""" Rename columns in a columnar file. """
from hed.tools.remodeling.operations.base_op import BaseOp
[docs]class RenameColumnsOp (BaseOp):
""" Rename columns in a tabular file.
Required remodeling parameters:
- **column_mapping** (*dict*): The names of the columns to be renamed with values to be remapped to.
- **ignore_missing** (*bool*): If true, the names in column_mapping that are not columns and should be ignored.
"""
NAME = "rename_columns"
PARAMS = {
"type": "object",
"properties": {
"column_mapping": {
"type": "object",
"description": "Mapping between original column names and their respective new names.",
"patternProperties": {
".*": {
"type": "string"
}
},
"minProperties": 1
},
"ignore_missing": {
"type": "boolean",
"description": "If true ignore column_mapping keys that don't correspond to columns, otherwise error."
}
},
"required": [
"column_mapping",
"ignore_missing"
],
"additionalProperties": False
}
[docs] def __init__(self, parameters):
""" Constructor for rename columns operation.
Parameters:
parameters (dict): Dictionary with the parameter values for required and optional parameters
"""
super().__init__(parameters)
self.column_mapping = parameters['column_mapping']
if parameters['ignore_missing']:
self.error_handling = 'ignore'
else:
self.error_handling = 'raise'
[docs] def do_op(self, dispatcher, df, name, sidecar=None):
""" Rename columns as specified in column_mapping dictionary.
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:
- When ignore_missing is False and column_mapping has columns not in the data.
"""
df_new = df.copy()
try:
return df_new.rename(columns=self.column_mapping, errors=self.error_handling)
except KeyError:
raise KeyError("MappedColumnsMissingFromData",
f"{name}: ignore_missing is False, mapping columns [{self.column_mapping}]"
f" but df columns are [{str(df.columns)}")