迭代 pandas dataframe 并改变值

Question

I'm looking to predict the number of customers in a restaurant at a certain time. My data preprocessing is almost finished - I have acquired the arrival dates of each customer. Those are presented in acchour . weekday means the day of the week, 0 being Monday and 6 Sunday. Now, I'd like to calculate the number of customers at that certain time in the restaurant. I figured I have to loop through the dataframe in reverse and keep adding the arrived customers to the customer count at a certain time, while simultaneously keeping track of when previous customers are leaving. As there is no data on this, we will simply assume every guest stays for an hour.

My sketch looks like this:

exp = [] #Keep track of the expiring customers

for row in reversed(df['customers']): #start from the earliest time
    if row != 1: #skip the 1st row
        res = len(exp) + 1 #amount of customers
        for i in range(len(exp) - 1, -1, -1): #loop exp sensibly while deleting
            if df['acchour'] > exp[i]+1: #if the current time is more than an hour more than the customer's arrival time
                res -= 1
                del exp[i]
        exp.append(df['acchour'])
        row = res

However, I can see that df['acchour'] is not a sensible expression and was wondering how to reference the different column on the same row properly. Altogether, if someone can come up with a more convenient way to solve the problem, I'd hugely appreciate it!

Answer 1

So you can get the total customers visiting at a specific time like so:

df.groupby(['weekday','time', 'hour'], as_index=False).sum()

Then maybe you can calculate the difference between each time window you want?