""" Clean and validate a DataFrame column containing CPF numbers, Brazilian national identifier. """ # 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.br import cpf from ..progress_bar import ProgressBar from .utils import NULL_VALUES, to_dask [docs]def clean_br_cpf( df: Union[pd.DataFrame, dd.DataFrame], column: str, output_format: str = "standard", inplace: bool = False, errors: str = "coerce", progress: bool = True, ) -> pd.DataFrame: """ Clean Brazilian national identifier 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 CPF 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 CPF data. >>> df = pd.DataFrame({{ "cpf": [ '23100299900', '231.002.999-00', # InvalidChecksum '390.533.447=0'] # invalid delimiter }) >>> clean_br_cpf(df, 'cpf') cpf cpf_clean 0 23100299900 231.002.999-00 1 231.002.999-00 NaN 2 390.533.447=0 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_br_cpf( 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 CPF 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(cpf.is_valid) elif isinstance(df, (pd.DataFrame, dd.DataFrame)): if column != "": return df[column].apply(cpf.is_valid) else: return df.applymap(cpf.is_valid) return cpf.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) if val in NULL_VALUES: return [np.nan] if not validate_br_cpf(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 = [cpf.compact(val)] elif output_format == "standard": result = [cpf.format(val)] return result