Pandas sum over multiple columns after group by - pandas

Question

if I have a data set where the columns are something like:
Day Column2 Column3 Column4......Column100
Is there a better way to do something like the below?
grouped_df = df.groupby('Day').agg({
'Column2': lambda x : sum(x),
'Column3': lambda x : sum(x),
'Column4': lambda x : sum(x),
..........
'Column100': lambda x : sum(x)})
What I have works but wondering if there is a more elegant solution.
Thank You

Answer 1

You can try df.groupby('Day').sum() just like what MaxU said.

Answer 2

you can do it this way:
In [17]: df
Out[17]:
a b c d e Day
0 7 5 4 9 4 2016-01-01
1 2 1 5 4 5 2014-01-01
2 2 8 8 6 9 2014-01-01
3 1 4 4 3 7 2015-01-01
4 5 6 7 9 5 2016-01-01
5 3 6 0 8 7 2015-01-01
6 7 4 4 5 5 2014-01-01
7 1 1 0 1 6 2015-01-01
8 7 8 9 8 3 2015-01-01
9 8 5 5 2 8 2015-01-01
10 6 1 3 0 3 2014-01-01
11 1 8 2 7 2 2016-01-01
12 2 5 2 5 1 2016-01-01
13 1 2 3 2 2 2016-01-01
14 7 4 9 5 2 2016-01-01
15 4 0 8 9 5 2015-01-01
16 8 5 8 9 7 2015-01-01
17 6 7 9 5 4 2016-01-01
18 7 4 2 3 2 2016-01-01
19 2 7 8 6 8 2015-01-01
In [18]: cols = df.columns
In [19]: cols[1:]
Out[19]: Index(['b', 'c', 'd', 'e', 'Day'], dtype='object')
In [20]: df.ix[:, cols[1:]].groupby('Day').sum()
Out[20]:
b c d e
Day
2014-01-01 14 20 15 22
2015-01-01 36 42 46 51
2016-01-01 41 38 45 22
setup sample DF:
rows = 20
df = pd.DataFrame(np.random.randint(0, 10, size=(rows, 5)), columns=list('abcde'))
dates = [pd.to_datetime(d) for d in ['2016-01-01','2015-01-01','2014-01-01']]
df['Day'] = np.random.choice(dates, len(df))