Increase Loop Speed (Pandas dataframe)

Question

Good Afternoon

I have created a function that assign a value depending on the previous row in a dataframe:

#Function to calculate cycles

def new_cycle (dfTickets_CC, cicle, id, prev_id,prev_status):
    global new_cicle
    if cicle is not None:
        new_cicle = cicle
    elif id != prev_id:
        if not dfTickets_CC.loc[dfTickets_CC['Ticket_ID'].isin([id])].empty:
            new_cicle = dfTickets_CC[dfTickets_CC['Ticket_ID'] == id]['Cicle_lastNr'].values[0] + 1
        else:
            new_cicle = 1
    elif id == prev_id:
        if prev_status == "Completed":
            new_cicle  = int(new_cicle)
            new_cicle += 1
        else:
            new_cicle = new_cicle
    return str(new_cicle).split(".")[0]

I call the function and iter the dataframe :

#Step 4, Calculating new cicle

ncicle = []
for i in range(len(dfCompilate.index)):
    if i == 0:
        ncicle.append(new_cycle(dfTickets_CC,dfCompilate['Cicle'].values[i],dfCompilate['Ticket_ID'].values[i],None,None))
    else:
        ncicle.append(new_cycle(dfTickets_CC,dfCompilate['Cicle'].values[i],dfCompilate['Ticket_ID'].values[i],dfCompilate['Ticket_ID'].values[i-1],dfCompilate['Status'].values[i-1]))
dfCompilate['New_cicle'] = ncicle

Problem is that, even though it works correctly, it takes a lot of time... For instance, it takes 2 hours to process a dataframe with 500,000 rows

Does anybody know how to make it faster?

Thanks in advance