Taking average of Dataframe, Object values not printing

Question

I have a Data frame as follows:

Date	Time	U	V
2023-01-03	10:00:21:08	5	8
2023-01-03	10:00:25:08	2	3
2023-01-03	10:00:27:08	3	3
2023-01-03	10:00:29:08	6	6
2023-01-03	10:00:31:08	4	6
2023-01-03	10:00:35:08	9	7
2023-01-03	10:00:37:08	10	4
2023-01-03	10:00:39:08	11	12

I am trying to average 2 values and printing the results for the averages of the Date, Time, U and V.

I have done this:

data_columns=['Date','Time','U','V']
df3= data.groupby(np.arange(len(data))//2).mean()
print(df3)

However, my numeric values average and print, but my Date and Time don't. I know thats because df.groupby function only works on the numeric values and the Date and Time types are objects. How do I get the date and time to print such that the initial time prints for the averaging value. Like so:

Date | Time |U  | V|
2023-01-03 10:00:21:08 3.5 5.5 
2023-01-03 10:00:27:08 4.5 4.5
2023-01-03 10:00:31:08 6.5 6.5
2023-01-03 10:00:37:08 10.5 8

` ** in reality its...np.arrange(len(data)//300).mean()

Answer 1

IIUC, the groupby() function uses with a column name or a list of column name like

groupby('Date') or groupby(['Date','Time'])

UPDATE After discuss with @doctorstrange, I would like to update the answer. The code below try to create a dummy data:

import pandas as pd
from datetime import datetime
import time
import random
logfile='log.csv'
c=0

with open(logfile,'a') as handle:
    handle.write('Date,Time,U,V\n')
    while c<=100:
        now=datetime.now()
        D=now.strftime('%Y-%m-%d')
        T=now.strftime('%H:%M:%S:%f')
        u=random.randint(1, 20)
        v=random.randint(1, 20)
        handle.write(f'{D},{T},{u},{v}\n')
        c=c+1
        time.sleep(1)

The data is the pandas dataframe, like this

	Date	Time	U	V
0	2023-01-13	11:31:43:481765	15	8
1	2023-01-13	11:31:44:485698	13	11
2	2023-01-13	11:31:45:489411	7	11
3	2023-01-13	11:31:46:492592	6	11
4	2023-01-13	11:31:47:496405	18	17
...	...	...	...	...
96	2023-01-13	11:33:19:812832	10	5
97	2023-01-13	11:33:20:812896	5	15
98	2023-01-13	11:33:21:816767	16	9
99	2023-01-13	11:33:22:818553	6	8
100	2023-01-13	11:33:23:818651	13	6

The data was writen into logfile , 'log.csv' file. Read into the dataframe. data variable.

data=pd.read_csv(logfile)

Create a new column timestamp by apply the pd.Timestamp.combine() which combine Date and Time .

data['timestamp']=data.apply(lambda x: pd.Timestamp.combine(
     datetime.strptime(x['Date'],'%Y-%m-%d')
    ,datetime.strptime(x['Time'],'%H:%M:%S:%f').time()
   ), axis=1)

You will get this,

	Date	Time	U	V	timestamp
0	2023-01-13	11:31:43:481765	15	8	2023-01-13 11:31:43.481765
1	2023-01-13	11:31:44:485698	13	11	2023-01-13 11:31:44.485698
2	2023-01-13	11:31:45:489411	7	11	2023-01-13 11:31:45.489411
3	2023-01-13	11:31:46:492592	6	11	2023-01-13 11:31:46.492592
4	2023-01-13	11:31:47:496405	18	17	2023-01-13 11:31:47.496405
...	...	...	...	...	...
96	2023-01-13	11:33:19:812832	10	5	2023-01-13 11:33:19.812832
97	2023-01-13	11:33:20:812896	5	15	2023-01-13 11:33:20.812896
98	2023-01-13	11:33:21:816767	16	9	2023-01-13 11:33:21.816767
99	2023-01-13	11:33:22:818553	6	8	2023-01-13 11:33:22.818553
100	2023-01-13	11:33:23:818651	13	6	2023-01-13 11:33:23.818651

Then, as you use df3 variable, the code below use resample() function with rule='min' (every minute) on the timestamp column and set the offset by 0s (zero second). Then calculate the mean() and then reset the dataframe index, set the name of the column to U_mean

df3=data.resample(rule='min', on='timestamp', offset='0s').U.mean().reset_index(name='U_mean')
df3

The first result like this

	timestamp	U_mean
0	2023-01-13 11:31:00	9.823529
1	2023-01-13 11:32:00	10.783333
2	2023-01-13 11:33:00	8.250000

Lastly, for df4 for V mean

df4=data.resample(rule='min', on='timestamp', offset='0s').V.mean().reset_index(name='V_mean')
df4

Then merge df3 and df4 on timestamp you will get the final result

pd.merge(df3,df4, on='timestamp')

Finally, the result is

	timestamp	U_mean	V_mean
0	2023-01-13 11:31:00	9.823529	11.117647
1	2023-01-13 11:32:00	10.783333	10.716667
2	2023-01-13 11:33:00	8.250000	12.541667

Hope this help.