如何加快这个计算？

Question

i have a data set that looks like this:

|.....userId.................|..cahtroomID....|..msg_index_in_chat..|..time_difference_between_msg..| |1234567891222222|sdfbsjkfdsdklf...|..............1.................|......0 hours 0 minutes....................| |9876543112252141|sdfbsjkfdsdklf...|...............2................|......0 hours 4 minutes....................| |2374623982398939|quweioqewiieio|...............1................|......0 hours 0 minutes....................| |1234567891222222|quweioqewiieio|...............2................|......0 hours 4 minutes....................| |2374623982398939|quweioqewiieio|....................3...........|......1 hours 0 minutes....................|

I need to calculate the average time between messages in every room and assign the value I've gotten to every row. To do so, I wrote this:

 df['avg_time'] = 0
    for room in set(df.roomId):
        table = df[['msg_index_in_chat', 'time_difference_between_msg']][df.roomId == room]
        if len(table) > 1:
            avg_time = []
            times = table.time_difference_between_msg.tolist()
            avg_time = sum(times[1:], timedelta(0))/len(times[1:])
        elif len(table) ==1:
            avg_time = timedelta(hours = 0)
        df.loc[df.roomId == room,('avg_time')] = avg_time

the problem is that this code runs for a lot of time. can you suggest a faster way for doing this calculation?

Thank you!

Answer 1

Use GroupBy.transform with custom lambda function:

f = lambda times: sum(times.iloc[1:], pd.Timedelta(0))/len(times.iloc[1:]) if len(times) > 1 else pd.Timedelta(0)
df['avg_time'] = df.groupby('cahtroomID')['time_difference_between_msg'].transform(f)
print (df)
             userId      cahtroomID  msg_index_in_chat  \
0  1234567891222222  sdfbsjkfdsdklf                  1   
1  9876543112252141  sdfbsjkfdsdklf                  2   
2  2374623982398939  quweioqewiieio                  1   
3  1234567891222222  quweioqewiieio                  2   
4  2374623982398939  quweioqewiieio                  3   

  time_difference_between_msg avg_time  
0                    00:00:00 00:04:00  
1                    00:04:00 00:04:00  
2                    00:00:00 00:32:00  
3                    00:04:00 00:32:00  
4                    01:00:00 00:32:00