"""
Clean and validate a DataFrame column containing Montenegro IBANs (IBANs).
"""
# pylint: disable=too-many-lines, too-many-arguments, too-many-branches
from typing import Any, Union
from operator import itemgetter
import dask.dataframe as dd
import numpy as np
import pandas as pd
from stdnum.me import iban
from ..progress_bar import ProgressBar
from .utils import NULL_VALUES, to_dask
[docs]def clean_me_iban(
df: Union[pd.DataFrame, dd.DataFrame],
column: str,
output_format: str = "standard",
inplace: bool = False,
errors: str = "coerce",
progress: bool = True,
) -> pd.DataFrame:
"""
Clean Montenegro IBANs (IBANs) type data in a DataFrame column.
Parameters
----------
df
A pandas or Dask DataFrame containing the data to be cleaned.
col
The name of the column containing data of IBAN type.
output_format
The output format of standardized number string.
If output_format = 'compact', return string without any separators or whitespace.
If output_format = 'standard', return string with proper separators and whitespace.
(default: "standard")
inplace
If True, delete the column containing the data that was cleaned.
Otherwise, keep the original column.
(default: False)
errors
How to handle parsing errors.
- ‘coerce’: invalid parsing will be set to NaN.
- ‘ignore’: invalid parsing will return the input.
- ‘raise’: invalid parsing will raise an exception.
(default: 'coerce')
progress
If True, display a progress bar.
(default: True)
Examples
--------
Clean a column of IBAN data.
>>> df = pd.DataFrame({{
"iban": [
"ME25510000000006234133",
"ME52510000000006234132",]
})
>>> clean_me_iban(df, 'iban')
iban iban_clean
0 ME25510000000006234133 ME 2551 0000 0000 0623 4133
1 ME52510000000006234132 NaN
"""
if output_format not in {"compact", "standard"}:
raise ValueError(
f"output_format {output_format} is invalid. " 'It needs to be "compact" or "standard".'
)
# convert to dask
df = to_dask(df)
# To clean, create a new column "clean_code_tup" which contains
# the cleaned values and code indicating how the initial value was
# changed in a tuple. Then split the column of tuples and count the
# amount of different codes to produce the report
df["clean_code_tup"] = df[column].map_partitions(
lambda srs: [_format(x, output_format, errors) for x in srs],
meta=object,
)
df = df.assign(
_temp_=df["clean_code_tup"].map(itemgetter(0)),
)
df = df.rename(columns={"_temp_": f"{column}_clean"})
df = df.drop(columns=["clean_code_tup"])
if inplace:
df[column] = df[f"{column}_clean"]
df = df.drop(columns=f"{column}_clean")
df = df.rename(columns={column: f"{column}_clean"})
with ProgressBar(minimum=1, disable=not progress):
df = df.compute()
return df
[docs]def validate_me_iban(
df: Union[str, pd.Series, dd.Series, pd.DataFrame, dd.DataFrame],
column: str = "",
) -> Union[bool, pd.Series, pd.DataFrame]:
"""
Validate if a data cell is IBAN in a DataFrame column. For each cell, return True or False.
Parameters
----------
df
A pandas or Dask DataFrame containing the data to be validated.
col
The name of the column to be validated.
"""
if isinstance(df, (pd.Series, dd.Series)):
return df.apply(iban.is_valid)
elif isinstance(df, (pd.DataFrame, dd.DataFrame)):
if column != "":
return df[column].apply(iban.is_valid)
else:
return df.applymap(iban.is_valid)
return iban.is_valid(df)
def _format(val: Any, output_format: str = "standard", errors: str = "coarse") -> Any:
"""
Reformat a number string with proper separators and whitespace.
Parameters
----------
val
The value of number string.
output_format
If output_format = 'compact', return string without any separators or whitespace.
If output_format = 'standard', return string with proper separators and whitespace.
"""
val = str(val)
result: Any = []
if val in NULL_VALUES:
return [np.nan]
if not validate_me_iban(val):
if errors == "raise":
raise ValueError(f"Unable to parse value {val}")
error_result = val if errors == "ignore" else np.nan
return [error_result]
if output_format == "compact":
result = [iban.compact(val)] + result
elif output_format == "standard":
result = [iban.format(val)] + result
return result