""" Remove columns from a tabular file. """
from hed.tools.remodeling.operations.base_op import BaseOp
[docs]class RemoveColumnsOp(BaseOp):
""" Remove columns from a tabular file.
Required remodeling parameters:
- **column_names** (*list*): The names of the columns to be removed.
- **ignore_missing** (*boolean*): If true, names in column_names that are not columns in df should be ignored.
"""
PARAMS = {
"operation": "remove_columns",
"required_parameters": {
"column_names": list,
"ignore_missing": bool
},
"optional_parameters": {}
}
[docs] def __init__(self, parameters):
""" Constructor for remove columns operation.
Parameters:
parameters (dict): Dictionary with the parameter values for required and optional parameters
:raises KeyError:
- If a required parameter is missing.
- If an unexpected parameter is provided.
:raises TypeError:
- If a parameter has the wrong type.
"""
super().__init__(self.PARAMS, parameters)
self.column_names = parameters['column_names']
ignore_missing = parameters['ignore_missing']
if ignore_missing:
self.error_handling = 'ignore'
else:
self.error_handling = 'raise'
[docs] def do_op(self, dispatcher, df, name, sidecar=None):
""" Remove indicated columns from a dataframe.
Parameters:
dispatcher (Dispatcher): Manages the operation I/O.
df (DataFrame): The DataFrame to be remodeled.
name (str): Unique identifier for the dataframe -- often the original file path.
sidecar (Sidecar or file-like): Not needed for this operation.
Returns:
Dataframe: A new dataframe after processing.
:raises KeyError:
- If ignore_missing is False and a column not in the data is to be removed.
"""
df_new = df.copy()
try:
return df_new.drop(self.column_names, axis=1, errors=self.error_handling)
except KeyError:
raise KeyError("MissingColumnCannotBeRemoved",
f"{name}: Ignore missing is False but a column in {str(self.column_names)} is "
f"not in the data columns [{str(df_new.columns)}]")