Swiss VAT Numbers¶
Introduction¶
The function clean_ch_vat()
cleans a column containing Swiss VAT number (VAT) strings, and standardizes them in a given format. The function validate_ch_vat()
validates either a single VAT strings, a column of VAT strings or a DataFrame of VAT strings, returning True
if the value is valid, and False
otherwise.
VAT strings can be converted to the following formats via the output_format
parameter:
compact
: only number strings without any seperators or whitespace, like “CHE107787577IVA’”standard
: VAT strings with proper whitespace in the proper places, like “CHE-107.787.577 IVA”
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_ch_vat()
and validate_ch_vat()
.
An example dataset containing VAT strings¶
[1]:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"vat": [
'CHE107787577IVA',
'CHE-107.787.578 IVA',
"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]:
vat | address | |
---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. |
1 | CHE-107.787.578 IVA | 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_ch_vat
¶
By default, clean_ch_vat
will clean vat strings and output them in the standard format with proper separators.
[2]:
from dataprep.clean import clean_ch_vat
clean_ch_vat(df, column = "vat")
[2]:
vat | address | vat_clean | |
---|---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. | CHE-107.787.577 IVA |
1 | CHE-107.787.578 IVA | 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_ch_vat(df, column = "vat", output_format="standard")
[3]:
vat | address | vat_clean | |
---|---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. | CHE-107.787.577 IVA |
1 | CHE-107.787.578 IVA | 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_ch_vat(df, column = "vat", output_format="compact")
[4]:
vat | address | vat_clean | |
---|---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. | CHE107787577IVA |
1 | CHE-107.787.578 IVA | 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 VAT strings is added with a title in the format "{original title}_clean"
.
[5]:
clean_ch_vat(df, column="vat", inplace=True)
[5]:
vat_clean | address | |
---|---|---|
0 | CHE-107.787.577 IVA | 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_ch_vat(df, "vat", errors="coerce")
[6]:
vat | address | vat_clean | |
---|---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. | CHE-107.787.577 IVA |
1 | CHE-107.787.578 IVA | 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_ch_vat(df, "vat", errors="ignore")
[7]:
vat | address | vat_clean | |
---|---|---|---|
0 | CHE107787577IVA | 123 Pine Ave. | CHE-107.787.577 IVA |
1 | CHE-107.787.578 IVA | main st | CHE-107.787.578 IVA |
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_ch_vat()
¶
validate_ch_vat()
returns True
when the input is a valid VAT. Otherwise it returns False
.
The input of validate_ch_vat()
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_ch_vat()
only returns the validation result for the specified column. If user doesn’t specify the column name, validate_ch_vat()
returns the validation result for the whole DataFrame.
[8]:
from dataprep.clean import validate_ch_vat
print(validate_ch_vat("CHE107787577IVA"))
print(validate_ch_vat("CHE-107.787.578 IVA"))
print(validate_ch_vat("51824753556"))
print(validate_ch_vat("51 824 753 556"))
print(validate_ch_vat("hello"))
print(validate_ch_vat(np.nan))
print(validate_ch_vat("NULL"))
True
False
False
False
False
False
False
Series¶
[9]:
validate_ch_vat(df["vat"])
[9]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
Name: vat, dtype: bool
DataFrame + Specify Column¶
[10]:
validate_ch_vat(df, column="vat")
[10]:
0 True
1 False
2 False
3 False
4 False
5 False
6 False
Name: vat, dtype: bool
Only DataFrame¶
[11]:
validate_ch_vat(df)
[11]:
vat | address | |
---|---|---|
0 | True | False |
1 | False | False |
2 | False | False |
3 | False | False |
4 | False | False |
5 | False | False |
6 | False | False |
[ ]: