I’m relatively new to python. I have a data frame containing rainfall data. I would like to create function with a ‘scenario A’ that will increase the longest dry period (days with rainfall <= 0) by one day and another ‘scenario B’ that will increase the period by two days. The codes below shoes how far I have come. It does the function I want for scenario A, but now I don’t know how to incorporate scenario B into the function, and still get the outputs (specified in code below) that I need for both scenarios. Can anyone help please?

 def increase_dry_period(df):
    # Initialize variables
    season_length = df.shape[0] # Specify season length as length of row entries
    max_counter = 0 # Value of longest dry period
    counter = 0 # Cumulative counter for daily dry days
    start_row_dry_period = 0 # Starting index of longest dry period
    end_row_dry_period = 0 # Ending index of longest dry period
    
    
    # Convert all the rainfall events <= 1 mm to 0 mm
    idx_zero = df['PRECIP'] <= 1
    df.loc[idx_zero,'PRECIP'] = 0
    
    # Iterate through all the rows of percipitation
    for k,row in df.iterrows():
        P = row['PRECIP']

        # Set condition that counts longest dry period (days where rainfall = 0)
        if P == 0:
            # Count days when condition is met
            counter += 1
            # Identify the longest dry period (maximum number of dry days)
            if counter > max_counter:
                max_counter = counter
                
                # Identify the day before and day afer the longest dry period
                if k < season_length-1: 
                    # Store start and end of longest dry period
                    start_row_dry_period = k-counter+1
                    end_row_dry_period = k
                    max_counter_period = (end_row_dry_period - start_row_dry_period)

                    # Change the day after the longest dry period to zero (if still within growing season period) 
                    df.loc[k+1, 'PRECIP'] = 0.0
                
                elif k >= season_length-1: 
                    # Store index of start and end days of longest dry period
                    start_row_dry_period = k-counter+1
                    end_row_dry_period = k

                    # Define number of days of the longest dry period
                    max_counter_period = (end_row_dry_period - start_row_dry_period)
                    
                    # If last day of longest dry period = last day of entire growing season,
                    # change the day before longest dry period to zero 
                    df.loc[k-counter, 'PRECIP'] = 0.0
    
        else:
            # Reset the counter to zero when it encounters a rainy day
            counter = 0
    
    # Save the desired variables into one giant dictionary
    output = {'df': df, 'start': df.loc[start_row_dry_period, 'TIMESTAMP'],
                  'end': df.loc[end_row_dry_period, 'TIMESTAMP'], 
                 'length': max_counter_period}

    # Return the dictionary above as the output of the function
    return output

I tried to add an argument that takes in the both the dataframe containing rainfall data (df) and the list containing values for how many days I want to increase the longest dry period by in scenario A and scenario B. But I am not able to do that:

def increase_dry_period(df, no_of_days): 
    # no_of_days = [1,2]