clarite.modify.recode_values

clarite.modify.recode_values(data, replacement_dict, skip: Union[str, List[str], NoneType] = None, only: Union[str, List[str], NoneType] = None)

Convert values in a dataframe. By default, replacement occurs in all columns but this may be modified with ‘skip’ or ‘only’. Pandas has more powerful ‘replace’ methods for more complicated scenarios.

Parameters:

data: pd.DataFrame

The DataFrame to be processed and returned

replacement_dict: dictionary

A dictionary mapping the value being replaced to the value being inserted

skip: str, list or None (default is None)

List of variables that the replacement should not be applied to

only: str, list or None (default is None)

List of variables that the replacement should only be applied to

Examples

>>> import clarite
>>> clarite.modify.recode_values(df, {7: np.nan, 9: np.nan}, only=['SMQ077', 'DBD100'])
================================================================================
Running recode_values
--------------------------------------------------------------------------------
Replaced 17 values from 22,624 observations in 2 variables
>>> clarite.modify.recode_values(df, {10: 12}, only=['SMQ077', 'DBD100'])
================================================================================
Running recode_values
--------------------------------------------------------------------------------
No occurences of replaceable values were found, so nothing was replaced.