分組熊貓據幀 - 2.0

Question

而且這個問題Grouping Pandas dataframe across rows，運是：

  amount 
clients   
Comp1 16.360417 
Comp2 69.697501 
Comp3 85.700000 
Comp4 36.666667 
Comp5 44.156500 

如果日期列被添加到輸入：

tdate,client1,client2,client3,client4,client5,client6,amount 
12/31/2017,,,Comp1,,,4.475000 
12/31/2017,,,Comp2,,,16.305584 
10/31/2107,,,Comp3,,,4.050000 
10/31/2017,Comp2,Comp1,,Comp4,,,21.000000 
1/1/2017,,,Comp4,,,30.000000 
2/2/2017,Comp1,,Comp2,,,5.137500 
10/31/2017,,,Comp3,,,52.650000 
12/31/2017,,,Comp1,,,2.650000 
10/31/2017,Comp3,,,Comp3,,,29.000000 
12/31/2017,Comp5,,,Comp2,,,20.809000 
1/1/2017,Comp5,,,Comp2,,,15.100000 
10/31/2017,Comp5,,,Comp2,,,52.404000 

我們將如何得到這個輸出：

12/31/2017 Comp1 4.475+2.65 
12/31/2017 Comp2 16.305584+20.809/2 
10/31/2017 Comp2 21/3+5.1375/2+52.404/2 
1/1/2017 Comp2 15.1/2 
10/31/2017 Comp3 4.05+52.65+29 
1/1/2017 Comp4 30 
10/21/2017 Comp4 21/3 
12/31/2017 Comp5 20.809/2 
1/1/2017 Comp5 15.1/2 
10/31/2017 Comp5 52.404/2 

Answer 1

從前面的回答改進，我們需要使用堆棧通過設置兩列作爲inde X。

cols= ['amount','tdate'] 
df['new'] = df['amount']/df.drop(cols,1).count(1) 

#Set the index as new and tdate by droping amount column, stack and drop the nans. 
x = df.drop(['amount'],1).set_index(['new','tdate']).stack().dropna() 

#Create dataframe from amount,tdate and the clients 
ndf = pd.DataFrame({'amount':x.index.get_level_values('new'),'tdate':x.index.get_level_values('tdate'),'clients':x.values}) 

#Groupby `clients` and `tdate` 
ndf.groupby(['clients','tdate']).sum().reset_index() 

輸出：

 
    clients  tdate  amount 
0 Comp1 10/31/2017 7.000000 
1 Comp1 12/31/2017 7.125000 
2 Comp1 2/2/2017 2.568750 
3 Comp2 1/1/2017 7.550000 
4 Comp2 10/31/2017 33.202000 
5 Comp2 12/31/2017 26.710084 
6 Comp2 2/2/2017 2.568750 
7 Comp3 10/31/2017 81.650000 
8 Comp3 10/31/2107 4.050000 
9 Comp4 1/1/2017 30.000000 
10 Comp4 10/31/2017 7.000000 
11 Comp5 1/1/2017 7.550000 
12 Comp5 10/31/2017 26.202000 
13 Comp5 12/31/2017 10.404500