import pandas as pd

def get_peg(ticker: str):
    
    # Read current ratios .csv
    current_ratios = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_current_ratios.csv', index_col=[0])

    # Convert Object to DateTime
    current_ratios['asOfDate'] = pd.to_datetime(current_ratios['asOfDate'])

    # Sorting per Date
    current_ratios = current_ratios.sort_values('asOfDate', ascending=False)

    # Drop NaN pandas values
    current_ratios = current_ratios.dropna()

    # Take first value (the last peg ratio)
    peg_ratio = current_ratios['PegRatio'][:1]
    
    return peg_ratio.values[0]

def get_financial_health(ticker: str):
    
    # Read balance sheet .csv
    balance_sheet = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_balance_sheet_4Y+4Q.csv', index_col=[0])

    # Convert Object to DateTime
    balance_sheet['asOfDate'] = pd.to_datetime(balance_sheet['asOfDate'])

    # Sorting per Date
    balance_sheet = balance_sheet.sort_values('asOfDate', ascending=False)

    # Drop NaN pandas values
    balance_sheet = balance_sheet.dropna()
    
    # Create financial health column
    balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets'] 

    # Get financial health
    financial_health = balance_sheet['financial_health'][:1]

    return financial_health.values[0]

def estimated_growth(ticker: str):

    # Read 5 years growth estimates
    growth_estimated = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}5YGrowthEstimates.csv', index_col=[0])['5Y Growth estimate'].values[0]

    return growth_estimated

if __name__ == '__main__':
    print(get_peg('MCD'))  # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)
    print(get_financial_health('MCD'))  # < 1 (GREEN); > 1 (RED); = 1 (ORANGE) 
    print(estimated_growth('MCD'))  # < 0 (RED); 0 < x < 8% (ORANGE); < 8 % (GREEN)