Populate new column based on previous values in other column

Question

I have a dataseta with users, visit_types (bookings or searches) and hotels. I need to populate a new column with the most booked hotel, based on previous booked hotel for that row.

For example,

   **user**   **visit_type**   **hotel_code**   **most_booked**
1    user1       search             1                NaN
2    user1       search             2                NaN
3    user1       booking            1                NaN
4    user1       search             8                NaN
5    user1       booking            8                1
6    user2       search             6                NaN
7    user2       booking            6                NaN
8    user2       search             4                NaN
9    user2       booking            4                6
10   user2       booking            6                4
11   user2       booking            4                6

So with this example:

The most booked hotel for user1 would be, in row 3 hotel = NaN, beacuse it has no hotel booked previously, and in row 5 it would be hotel = 1.

For user2, row 7 would be hotel = NaN, row 9 would be hotel = 6, and row 10 hotel = 4 (as it is the last booked and there are only two hotels booked) and for the last row 11, the hotel would be 6 as it is the most booked up to that point (without taking into account row 11).

End genocide - save Gaza · Accepted Answer · 2018-06-11 12:16:24Z

This should achieve what you want:

import pandas as pd
import operator
from collections import defaultdict

d = {      "user":["user1","user1","user1","user1","user1","user2","user2","user2","user2","user2","user2"],
     "visit_type":["search","search","booking","search","booking","search","booking","search","booking","booking","booking"],
     "hotel_code":[1,2,1,8,8,6,6,4,4,6,4]}

df = pd.DataFrame(data=d)
#Setting default value
df['most_booked']='NaN'

for user in df.user.unique():
    #Ignoring searches, only considering bookings
    df_bookings = df.loc[(df["visit_type"] == "booking") & (df['user'] == user)]
    last_booked = ""
    booking_counts = defaultdict(int)

    for i, entry in df_bookings.iterrows():
        #Skipping first booking
        if last_booked != "":
            highest = max(booking_counts.values())
            #Prefers last booked if it equals max
            if booking_counts[last_booked] == highest:
                max_booked = last_booked
            #Otherwise chooses max
            else:
                max_booked = max(booking_counts.items(), key=operator.itemgetter(1))[0]
            df.loc[i, 'most_booked'] = max_booked

        #Update number of bookings in dictionary
        current_booking = entry["hotel_code"]
        booking_counts[current_booking] += 1
        last_booked = current_booking

print(df)

    hotel_code   user visit_type most_booked
0            1  user1     search         NaN
1            2  user1     search         NaN
2            1  user1    booking         NaN
3            8  user1     search         NaN
4            8  user1    booking           1
5            6  user2     search         NaN
6            6  user2    booking         NaN
7            4  user2     search         NaN
8            4  user2    booking           6
9            6  user2    booking           4
10           4  user2    booking           6

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