File size: 2,955 Bytes
9bde11f dfc9b9e 9bde11f dfc9b9e 9bde11f dfc9b9e 9bde11f dfc9b9e 9bde11f dfc9b9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import pandas as pd
def date_dict(df_list, pif_key):
encounter_dates = []
for df in df_list:
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:
latest_date = max(encounter_dates)
return str(latest_date)
else:
return ''
def report_date_check(dict_list, df_list, logging_df):
pif_keys_with_report_date = set()
pif_keys_without_report_date = set()
for col in dict_list:
pif_key = col.get('attribute_prediction', None)
if pif_key is not None:
if col['attribute_name'] == 'report_date':
pif_keys_with_report_date.add(pif_key)
else:
pif_keys_without_report_date.add(pif_key)
latest_date = date_dict(df_list, pif_key)
if latest_date:
logging_df = logging_df.append({'pif_key': pif_key,
'report_date_exists': False,
'report_date_missing': True,
'encounter_dates': None,
'latest_date': latest_date}, ignore_index=True)
else:
logging_df = logging_df.append({'pif_key': pif_key,
'report_date_exists': False,
'report_date_missing': True,
'encounter_dates': None,
'latest_date': ''}, ignore_index=True)
for pif_key in pif_keys_with_report_date:
logging_df = logging_df.append({'pif_key': pif_key,
'report_date_exists': True,
'report_date_missing': False,
'encounter_dates': None,
'latest_date': ''}, ignore_index=True)
return logging_df
def json_report_date_insertion(json_data, df_list):
logging_df = pd.DataFrame(columns=['pif_key', 'report_date_exists', 'report_date_missing', 'encounter_dates', 'latest_date'])
for biomarker_detail in json_data['patient_level']['biomarkers']['details']:
for attribute in biomarker_detail['attribute']:
attribute_details = attribute['attribute_details']
logging_df = report_date_check(attribute_details, df_list, logging_df)
return logging_df
# Usage
# Load dataframes df2022, df2023, df2024
# df2022 = pd.read_csv('df2022.csv')
# df2023 = pd.read_csv('df2023.csv')
# df2024 = pd.read_csv('df2024.csv')
# json_data = {} # Load JSON data
# logging_df = json_report_date_insertion(json_data, [df2022, df2023, df2024])
# print(logging_df)
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