<jupyter_start><jupyter_text>Portuguese NIF Numbers  Introduction The function `clean_pt_nif()` cleans a column containing Portuguese NIF number (NIF) strings, and standardizes them in a given format. The function `validate_pt_nif()` validates either a single NIF strings, a column of NIF strings or a DataFrame of NIF strings, returning `True` if the value is valid, and `False` otherwise. NIF strings can be converted to the following formats via the `output_format` parameter:* `compact`: only number strings without any seperators or whitespace, like "501964843"* `standard`: NIF strings with proper whitespace in the proper places. Note that in the case of NIF, 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_pt_nif()` and `validate_pt_nif()`.   An example dataset containing NIF strings<jupyter_code>import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "nif": [
            'PT 501 964 843',
            'PT 501 964 842',
            '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_pt_nif`By default, `clean_pt_nif` will clean nif strings and output them in the standard format with proper separators.<jupyter_code>from dataprep.clean import clean_pt_nif
clean_pt_nif(df, column = "nif")<jupyter_output><empty_output><jupyter_text>2. Output formats This section demonstrates the output parameter.  `standard` (default)<jupyter_code>clean_pt_nif(df, column = "nif", output_format="standard")<jupyter_output><empty_output><jupyter_text>`compact`<jupyter_code>clean_pt_nif(df, column = "nif", output_format="compact")<jupyter_output><empty_output><jupyter_text>3. `inplace` parameterThis deletes the given column from the returned DataFrame. A new column containing cleaned NIF strings is added with a title in the format `"{original title}_clean"`.<jupyter_code>clean_pt_nif(df, column="nif", inplace=True)<jupyter_output><empty_output><jupyter_text>4. `errors` parameter  `coerce` (default)<jupyter_code>clean_pt_nif(df, "nif", errors="coerce")<jupyter_output><empty_output><jupyter_text>`ignore`<jupyter_code>clean_pt_nif(df, "nif", errors="ignore")<jupyter_output><empty_output><jupyter_text>4. `validate_pt_nif()` `validate_pt_nif()` returns `True` when the input is a valid NIF. Otherwise it returns `False`.The input of `validate_pt_nif()` 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_pt_nif()` only returns the validation result for the specified column. If user doesn't specify the column name, `validate_pt_nif()` returns the validation result for the whole DataFrame.<jupyter_code>from dataprep.clean import validate_pt_nif
print(validate_pt_nif("PT 501 964 843"))
print(validate_pt_nif("PT 501 964 842"))
print(validate_pt_nif('BE 428759497'))
print(validate_pt_nif('BE431150351'))
print(validate_pt_nif("004085616"))
print(validate_pt_nif("hello"))
print(validate_pt_nif(np.nan))
print(validate_pt_nif("NULL"))<jupyter_output><empty_output><jupyter_text>Series<jupyter_code>validate_pt_nif(df["nif"])<jupyter_output><empty_output><jupyter_text>DataFrame + Specify Column<jupyter_code>validate_pt_nif(df, column="nif")<jupyter_output><empty_output><jupyter_text>Only DataFrame<jupyter_code>validate_pt_nif(df)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09623817216708977, 'fraction_numerical': 0.031387029771520886, 'mean_word_length': 5.121468926553672, 'pattern_counts': {'":': 2, '<': 45, '<?xml version=': 0, '>': 45, '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': '11', 'dir': 'jupyter-structured-clean-dedup', 'id': '490558', 'license': 'MIT', 'n_tokens_mistral': 1490, 'n_tokens_neox': 1351, 'n_words': 431, 'path': 'docs/source/user_guide/clean/clean_pt_nif.ipynb', 'repo_name': 'jwa345/dataprep', 'size': '4333'}