<jupyter_start><jupyter_text>Spanish Fiscal Numbers  Introduction The function `clean_es_cif()` cleans a column containing Spanish fiscal numbers (CIF) strings, and standardizes them in a given format. The function `validate_es_cif()` validates either a single CIF strings, a column of CIF strings or a DataFrame of CIF strings, returning `True` if the value is valid, and `False` otherwise. CIF strings can be converted to the following formats via the `output_format` parameter:* `compact`: only number strings without any seperators or whitespace, like "A13585625"* `standard`: CIF strings with proper whitespace in the proper places. Note that in the case of CIF, the compact format is the same as the standard one.Invalid parsing is handled with the `errors` parameter:* `coerce` (default): invalid parsing will be set to NaN* `ignore`: invalid parsing will return the input* `raise`: invalid parsing will raise an exceptionThe following sections demonstrate the functionality of `clean_es_cif()` and `validate_es_cif()`.   An example dataset containing CIF strings<jupyter_code>import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "cif": [
            'A13 585 625',
            'M-1234567-L',
            'BE 428759497',
            'BE431150351',
            "002 724 334",
            "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",
            "1111 S Figueroa St, Los Angeles, CA 90015",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df<jupyter_output><empty_output><jupyter_text>1. Default `clean_es_cif`By default, `clean_es_cif` will clean cif strings and output them in the standard format with proper separators.<jupyter_code>from dataprep.clean import clean_es_cif
clean_es_cif(df, column = "cif")<jupyter_output><empty_output><jupyter_text>2. Output formats This section demonstrates the output parameter.  `standard` (default)<jupyter_code>clean_es_cif(df, column = "cif", output_format="standard")<jupyter_output><empty_output><jupyter_text>`compact`<jupyter_code>clean_es_cif(df, column = "cif", output_format="compact")<jupyter_output><empty_output><jupyter_text>3. `split` parameterThe `split` parameter adds individual columns containing the cleaned 9-digits CIF values to the given DataFrame.<jupyter_code>clean_es_cif(df, column="cif", split=True)<jupyter_output><empty_output><jupyter_text>4. `inplace` parameterThis deletes the given column from the returned DataFrame. A new column containing cleaned CIF strings is added with a title in the format `"{original title}_clean"`.<jupyter_code>clean_es_cif(df, column="cif", inplace=True)<jupyter_output><empty_output><jupyter_text>5. `errors` parameter  `coerce` (default)<jupyter_code>clean_es_cif(df, "cif", errors="coerce")<jupyter_output><empty_output><jupyter_text>`ignore`<jupyter_code>clean_es_cif(df, "cif", errors="ignore")<jupyter_output><empty_output><jupyter_text>6. `validate_es_cif()` `validate_es_cif()` returns `True` when the input is a valid CIF. Otherwise it returns `False`.The input of `validate_es_cif()` 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_es_cif()` only returns the validation result for the specified column. If user doesn't specify the column name, `validate_es_cif()` returns the validation result for the whole DataFrame.<jupyter_code>from dataprep.clean import validate_es_cif
print(validate_es_cif("A13 585 625"))
print(validate_es_cif("M-1234567-L"))
print(validate_es_cif('BE 428759497'))
print(validate_es_cif('BE431150351'))
print(validate_es_cif("004085616"))
print(validate_es_cif("hello"))
print(validate_es_cif(np.nan))
print(validate_es_cif("NULL"))<jupyter_output><empty_output><jupyter_text>Series<jupyter_code>validate_es_cif(df["cif"])<jupyter_output><empty_output><jupyter_text>DataFrame + Specify Column<jupyter_code>validate_es_cif(df, column="cif")<jupyter_output><empty_output><jupyter_text>Only DataFrame<jupyter_code>validate_es_cif(df)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09749670619235837, 'fraction_numerical': 0.028765920070267896, 'mean_word_length': 5.274104683195592, 'pattern_counts': {'":': 2, '<': 49, '<?xml version=': 0, '>': 49, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '12', 'dir': 'jupyter-structured-clean-dedup', 'id': '541452', 'license': 'MIT', 'n_tokens_mistral': 1556, 'n_tokens_neox': 1378, 'n_words': 442, 'path': 'docs/source/user_guide/clean/clean_es_cif.ipynb', 'repo_name': 'jwa345/dataprep', 'size': '4554'}