Python 从带有sum函数的groupby数据框中排除日期列
我有一个df，我将它摸索到另一个数据帧中，使用求和函数，它排除了一些列，但是date对于进一步的计算是至关重要的Python 从带有sum函数的groupby数据框中排除日期列,python,pandas,datetime,merge,group-by,Python,Pandas,Datetime,Merge,Group By,我有一个df，我将它摸索到另一个数据帧中，使用求和函数，它排除了一些列，但是date对于进一步的计算是至关重要的 grdf = df.groupby(['Year', 'Month', 'Percentage']).sum() grdf['Gross Sales'] = grdf['Gross Sales'].astype(float) grdf['Sum'] = grdf['Gross Sales'].cumsum() 追加、合并、重新索引、掩码、合并、concat、交集的最佳方式， 你能说
grdf = df.groupby(['Year', 'Month', 'Percentage']).sum()
grdf['Gross Sales'] = grdf['Gross Sales'].astype(float)
grdf['Sum'] = grdf['Gross Sales'].cumsum()
- Net Units Net Sales Gross Sales Sum Payout
Year Month Percentage
2017 11 70% 3 147.97 103.58 103.58 103.58
12 70% 1 24.99 17.49 121.07 17.49
2018 1 70% 2 49.98 34.99 156.06 34.99
2 70% 3 74.97 52.48 208.54 104.96
3 70% 1 24.99 17.49 226.03 17.49
4 70% 1 24.99 17.49 243.52 17.49
8 88% 2 89.98 79.18 322.71 114.17
9 88% 1 64.99 57.19 379.90 57.19
10 88% 3 104.97 92.37 472.27 149.56
11 88% 2 79.98 70.38 542.65 70.38
2019 1 88% 2 39.98 35.18 577.83 105.56
因此，您可以决定对每列使用哪种操作。您只需说出“日期”列的内容（首先可能还可以）。原始数据帧是什么样子的？能否添加原始数据帧的示例？@Ghorich-Updated@先生-更新@jezrael-不幸的是不是，我按月分组（所以我的数据是汇总的），行数-与
'last'
Day Product Base Price Net Units Net Sales Gross Sales Percentage Year Month Payout Pay Day
0 2017-11-11 asdasdasdnts $69.99 1 69.99 48.9930 70% 2017 11 x x
1 2017-11-13 asdasdasdnts $69.99 1 69.99 48.9930 70% 2017 11 x x
2 2017-11-27 asdasdasdnts $7.99 1 7.99 5.5930 70% 2017 11 103.579 2018-01-11
3 2017-12-06 asdasdasdnts $24.99 1 24.99 17.4930 70% 2017 12 x x
4 2018-01-03 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 1 x x
5 2018-01-17 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 1 x x
6 2018-02-10 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 2 x x
7 2018-02-19 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 2 x x
8 2018-02-28 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 2 104.958 2018-04-14
9 2018-03-04 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 3 x x
10 2018-04-22 asdasdasdnts $24.99 1 24.99 17.4930 70% 2018 4 x x
11 2018-08-01 asdasdasdnts $24.99 1 24.99 21.9912 88% 2018 8 x x
12 2018-08-22 asdasdasdial $64.99 1 64.99 57.1912 88% 2018 8 176.789 2018-10-06
13 2018-09-19 asdasdasdial $64.99 1 64.99 57.1912 88% 2018 9 x x
14 2018-10-15 asdasdasdial $64.99 1 64.99 57.1912 88% 2018 10 114.382 2018-11-29
15 2018-10-23 asdasdasdnts $24.99 1 24.99 21.9912 88% 2018 10 x x
16 2018-10-26 asdasdasdock $14.99 1 14.99 13.1912 88% 2018 10 x x
17 2018-11-20 asdasdasdial $64.99 1 64.99 57.1912 88% 2018 11 x x
18 2018-11-20 asdasdasdock $14.99 1 14.99 13.1912 88% 2018 11 105.565 2019-01-04
19 2019-01-04 asdasdasdnts $24.99 1 24.99 21.9912 88% 2019 1 x x
20 2019-01-04 asdasdasdock $14.99 1 14.99 13.1912 88% 2019 1 x x
In : df = pd.DataFrame([[1, 2, 3],
[4, 5, 6],
[1, 5, 7]],
columns=['A', 'B', 'C'])
In : df
Out:
A B C
0 1 2 3
1 4 5 6
2 1 5 7
In : df.groupby('A').agg({'B':np.sum, 'C':'first'})
Out:
B C
A
1 7 3
4 5 6