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import pandas as pd
def getSymptomsCausesAndDiseaseNameFromJSON(data: pd.DataFrame) -> dict:
"""
Generate Symptoms, Causes and Disease Name
=========================
Parameters:
-----------
data:
description: Augmented Data
type: pd.DataFrame
-----------
Returns:
--------
data:
description: Symptoms, Causes and Disease Name
type: dict
--------------------------------------------------------------------------------------------
Working:
--------
- Create a DataFrame from data
- Create a new DataFrame with columns: disease, symptoms and causes
- For each row in data:
- Add all the column names that are 1 in array to the 'symptoms' key if the prefix is symptom_ otherwise to 'causes' key and set the value of 'disease' key to disease name
- Return the new dict
"""
numberOfSymptoms = len([col for col in data.columns if col.startswith('symptoms_')])
diseases = data['disease']
symptoms = data.columns[1:numberOfSymptoms]
causes = data.columns[numberOfSymptoms:]
# Get symptoms column names where symptoms = 1
symptomsArray = data[symptoms].apply(lambda x: symptoms[x.values.astype(bool)].tolist(), axis=1)
# Get causes column names where causes = 1
causesArray = data[causes].apply(lambda x: causes[x.values.astype(bool)].tolist(), axis=1)
# Remove the prefix 'symptoms_' and 'causes_' from the symptomsArray and causesArray
symptomsArray = [list(map(lambda x: x.replace('symptoms_', ''), symptom)) for symptom in symptomsArray]
causesArray = [list(map(lambda x: x.replace('causes_', ''), cause)) for cause in causesArray]
diseaseDict = {
"diseases": [
{
"disease": disease,
"symptoms": symptom,
"causes": cause
}
for disease, symptom, cause in zip(diseases, symptomsArray, causesArray)
]
}
return diseaseDict |