Python 基于其他列中以前的值填充新列

我有一个关于用户、访问类型（预订或搜索）和酒店的数据集。我需要根据该行先前预订的酒店，用预订最多的酒店填充一个新列

比如说,

   **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

在这个例子中：

第3行hotel=NaN中，用户1预订最多的酒店是，因为之前没有预订过酒店，而第5行中的酒店是hotel=1

---

对于用户2，第7行是hotel=NaN，第9行是hotel=6，第10行hotel=4（因为这是最后一次预订，只有两个酒店预订），而对于最后一行11，酒店将是第6行，因为这是到目前为止预订最多的酒店（不考虑第11行）。

这将实现您想要的：

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