cdpg_anonkit.sanitisation
=========================

.. py:module:: cdpg_anonkit.sanitisation


Classes
-------

.. autoapisummary::

   cdpg_anonkit.sanitisation.SanitiseData


Module Contents
---------------

.. py:class:: SanitiseData

   .. py:method:: clip(min_value: float, max_value: float) -> pandas.Series

      Clip (limit) the values in a Series to a specified range.

      :param series: The input Series to be clipped.
      :type series: pd.Series
      :param min_value: The minimum value to clip to.
      :type min_value: float
      :param max_value: The maximum value to clip to.
      :type max_value: float

      :returns: The clipped Series.
      :rtype: pd.Series



   .. py:method:: hash_values(salt: str = '') -> pandas.Series

      Hash the values in a Series using the SHA-256 algorithm.

      This can be used to pseudonymise values that need to be kept secret.
      The salt parameter can be used to add a common salt to all values.
      This can be useful if you want to combine the hashed values with other columns
      to create a unique identifier.

      :param series: The input Series to be hashed.
      :type series: pd.Series
      :param salt: The salt to add to all values before hashing.
                   Defaults to an empty string.
      :type salt: str, optional

      :returns: The hashed Series.
      :rtype: pd.Series



   .. py:method:: suppress(threshold: int = 5, replacement: Optional[Union[str, int, float]] = None) -> pandas.Series

      Suppress all values in a Series that occur less than a given threshold.

      Replace all values that occur less than the threshold with the replacement value.

      :param series: The input Series to be suppressed.
      :type series: pd.Series
      :param threshold: The minimum number of occurrences for a value to be kept.
                        Defaults to 5.
      :type threshold: int, optional
      :param replacement: The value to replace suppressed values with.
                          Defaults to None, which means that the values will be replaced with NaN.
      :type replacement: Optional[Union[str, int, float]], optional

      :returns: The Series with suppressed values.
      :rtype: pd.Series



   .. py:method:: sanitise_data(columns_to_sanitise: List[str], sanitisation_rules: Dict[str, Dict[str, Union[str, float, int, List, Dict]]], drop_na: bool = False) -> pandas.DataFrame

      Sanitise a DataFrame by applying different methods to each column.

      :param df: The input DataFrame to be sanitised.
      :type df: pd.DataFrame
      :param columns_to_sanitise: The columns in the DataFrame to be sanitised.
      :type columns_to_sanitise: List[str]
      :param sanitisation_rules: A dictionary that maps each column in columns_to_sanitise to a dictionary
                                 that specifies the sanitisation method and parameters for that column.
                                 The dictionary should contain the following keys:
                                 * 'method': str, the sanitisation method to use
                                 * 'params': Dict[str, Union[str, float, int, List, Dict]], the parameters
                                   for the sanitisation method
      :type sanitisation_rules: Dict[str, Dict[str, Union[str, float, int, List, Dict]]]
      :param drop_na: If True, drop all rows in the DataFrame that have any NaN values in the
                      columns specified in columns_to_sanitise. Defaults to False.
      :type drop_na: bool, optional

      :returns: The sanitised DataFrame.
      :rtype: pd.DataFrame