French Tax Identification Numbers¶
Introduction¶
The function clean_fr_nif()
cleans a column containing French tax identification number (NIF) strings, and standardizes them in a given format. The function validate_fr_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 “0701987765432”standard
: NIF strings with proper whitespace in the proper places, like “07 01 987 765 432”
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_fr_nif()
and validate_fr_nif()
.
An example dataset containing NIF strings¶
[1]:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"nif": [
"0701987765432",
"070198776543",
"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
[1]:
nif | address | |
---|---|---|
0 | 0701987765432 | 123 Pine Ave. |
1 | 070198776543 | 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_fr_nif
¶
By default, clean_fr_nif
will clean nif strings and output them in the standard format with proper separators.
[2]:
from dataprep.clean import clean_fr_nif
clean_fr_nif(df, column = "nif")
[2]:
nif | address | nif_clean | |
---|---|---|---|
0 | 0701987765432 | 123 Pine Ave. | 07 01 987 765 432 |
1 | 070198776543 | 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)¶
[3]:
clean_fr_nif(df, column = "nif", output_format="standard")
[3]:
nif | address | nif_clean | |
---|---|---|---|
0 | 0701987765432 | 123 Pine Ave. | 07 01 987 765 432 |
1 | 070198776543 | 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
¶
[4]:
clean_fr_nif(df, column = "nif", output_format="compact")
[4]:
nif | address | nif_clean | |
---|---|---|---|
0 | 0701987765432 | 123 Pine Ave. | 0701987765432 |
1 | 070198776543 | 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 NIF strings is added with a title in the format "{original title}_clean"
.
[5]:
clean_fr_nif(df, column="nif", inplace=True)
[5]:
nif_clean | address | |
---|---|---|
0 | 07 01 987 765 432 | 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)¶
[6]:
clean_fr_nif(df, "nif", errors="coerce")
[6]:
nif | address | nif_clean | |
---|---|---|---|
0 | 0701987765432 | 123 Pine Ave. | 07 01 987 765 432 |
1 | 070198776543 | 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
¶
[7]:
clean_fr_nif(df, "nif", errors="ignore")
[7]:
nif | address | nif_clean | |
---|---|---|---|
0 | 0701987765432 | 123 Pine Ave. | 07 01 987 765 432 |
1 | 070198776543 | main st | 070198776543 |
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_fr_nif()
¶
validate_fr_nif()
returns True
when the input is a valid NIF. Otherwise it returns False
.
The input of validate_fr_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_fr_nif()
only returns the validation result for the specified column. If user doesn’t specify the column name, validate_fr_nif()
returns the validation result for the whole DataFrame.
[8]:
from dataprep.clean import validate_fr_nif
print(validate_fr_nif("0701987765432"))
print(validate_fr_nif("070198776543"))
print(validate_fr_nif("51824753556"))
print(validate_fr_nif("51 824 753 556"))
print(validate_fr_nif("hello"))
print(validate_fr_nif(np.nan))
print(validate_fr_nif("NULL"))
True
False
False
False
False
False
False
Series¶
[9]:
validate_fr_nif(df["nif"])
[9]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
Name: nif, dtype: bool
DataFrame + Specify Column¶
[10]:
validate_fr_nif(df, column="nif")
[10]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
Name: nif, dtype: bool
Only DataFrame¶
[11]:
validate_fr_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 |
[ ]: