Select the last value in time after multiple groupings - pandas

Question

I want to group ‘name’ first, then press ‘day’ to aggregate and select the last value of each ‘name’ every day.
I got some ideas from here：pandas - how to organised dataframe based on date and assign new values to column
I tried this, but I can't succeed. Is there any good way?
df = df.groupby(df['name']).resample('D',on='Timestamp').apply(['last'])
eg:
import pandas as pd
N = 9
rng = pd.date_range('2011-01-01', periods=N, freq='15S')
df = pd.DataFrame({'Timestamp': rng, 'name': ['A','A', 'B','B','B','B','C','C','C'],
'value': [1, 2, 3, 2, 3, 1, 3, 4, 3],'Temp': range(N)})
[out]:
Timestamp name value Temp
0 2011-01-01 00:00:00 A 1 0
1 2011-01-01 00:00:15 A 2 1
2 2011-01-01 00:00:30 B 3 2
3 2011-01-01 00:00:45 B 2 3
4 2011-01-01 00:01:00 B 3 4
5 2011-01-01 00:01:15 B 1 5
6 2011-01-01 00:01:30 C 3 6
7 2011-01-01 00:01:45 C 4 7
8 2011-01-01 00:02:00 C 3 8
I want to get these:
[out]:
Timestamp name value Temp
1 2011-01-01 00:00:15 A 2 1
5 2011-01-01 00:01:15 B 1 5
8 2011-01-01 00:02:00 C 3 8

Answer 1

IIUC
df.groupby('name').tail(1)
Out[25]:
Temp Timestamp name value
1 1 2011-01-01 00:00:15 A 2
5 5 2011-01-01 00:01:15 B 1
8 8 2011-01-01 00:02:00 C 3
Or
df.drop_duplicates('name',keep='last')
Out[26]:
Temp Timestamp name value
1 1 2011-01-01 00:00:15 A 2
5 5 2011-01-01 00:01:15 B 1
8 8 2011-01-01 00:02:00 C 3

Answer 2

If need last values per days and per column name, use GroupBy.tail with Grouper:
df1 = df.groupby([pd.Grouper(freq='D', key='Timestamp'), 'name']).tail(1)
print (df1)
Timestamp name value Temp
1 2011-01-01 00:00:15 A 2 1
5 2011-01-01 00:01:15 B 1 5
8 2011-01-01 00:02:00 C 3 8
Or convert values of Timestamp to dates by Series.dt.date:
df2 = df.groupby([df['Timestamp'].dt.date, 'name']).tail(1)
print (df2)
Timestamp name value Temp
1 2011-01-01 00:00:15 A 2 1
5 2011-01-01 00:01:15 B 1 5
8 2011-01-01 00:02:00 C 3 8
There are also alternatives with Series.dt.normalize:
df2 = df.groupby([df['Timestamp'].dt.normalize(), 'name']).tail(1)
Or Series.dt.floor:
df2 = df.groupby([df['Timestamp'].dt.floor('D'), 'name']).tail(1)