---|
0 | 501964843 | 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)¶
[6]:
clean_pt_nif(df, "nif", errors="coerce")
[6]:
nif | address | nif_clean | |
---|---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. | 501964843 |
1 | PT 501 964 842 | 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
¶
[7]:
clean_pt_nif(df, "nif", errors="ignore")
[7]:
nif | address | nif_clean | |
---|---|---|---|
0 | PT 501 964 843 | 123 Pine Ave. | 501964843 |
1 | PT 501 964 842 | main st | PT 501 964 842 |
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_pt_nif()
¶
validate_pt_nif()
returns True
when the input is a valid NIF. Otherwise it returns False
.
The input of validate_pt_nif()
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_pt_nif()
only returns the validation result for the specified column. If user doesn’t specify the column name, validate_pt_nif()
returns the validation result for the whole DataFrame.
[8]:
from dataprep.clean import validate_pt_nif
print(validate_pt_nif("PT 501 964 843"))
print(validate_pt_nif("PT 501 964 842"))
print(validate_pt_nif('BE 428759497'))
print(validate_pt_nif('BE431150351'))
print(validate_pt_nif("004085616"))
print(validate_pt_nif("hello"))
print(validate_pt_nif(np.nan))
print(validate_pt_nif("NULL"))
True
False
False
False
False
False
False
False
Series¶
[9]:
validate_pt_nif(df["nif"])
[9]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
7 False
Name: nif, dtype: bool
DataFrame + Specify Column¶
[10]:
validate_pt_nif(df, column="nif")
[10]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
7 False
Name: nif, dtype: bool
Only DataFrame¶
[11]:
validate_pt_nif(df)
[11]:
nif | 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 |
[ ]: