def date_dict(df_list, pif_key):
    encounter_dates = []
    for df in df_list:
        df['encounter_date'].fillna('', inplace=True)
        df_date = df.loc[df['pif_key'].astype(str) == str(pif_key), 'encounter_date'].values
        if len(df_date) > 0:
            encounter_dates.extend(df_date)
    if encounter_dates:
        ingested_date = max(encounter_dates)
    else:
        ingested_date = ''
    date_insert_dict = {
        'attribute_name': 'report_date',
        'attribute_method': 'cv',
        'attribute_normalized_prediction': '',
        'attribute_prediction': str(ingested_date),
        'attribute_version': 'v2_090523',
        'attribute_vocab': '',
        'attribute_code': '',
        'date_of_service': ''
    }    
    return date_insert_dict, encounter_dates, ingested_date


def report_date_insertion(dict_list, df_list, logging_df):
    col_names = {col['attribute_name'] for col in dict_list}
    if 'report_date' not in col_names:
        pif_key = next((col['attribute_prediction'] for col in dict_list if col['attribute_name'] == 'pif_key'), None)
        if pif_key is not None:
            date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
            dict_list.insert(1, date_insert_dict)
            if ingested_date:
                if len(encounter_dates)>1:
                    logging_df = logging_df.append({'pif_key': pif_key,
                                                    'json_report_date_exists': False,
                                                    'encounter_dates': encounter_dates,
                                                    'ingested_date': ingested_date,
                                                    'multiple_date': True, 'old_date': 'missing'}, ignore_index=True)
                else:
                    logging_df = logging_df.append({'pif_key': pif_key,
                                                    'json_report_date_exists': False,
                                                    'encounter_dates': encounter_dates,
                                                    'ingested_date': ingested_date,
                                                    'multiple_date': False, 'old_date': 'missing'}, ignore_index=True)
            else:
                logging_df = logging_df.append({'pif_key': pif_key,
                                                'json_report_date_exists': False,
                                                'encounter_dates': None,
                                                'ingested_date': '',
                                                'multiple_date': False, 'old_date': 'missing'}, ignore_index=True)

    elif 'report_date' in col_names:
        pif_key = next((col['attribute_prediction'] for col in dict_list if col['attribute_name'] == 'pif_key'), None)
        if pif_key is not None:
            date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
            if ingested_date:
                for report_date_idx, tm in enumerate(dict_list):
                    if tm['attribute_name']=='report_date':
                        old_date = tm['attribute_prediction']
                        break
                dict_list.pop(report_date_idx)
                dict_list.insert(1, date_insert_dict)
                if len(encounter_dates)>1:
                    logging_df = logging_df.append({'pif_key': pif_key,
                                                    'json_report_date_exists': True,
                                                    'encounter_dates': encounter_dates,
                                                    'ingested_date': ingested_date,
                                                    'multiple_date': True, 'old_date': old_date}, ignore_index=True)
                else:
                    logging_df = logging_df.append({'pif_key': pif_key,
                                                    'json_report_date_exists': True,
                                                    'encounter_dates': encounter_dates,
                                                    'ingested_date': ingested_date,
                                                    'multiple_date': False, 'old_date': old_date}, ignore_index=True)
            else:
                for report_date_idx, tm in enumerate(dict_list):
                    if tm['attribute_name']=='report_date':
                        old_date = tm['attribute_prediction']
                        break
                logging_df = logging_df.append({'pif_key': pif_key,
                                                'json_report_date_exists': True,
                                                'encounter_dates': None,
                                                'ingested_date': '',
                                                'multiple_date': False, 'old_date': old_date}, ignore_index=True)
    return dict_list, logging_df

def json_report_date_insertion(json_data, df_list, logging_df):
    for biomarker_detail in json_data['patient_level']['biomarkers']['details']:
        for attribute in biomarker_detail['attribute']:
            attribute_details = attribute['attribute_details']
            dict_list, logging_df = report_date_insertion(attribute_details, df_list, logging_df)
            attribute['attribute_details'] = dict_list
    
    return json_data, logging_df