I have a Pandas DataFrame which looks as follows:
ID1 ID2 timestamp x y
0 0 43 1 40
0 0 53 20 41
0 0 63 21 41
0 1 73 5 100
0 1 75 6 99
0 1 83 7 87
1 0 100 34 23
1 0 200 0 0
1 0 210 0 22
1 0 222 22 15
2 0 300 22 15
2 1 450 22 15
2 1 451 22 15
Now, for each group of ID1 and ID2 I would like to calculate the time difference (max - min timestamp) and the mean of x and y. The resulting dataframe should look as follows:
ID1 ID2 timestamp x y
0 0 20 14 40.6
0 1 10 6 95.3
1 0 110 14 15
2 0 300 22 15
2 1 1 22 15
How can this be done? I could use df.groupby(["ID1", "ID2"]) to group but I don't know how to calculate the difference and mean in situ.
Should be something like:
df.groupby(["ID1", "ID2"]).agg({
'timestamp': lambda x: x.max() - x.min(),
'x': 'mean',
'y': 'mean'
}).reset_index()
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