@export
def char_ord_score_df(data: pd.DataFrame, column: str, scale: int=1) -> pd.Series:
    '\n    Return sum of ord values of characters in string.\n\n    Parameters\n    ----------\n    data : pd.DataFrame\n        The DataFrame to process\n    column : str\n        Column name to process\n    scale : int, optional\n        reduce the scale of the feature (reducing the\n        influence of variations this feature on the clustering\n        algorithm (the default is 1)\n\n    Returns\n    -------\n    pd.Series\n        The sum of the ordinal values of the characters\n        in `column`.\n\n    Notes\n    -----\n    This function sums the ordinal value of each character in the\n    input string. Two strings with minor differences will result in\n    a similar score. However, for strings with highly variable content\n    (e.g. command lines or http requests containing GUIDs) this may result\n    in too much variance to be useful when you are trying to detect\n    similar patterns. You can use the scale parameter to reduce the\n    influence of features using this function on clustering and anomaly\n    algorithms.\n\n    '
    return data.apply((lambda x: (sum((ord(char) for char in x[column])) / scale)), axis=1)
Return sum of ord values of characters in string.

Parameters
----------
data : pd.DataFrame
    The DataFrame to process
column : str
    Column name to process
scale : int, optional
    reduce the scale of the feature (reducing the
    influence of variations this feature on the clustering
    algorithm (the default is 1)

Returns
-------
pd.Series
    The sum of the ordinal values of the characters
    in `column`.

Notes
-----
This function sums the ordinal value of each character in the
input string. Two strings with minor differences will result in
a similar score. However, for strings with highly variable content
(e.g. command lines or http requests containing GUIDs) this may result
in too much variance to be useful when you are trying to detect
similar patterns. You can use the scale parameter to reduce the
influence of features using this function on clustering and anomaly
algorithms.
@export
def char_ord_score_df(data: pd.DataFrame, column: str, scale: int=1) -> pd.Series:
    '\n    Return sum of ord values of characters in string.\n\n    Parameters\n    ----------\n    data : pd.DataFrame\n        The DataFrame to process\n    column : str\n        Column name to process\n    scale : int, optional\n        reduce the scale of the feature (reducing the\n        influence of variations this feature on the clustering\n        algorithm (the default is 1)\n\n    Returns\n    -------\n    pd.Series\n        The sum of the ordinal values of the characters\n        in `column`.\n\n    Notes\n    -----\n    This function sums the ordinal value of each character in the\n    input string. Two strings with minor differences will result in\n    a similar score. However, for strings with highly variable content\n    (e.g. command lines or http requests containing GUIDs) this may result\n    in too much variance to be useful when you are trying to detect\n    similar patterns. You can use the scale parameter to reduce the\n    influence of features using this function on clustering and anomaly\n    algorithms.\n\n    '
    return data.apply((lambda x: (sum((ord(char) for char in x[column])) / scale)), axis=1)