<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def update_record(self, name, new_data, condition, update_only=False, debug=False):
 """ Find the first row in self.df with index == name and condition == True. Update that record with new_data, then delete any additional records where index == name and condition == True. Change is inplace """

        # add numeric index column temporarily
        self.df['num'] = list(range(len(self.df)))
        df_data = self.df
        condition2 = (df_data.index == name)
        # edit first of existing data that meets condition
        if len(df_data[condition & condition2]) > 0:  #we have one or more records to update or delete
            # list of all rows where condition is true and index == name
            inds = df_data[condition & condition2]['num']
            #inds = df_data[condition]['num'] # list of all rows where condition is true
            existing_data = dict(df_data.iloc[inds.iloc[0]]) # get first record of existing_data from dataframe
            existing_data.update(new_data) # update existing data with new interpretations
            # update row
            self.update_row(inds.iloc[0], existing_data)
            # now remove all the remaining records of same condition
            if len(inds) > 1:
                for ind in inds[1:]:
                    print("deleting redundant records for:", name)
                    df_data = self.delete_row(ind)
        else:
            if update_only:
                print("no record found for that condition, not updating ", name)
            else:
                print('no record found - creating new one for ', name)
                # add new row
                df_data = self.add_row(name, new_data)
        # sort so that all rows for an item are together
        df_data.sort_index(inplace=True)
        # redo temporary index
        df_data['num'] = list(range(len(df_data)))
        self.df = df_data
        return df_data