impute dates with iterative solution and change log

impute_dates_iter_change_log.py

# same iterrows() solution with change log
curr_year = 2020
past_year = 2010
change_log = []
# loop through index and rows of df
for idx, row in df.iterrows():
    if row[col1].year > curr_year:
        new_year = row[col2].year
        # save reference data as tuple
        records = tuple((row['case_id']
                         , row['event_id']
                         , row[col1].year
                         , new_year))
        
        # append to list
        change_log.append(records)

        df.loc[idx, col1] = row[col1].replace(year=new_year)

    if row[col1].year < past_year:
        new_year = row[col2].year
        # save reference data as tuple
        records = tuple((row['case_id']
                         , row['event_id']
                         , row[col1].year
                         , new_year))
        
        # append to list
        change_log.append(records)

        df.loc[idx, col1] = row[col1].replace(year=new_year)


# some new column names
old_date = str('old_' + date_type)
new_date = str('new_' + date_type)
# a new DataFrame from list of tuples
log = pd.DataFrame.from_records(change_log, 
                                columns=['case_id',
                                  'event_id', 
                                  old_date, 
                                  new_date])
# traceable record of changes to csv
log.to_csv('change_log.csv')