#need to use pip to get requests, pandas, and
import requests  # Import the requests library to make HTTP requests
import pandas as pd  # Import the pandas library for data manipulation and analysis

def get_adverse_events(drug_name, skip=0):
    # Define the URL for the FDA API request
    url = f"https://api.fda.gov/drug/event.json?search=patient.drug.medicinalproduct:{drug_name}+AND+receivedate:[20220101+TO+20220105]&limit=100&skip={skip}"
    response = requests.get(url)  # Make a GET request to the FDA API
    if response.status_code == 200:  # Check if the request was successful (status code 200)
        return response.json()  # Return the response data as a JSON object
    else:
        return None  # Return None if the request was not successful

statins = ["atorvastatin", "rosuvastatin", "simvastatin", "pravastatin", "lovastatin"]
# List of statin drugs to search for adverse events

all_data = []  # Initialize an empty list to store all adverse event data

for statin in statins:  # Loop through each statin in the list
    skip = 0  # Initialize the skip parameter to 0 for pagination
    while True:  # Infinite loop to keep fetching data until no more results
        data = get_adverse_events(statin, skip)  # Get adverse events data for the current statin
        if not data or 'results' not in data:  # Check if there is no data or no results
            break  # Exit the loop if no more data
        for event in data['results']:  # Loop through each event in the results
            adverse_event = {
                'Drug': statin.capitalize(),  # Capitalize the drug name
                'Reaction': event['patient']['reaction'][0]['reactionmeddrapt'] if 'reaction' in event['patient'] else None,
                # Get the reaction type if available
                'Outcome': event['patient']['reaction'][0].get('reactionoutcome', None),
                # Get the reaction outcome if available
                'Date': event['receivedate'],  # Include the receivedate
                'Age': event['patient'].get('patientonsetage', None),
                # Get the patient's age if available
                'Age Unit': event['patient'].get('patientonsetageunit', None),
                # Get the age unit (e.g., years, months) if available
                'Gender': event['patient'].get('patientsex', None),  # Get the patient's gender if available
            }
            all_data.append(adverse_event)  # Add the adverse event data to the list
        skip += 100  # Increment the skip parameter to get the next set of results (we already got these, so it's skipping what we got and is going to the next batch)

df = pd.DataFrame(all_data)  # Create a DataFrame from the list of adverse event data

# Save the DataFrame to an Excel file
df.to_excel('adverse_events_statins.xlsx', index=False)