File size: 1,718 Bytes
a71b937 fba9ff3 a71b937 fba9ff3 a71b937 fba9ff3 a71b937 fba9ff3 f0a794c | 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 | def date_dict(df, pif_key):
date = df.loc[df['pif_key'].astype(str) == str(pif_key), 'encounter_date'].values[0]
date_insert_dict = {
'attribute_name': 'report_date',
'attribute_method': 'cv',
'attribute_normalized_prediction': '',
'attribute_prediction': str(date),
'attribute_version': 'v2_090523',
'attribute_vocab': '',
'attribute_code': '',
'date_of_service': ''
}
return date_insert_dict
def report_date_check(dict_list, 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 = date_dict(df, pif_key)
dict_list.append(date_insert_dict)
return dict_list
def json_report_date_insertion(json_data, df):
for biomarker_detail in json_data['patient_level']['biomarkers']['details']:
for attribute in biomarker_detail['attribute']:
attribute_details = attribute['attribute_details']
attribute['attribute_details'] = report_date_check(attribute_details, df)
return json_data
def dicts_to_dataframe(list_of_dicts):
# Initialize an empty DataFrame
df = pd.DataFrame()
# Iterate through each dictionary in the list
for d in list_of_dicts:
# Convert each dictionary into a DataFrame
temp_df = pd.DataFrame([d])
# Concatenate the temporary DataFrame with the main DataFrame
df = pd.concat([df, temp_df], ignore_index=True)
# Fill missing values with empty strings
df = df.fillna('')
return df |