""" Clean and validate a DataFrame column containing Spanish fiscal numbers (CIFs). """ # 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.es import cif from ..progress_bar import ProgressBar from .utils import NULL_VALUES, to_dask [docs]def clean_es_cif( df: Union[pd.DataFrame, dd.DataFrame], column: str, output_format: str = "standard", split: bool = False, inplace: bool = False, errors: str = "coerce", progress: bool = True, ) -> pd.DataFrame: """ Clean Spanish fiscal numbers (CIFs) 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 CIF 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. Note: in the case of CIF, the compact format is the same as the standard one. (default: "standard") split If True, each component of derived from its number string will be put into its own column. (default: False) 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 CIF data. >>> df = pd.DataFrame({{ "cif": [ 'A13 585 625', 'M-1234567-L',] }) >>> clean_es_cif(df, 'cif') cif cif_clean 0 A13 585 625 A13585625 1 M-1234567-L 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, split, errors) for x in srs], meta=object, ) if split: df = df.assign( _temp_=df["clean_code_tup"].map(itemgetter(0), meta=("_temp", object)), type_code=df["clean_code_tup"].map(itemgetter(1), meta=("type_code", object)), province_code=df["clean_code_tup"].map(itemgetter(2), meta=("province_code", object)), within_code=df["clean_code_tup"].map(itemgetter(3), meta=("within_code", object)), check_digit=df["clean_code_tup"].map(itemgetter(4), meta=("check_digit", object)), ) else: 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_es_cif( 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 CIF 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(cif.is_valid) elif isinstance(df, (pd.DataFrame, dd.DataFrame)): if column != "": return df[column].apply(cif.is_valid) else: return df.applymap(cif.is_valid) return cif.is_valid(df) def _format( val: Any, output_format: str = "standard", split: bool = False, 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. Note: in the case of CIF, the compact format is the same as the standard one. """ val = str(val) result: Any = [] if val in NULL_VALUES: if split: return [np.nan, np.nan, np.nan, np.nan, np.nan] else: return [np.nan] if not validate_es_cif(val): if errors == "raise": raise ValueError(f"Unable to parse value {val}") error_result = val if errors == "ignore" else np.nan if split: return [error_result, np.nan, np.nan, np.nan, np.nan] else: return [error_result] if split: result = list(cif.split(val)) if len(result) == 0: return [np.nan, np.nan, np.nan, np.nan, np.nan] if output_format in {"compact", "standard"}: result = [cif.compact(val)] + result return result